Jan Akmal - Profile on Academia.edu (original) (raw)
Papers by Jan Akmal
Materials Science in Additive Manufacturing, 2025
Metal additive manufacturing (AM) has attracted significant interest in high-value industries due... more Metal additive manufacturing (AM) has attracted significant interest in high-value industries due to its ability to produce complex parts flexibly, but the reliance on costly manual monitoring remains a major burden for quality control. Artificial intelligence (AI)-driven models for automated defect detection are emerging as promising solutions. This study contributes a new annotated dataset for AI research in AM and evaluates the performance of four widely used convolutional neural network (CNN) models in detecting powder bed morphology defects, based on layer-wise images acquired by the EOSTATE PowderBed system during the metal laser-based powder bed fusion process. The models were trained through transfer learning methods with manually labeled and pre-processed data. Results demonstrated that ResNet50 and EfficientNetV2B0 achieved over 99% accuracy in defect classification, while YOLOv5 outperformed Faster region-based-CNN in defect detection and localization. However, lower average precision values in object detection tasks were attributed to variability in defect scales and annotation quality. This study confirms the potential of AI-based models for defect identification in AM, with YOLOv5 demonstrating clear advantages in managing complex, multi-scale defects. Future improvements will focus on expanding the dataset and refining annotation strategies to further enhance model robustness.
Virtual and Physical Prototyping , 2025
This pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6... more This pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6Al-4V fabricated using laser-based powder bed fusion. A tailor-made test specimen was designed and manufactured for the nanofocus tube X-ray computed tomography (XCT) system. In situ optical tomography was used to capture infrared images containing heat signatures of the hot laser interaction zone. Depicting natural process variation, defective regions were seeded using process manipulation (up to ±30%) in proximity of the experimental standard volumetric energy density (VED). The concomitant defects and heat signatures were both spatially and temporally captured. The results indicate that porosity significantly grows from an average value of 27 parts per million (PPM) to a value of 337 PPM comprising defect sizes of <112 µm when the VED increases by 30%. The outcome confirmed that Ti-6Al-4V can self-heal these defective regions by up to 7 ± 1 layers using the standard VED. A convolutional neural network was trained (n = 211) and was verified with XCT. The model demonstrated prediction accuracy of 94% for the six classes of unfamiliar defective regions. This work enables in-situ detection and healing of defective regions caused by process uncertainty that can shift the quality frontier of novel product design and development.
Materials & Design, 2025
This paper introduces a novel 4D printing approach for dynamic displays, using thermochromic-PLA ... more This paper introduces a novel 4D printing approach for dynamic displays, using thermochromic-PLA composites embedded with FeCrAl wires activated by Joule heating. Previous studies struggled with high energy consumption, heat bleed, and slow activation. By embedding continuous resistance wires directly within the thermochromic-plastic matrix, localized, efficient heating is achieved, improving response speed and energy efficiency. The study optimizes color change by focusing on specimen thickness, current intensity, and pulse duration. A customized G-code workflow allowed for precise wire integration into 3D-printed specimens fabricated using a composite dual-nozzle co-extrusion printer, enabling controlled color changes upon activation. Experiments identified 1.5 mm as the ideal specimen thickness, balancing rapid response time (~78.5 s for 80 % color change) with effective wire concealment. A water medium significantly accelerated recovery time compared to air, emphasizing the benefits of faster heat dissipation. The color change was found to be linearly proportional to pulse-on time and the square of the current. Shorter pulses at higher currents resulted in more efficient and predictable color transformations than constant current inputs. A functional 7-segment display was fabricated, demonstrating the practical application of this technology for smart displays, sensors, and other 4D-printing applications.
Materials, Mar 22, 2024
In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser... more In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development.
Additive manufacturing of self-sensing parts through material extrusion
Virtual and physical prototyping, Feb 28, 2024
The objective of this study is to develop and evaluate self-sensing capabilities in additively ma... more The objective of this study is to develop and evaluate self-sensing capabilities in additively manufactured parts by embedding conductive elements that are copper and continuous carbon fiber. Two sets of test specimen were manufactured using a custom g-code on material extrusion-based Anisoprint A4 machine. Each set contained copper and continuous carbon fiber in an amorphous thermoplastic matrix. A tailor-made test setup was developed by improvising the American Society for Testing and Materials (ASTM D790) three-point loading system. Electrical resistance measurements were conducted under flexural loads to evaluate the selfsensing capability of each test specimen. The results confirmed that material extrusion technology can allow production of self-sensing parts. The electrical resistance increases linearly (Sensing tolerance <±2.6%, R 2 >93.8% p-value < 0.005), establishing a strong correlation with applied force and strain. The work allows for creating smart parts that can facilitate big data collection, analysis, and evidence-based decision-making for condition monitoring and preventive maintenance needed for Industry 4.0.
Defect detection in laser-based powder bed fusion process using machine learning classification methods
IOP Conference Series: Materials Science and Engineering, Nov 30, 2023
The aim of this study is to deploy machine learning (ML) classification methods to detect defecti... more The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design-and shape-agnostic.
Progress in Additive Manufacturing, Dec 18, 2023
Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and ... more Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and wide-ranging applications. Understanding the bending shape recovery characteristics of 3D printed SMPCs is crucial for optimizing their performance. This study focuses on investigating the influence of different fiber orientations of continuous carbon fiber (CCF) in thermally stimulated SMPC. By controlling printing parameters and fiber orientation during the 3D printing process, we fabricate tailor-made rectangular composite test specimens. These specimens are subjected to controlled bending above the glass transition temperature of the polymer, inducing temporary deformation. The subsequent shape recovery process is carefully captured through high-speed video. Precise measurements of the bending curvature over time are obtained using the row-by-row image processing technique and analyzed. The shape recovery rate, shape recovery ratio, and shape fixity ratio of the test specimens were evaluated as a function of three CCF layout arrangements as well as fiber infill density embedded in Shape Memory Polymer (SMP) test specimens. The results revealed that the addition of CCF in the polymer matrix has a significant impact on shape memory behavior. Vertically aligned CCF in the SMP matrix improves the shape recovery ratio (92.97% compared to 78.77% of the pure SMP sample), while horizontal alignment of CCF ensures maximum shape fixity ratio (91.78% compared to 66.22% of the pure SMP sample). The cross-aligned CCF sample provides good recovery as well as fixity values. Further, it was observed that the horizontal alignment of CCF yields the fastest recovery performance. The outcome confirms that optimizing the fiber orientation enhances shape memory performance. Also, 40% of fiber infill density had greater shape fixity and overall recovery performance when compared to 30% and 50%. These findings have implications for tailored and high-performance SMPCs in biomedical devices, aerospace components, and robotics. Understanding temporal curvature behavior enables optimizing the design of materials with precise control over shape recovery. This research contributes to the design and optimization of SMPCs for diverse applications. Shape memory polymer composites • 4D printing • Continuous carbon fiber-reinforced SMP • Fiber orientation and layout • Bending recovery performance • Material extrusion (MEX) • Fused filament fabrication (FFF) • Fused deposition modeling (FDM) * S. Siddharth Kumar
Virtual and Physical Prototyping, 2024
The objective of this study is to develop and evaluate self-sensing capabilities in additively ma... more The objective of this study is to develop and evaluate self-sensing capabilities in additively manufactured parts by embedding conductive elements that are copper and continuous carbon fiber. Two sets of test specimen were manufactured using a custom g-code on material extrusion-based Anisoprint A4 machine. Each set contained copper and continuous carbon fiber in an amorphous thermoplastic matrix. A tailor-made test setup was developed by improvising the American Society for Testing and Materials (ASTM D790) three-point loading system. Electrical resistance measurements were conducted under flexural loads to evaluate the selfsensing capability of each test specimen. The results confirmed that material extrusion technology can allow production of self-sensing parts. The electrical resistance increases linearly (Sensing tolerance <±2.6%, R^2 >93.8% p-value < 0.005), establishing a strong correlation with applied force and strain. The work allows for creating smart parts that can facilitate big data collection, analysis, and evidence-based decision-making for condition monitoring and preventive maintenance needed for Industry 4.0.
IOP Conference Series: Materials Science and Engineering
Jatropha Curcas oil is a non-edible oil which is used for Jatropha biodiesel (JBD) production. Ja... more Jatropha Curcas oil is a non-edible oil which is used for Jatropha biodiesel (JBD) production. Jatropha biodiesel is produced using transesterification technique and it is used as an alternative fuel in CI diesel engine without any hardware modification. Jatropha biodiesel is used in CI diesel engine with various volumetric concentrations (blends) such as JBD5, JBD15, JBD25, JBD35 and JBD45. The combustion parameters such as in-cylinder pressure, rate of pressure rise, net heat release, cumulative heat release, mass fraction burned are analyzed and compared for all blends combustion data with mineral diesel fuel (D100).
IOP Conference Series: Materials Science and Engineering, 2023
The aim of this study is to deploy machine learning (ML) classification methods to detect defecti... more The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design-and shape-agnostic.
Progress in Additive Manufacturing, 2023
Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and ... more Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and wide-ranging applications. Understanding the bending shape recovery characteristics of 3D printed SMPCs is crucial for optimizing their performance. This study focuses on investigating the influence of different fiber orientations of continuous carbon fiber (CCF) in thermally stimulated SMPC. By controlling printing parameters and fiber orientation during the 3D printing process, we fabricate tailor-made rectangular composite test specimens. These specimens are subjected to controlled bending above the glass transition temperature of the polymer, inducing temporary deformation. The subsequent shape recovery process is carefully captured through high-speed video. Precise measurements of the bending curvature over time are obtained using the row-by-row image processing technique and analyzed. The shape recovery rate, shape recovery ratio, and shape fixity ratio of the test specimens were evaluated as a function of three CCF layout arrangements as well as fiber infill density embedded in Shape Memory Polymer (SMP) test specimens. The results revealed that the addition of CCF in the polymer matrix has a significant impact on shape memory behavior. Vertically aligned CCF in the SMP matrix improves the shape recovery ratio (92.97% compared to 78.77% of the pure SMP sample), while horizontal alignment of CCF ensures maximum shape fixity ratio (91.78% compared to 66.22% of the pure SMP sample). The cross-aligned CCF sample provides good recovery as well as fixity values. Further, it was observed that the horizontal alignment of CCF yields the fastest recovery performance. The outcome confirms that optimizing the fiber orientation enhances shape memory performance. Also, 40% of fiber infill density had greater shape fixity and overall recovery performance when compared to 30% and 50%. These findings have implications for tailored and high-performance SMPCs in biomedical devices, aerospace components, and robotics. Understanding temporal curvature behavior enables optimizing the design of materials with precise control over shape recovery. This research contributes to the design and optimization of SMPCs for diverse applications.
The International Journal of Advanced Manufacturing Technology, 2023
A finite element-based thermomechanical modeling approach is developed in this study to provide a... more A finite element-based thermomechanical modeling approach is developed in this study to provide a prediction of the mesoscale melt pool behavior and part-scale properties for AlSi10Mg alloy. On the mesoscale, the widely adopted Goldak heat source model is used to predict melt pool formed by laser during powder bed fusion process. This requires the determination of certain parameters as they control temperature distribution and, hence, melt pool boundaries. A systematic parametric approach is proposed to determine parameters, i.e., absorption coefficient and transient temperature evolution. The simulation results are compared in terms of morphology of melt pool with the literature results. Considering the part-scale domain, there is increasing demand for predicting geometric distortions and analyzing underlying residual stresses, which are highly influenced by the mesh size and initial temperature setup. This study aims to propose a strategy for evaluating the correlation between the mesh size and the initial temperature to provide correct residual stresses when increasing the scale of the model for efficiency. The outcomes revealed that the predicted melt pool error produced by optimal Goldak function parameters is between 5 and 12%. On the part-scale, the finite element model is less sensitive to mesh size for distortion prediction, and layer-lumping can be used to increase the speed of simulation. The effect of large time increments and layer lumping can be compensated by appropriate initial temperature value for AlSi10Mg. The study aids practitioners and researchers to establish and validate design for additive manufacturing within the scope of desired part quality metrics.
Annals of Work Exposures and Health
Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were... more Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were studied in three different work environments. Workplaces utilized powder bed fusion, material extrusion, and binder jetting techniques with metal and polymer powders, polymer filaments, and gypsum powder, respectively. The AM processes were studied from operator’s point of view to identify exposure events and possible safety risks. Total number of particle concentrations were measured in the range of 10 nm to 300 nm from operator’s breathing zone using portable devices and in the range of 2.5 nm to 10 µm from close vicinity of the AM machines using stationary measurement devices. Gas-phase compounds were measured with photoionization, electrochemical sensors, and an active air sampling method which were eventually followed by laboratory analyses. The duration of the measurements varied from 3 to 5 days during which the manufacturing processes were practically continuous. We identified s...
Annals of Work Exposures and Health, 2023
Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were... more Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were studied in three different work environments. Workplaces utilized powder bed fusion, material extrusion, and binder jetting techniques with metal and polymer powders, polymer filaments, and gypsum powder, respectively. The AM processes were studied from operator's point of view to identify exposure events and possible safety risks. Total number of particle concentrations were measured in the range of 10 nm to 300 nm from operator's breathing zone using portable devices and in the range of 2.5 nm to 10 µm from close vicinity of the AM machines using stationary measurement devices. Gas-phase compounds were measured with photoionization, electrochemical sensors, and an active air sampling method which were eventually followed by laboratory analyses. The duration of the measurements varied from 3 to 5 days during which the manufacturing processes were practically continuous. We identified several work phases in which an operator can potentially be exposed by inhalation (pulmonary exposure) to airborne emissions. A skin exposure was also identified as a potential risk factor based on the observations made on work tasks related to the AM process. The results confirmed that nanosized particles were present in the breathing air of the workspace when the ventilation of the AM machine was inadequate. Metal powders were not measured from the workstation air thanks to the closed system and suitable risk control procedures. Still, handling of metal powders and AM materials that can act as skin irritants such as epoxy resins were found to pose a potential risk for workers. This emphasizes the importance of appropriate control measures for ventilation and material handling that should be addressed in AM operations and environment.
Aalto University Doctoral Thesis, 2022
Additive manufacturing (AM) is rapidly developing into a general-purpose technology akin to elect... more Additive manufacturing (AM) is rapidly developing into a general-purpose technology akin to electric drives and computers serving a plethora of applications. The advent and proliferation of the additive process triggering Industry 4.0 is encouraging academics and practitioners to establish new practices, designs, and modes of creating and supplying end-use parts.
Contributing to this emerging stream of research on AM technologies, the overarching objective of this doctoral dissertation is to discover situations and ways in which companies can benefit from implementing AM in conjunction with conventional manufacturing technologies. This is addressed and limited by three sub-objectives.
First sub-objective establishes a new operational practice—dynamic supplier selection using the build-to-model mode of manufacturing—for the provision of idiosyncratic spare parts to improve the after-sales operations of a case company. Second sub-objective estimates the combined uncertainty and the worst-case error in creating an end-use part, particularly a personalized implant made by radiologic images, thresholding, digital design, and AM. Third sub-objective develops process interruption-based embedding and creates prototypes of smart parts, in particular intelligent implants using four AM technologies. The work uses a multi-methods approach combining three case studies, experiments, and research methodologies to achieve the aim of theoretical insights, practical relevance, and innovation.
The empirical evidence confirms that AM can radically shift the performance frontier for problematic parts in conventional supply. The dynamic supplier selection practice allows operations managers to choose a supplier or multiple suppliers for idiosyncratic parts both existing and new. The selection can be based on cost reduction, lead-time reduction, and trade-offs in cost and lead-time according to customer requirements without significant transaction costs.
The generative mechanism of successful outcome is triggered by the simplicity in AM process instructions. Encapsulating the design and production-process instructions reduces mundane transaction costs and enables highly interactive model-based supplier relationships for decentralized manufacturing. The accuracy of AM technologies is predominant for establishing and substantiating appropriate practices. The process interruption-based embedding opens a direction for creating smart parts facilitating condition monitoring, machine learning, and preventive maintenance for Industry 4.0.
This doctoral dissertation aids researchers and practitioners in switching parts over to AM technologies from large spare part repositories with a dynamic response as opposed to a static choice with conventional manufacturing involving increasing minimum order quantities, costs, and lead-times. It can allow a dynamic response for accurate, personalized, and smart end-use parts.
A finite-element (FE) based thermomechanical modeling approach is developed in this study to prov... more A finite-element (FE) based thermomechanical modeling approach is developed in this study to provide a prediction of the mesoscale melt-pool behavior and part-scale properties for AlSi10Mg alloy. On the mesoscale, the widely adopted Goldak heat source model is used to predict melt pool formed by laser during powder bed fusion process (PBF), which, however, requires the determination of certain parameters as they control temperature distribution and hence melt pool boundaries. An approach based on a systematic parametric study is proposed in the study to determine these parameters, such as absorption coefficient and transient temperature evolution compared with the morphology of melt pool from experiments. Focusing on the part-scale domain, there is increasing demand for predicting geometric distortions and analyzing underlying residual stresses, which are highly influenced by the mesh size and initial temperature (Tinitial) setup. This study aims to propose a strategy for the correl...
International Journal of Operations & Production Management
PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare... more PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.Design/methodology/approachIn this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.FindingsThe practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successf...
Aalto-yliopiston ja Teknologian tutkimuskeskus VTT:n yhteisjulkaisu.Digitaaliset varaosat on kons... more Aalto-yliopiston ja Teknologian tutkimuskeskus VTT:n yhteisjulkaisu.Digitaaliset varaosat on konsepti, jossa varaosat ja niihin liittyvä valmistustieto säilytetään ja siirretään digitaalisessa muodossa. Varaosien valmistus tapahtuu 3D-tulostamalla tarpeen mukaan yleensä lähellä loppukäyttäjää. Varaosien digitalisoinnilla tavoitellaan parempaa, joustavampaa ja nopeampaa varaosien saatavuutta sekä pienempiä varastointi-, valmistus- ja kuljetuskustannuksia. Nopeammalla varaosien toimittamisella voidaan myös pienentää seisokkiaikoja, mikä voi tarkoittaa merkittäviä kustannussäästöjä. Oleellista yritysten varaosien digitalisoinnissa on löytää varaosakirjastoista ne osat, joiden säilyttämisestä digitaalisessa muodossa ja valmistamisesta 3D-tulostamalla saadaan suurin hyöty. Tällaisia osia ovat etenkin vanhojen laitteiden ja koneiden osat sekä hitaasti kiertävät osat, jotka ovat geometrialtaan monimutkaisia. 3D-tulostamalla voidaan nykypäivänä valmistaa suorituskykyisiä kappaleita ja menet...
Switchover to industrial additive manufacturing: dynamic decision-making for problematic spare parts, 2022
Purpose: Introducing additive manufacturing (AM) in a multinational corporation with a global spa... more Purpose:
Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.
Design/methodology/approach:
In this explorative study, the authors develop a procedure in collaboration with the spare parts operations managers of a case company for dynamic operational decision making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.
Findings: The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.
Originality/value: The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.
Legal Aspects of Additive Manufacturing in the Military Logistics
Security Dimensions, Mar 31, 2022
Background: The use of Additive Manufacturing (AM) in military logistics is limited by the uncert... more Background: The use of Additive Manufacturing (AM) in military logistics is limited by the uncertainty about the legal risks of using the method. Objectives: The purpose of this study was to identify the potential legal risks associated with AM of military materiel and its effects. The study was conducted as a case study, where AM is considered in military logistics. Methods: Legal issues were approached from the normative point of view in the context of potential legal dispute situations in four different cases. The topic was studied by reviewing and analyzing literature sources, official sources, and Finnish Defence Forces military materiel purchasing contracts and interviews. The study interpreted and systematized the general principles of IPR and contract law when using AM in the military. Results: An identified result of the study was that the use of AM to print military materiel under normal conditions may entail risks that should be contractually managed. In other cases, e.g. crises or wartime, there are risks, but they are difficult to predict and manage or are acceptable. The results of the study concern countries with a similar military economic system to Finland in terms of military logistics. The results can be applied to the activities of different security sectors, due to the similarity of their activities. Conclusions: The use of AM can enable the material to be used in safety situations where speed and versatility are required for material readiness. The use of the method requires a legal right, the holder of which has the right to manufacture the product. In different situations, the legal basis is open to interpretation and may pose risks to the decision-maker. The research will provide information to decision-makers for the preparation of contracts for the acquisition and maintenance of military material and implementing AM to military logistics.
Materials Science in Additive Manufacturing, 2025
Metal additive manufacturing (AM) has attracted significant interest in high-value industries due... more Metal additive manufacturing (AM) has attracted significant interest in high-value industries due to its ability to produce complex parts flexibly, but the reliance on costly manual monitoring remains a major burden for quality control. Artificial intelligence (AI)-driven models for automated defect detection are emerging as promising solutions. This study contributes a new annotated dataset for AI research in AM and evaluates the performance of four widely used convolutional neural network (CNN) models in detecting powder bed morphology defects, based on layer-wise images acquired by the EOSTATE PowderBed system during the metal laser-based powder bed fusion process. The models were trained through transfer learning methods with manually labeled and pre-processed data. Results demonstrated that ResNet50 and EfficientNetV2B0 achieved over 99% accuracy in defect classification, while YOLOv5 outperformed Faster region-based-CNN in defect detection and localization. However, lower average precision values in object detection tasks were attributed to variability in defect scales and annotation quality. This study confirms the potential of AI-based models for defect identification in AM, with YOLOv5 demonstrating clear advantages in managing complex, multi-scale defects. Future improvements will focus on expanding the dataset and refining annotation strategies to further enhance model robustness.
Virtual and Physical Prototyping , 2025
This pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6... more This pilot study develops a process to evaluate in-situ defect detection and self-healing in Ti-6Al-4V fabricated using laser-based powder bed fusion. A tailor-made test specimen was designed and manufactured for the nanofocus tube X-ray computed tomography (XCT) system. In situ optical tomography was used to capture infrared images containing heat signatures of the hot laser interaction zone. Depicting natural process variation, defective regions were seeded using process manipulation (up to ±30%) in proximity of the experimental standard volumetric energy density (VED). The concomitant defects and heat signatures were both spatially and temporally captured. The results indicate that porosity significantly grows from an average value of 27 parts per million (PPM) to a value of 337 PPM comprising defect sizes of <112 µm when the VED increases by 30%. The outcome confirmed that Ti-6Al-4V can self-heal these defective regions by up to 7 ± 1 layers using the standard VED. A convolutional neural network was trained (n = 211) and was verified with XCT. The model demonstrated prediction accuracy of 94% for the six classes of unfamiliar defective regions. This work enables in-situ detection and healing of defective regions caused by process uncertainty that can shift the quality frontier of novel product design and development.
Materials & Design, 2025
This paper introduces a novel 4D printing approach for dynamic displays, using thermochromic-PLA ... more This paper introduces a novel 4D printing approach for dynamic displays, using thermochromic-PLA composites embedded with FeCrAl wires activated by Joule heating. Previous studies struggled with high energy consumption, heat bleed, and slow activation. By embedding continuous resistance wires directly within the thermochromic-plastic matrix, localized, efficient heating is achieved, improving response speed and energy efficiency. The study optimizes color change by focusing on specimen thickness, current intensity, and pulse duration. A customized G-code workflow allowed for precise wire integration into 3D-printed specimens fabricated using a composite dual-nozzle co-extrusion printer, enabling controlled color changes upon activation. Experiments identified 1.5 mm as the ideal specimen thickness, balancing rapid response time (~78.5 s for 80 % color change) with effective wire concealment. A water medium significantly accelerated recovery time compared to air, emphasizing the benefits of faster heat dissipation. The color change was found to be linearly proportional to pulse-on time and the square of the current. Shorter pulses at higher currents resulted in more efficient and predictable color transformations than constant current inputs. A functional 7-segment display was fabricated, demonstrating the practical application of this technology for smart displays, sensors, and other 4D-printing applications.
Materials, Mar 22, 2024
In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser... more In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development.
Additive manufacturing of self-sensing parts through material extrusion
Virtual and physical prototyping, Feb 28, 2024
The objective of this study is to develop and evaluate self-sensing capabilities in additively ma... more The objective of this study is to develop and evaluate self-sensing capabilities in additively manufactured parts by embedding conductive elements that are copper and continuous carbon fiber. Two sets of test specimen were manufactured using a custom g-code on material extrusion-based Anisoprint A4 machine. Each set contained copper and continuous carbon fiber in an amorphous thermoplastic matrix. A tailor-made test setup was developed by improvising the American Society for Testing and Materials (ASTM D790) three-point loading system. Electrical resistance measurements were conducted under flexural loads to evaluate the selfsensing capability of each test specimen. The results confirmed that material extrusion technology can allow production of self-sensing parts. The electrical resistance increases linearly (Sensing tolerance <±2.6%, R 2 >93.8% p-value < 0.005), establishing a strong correlation with applied force and strain. The work allows for creating smart parts that can facilitate big data collection, analysis, and evidence-based decision-making for condition monitoring and preventive maintenance needed for Industry 4.0.
Defect detection in laser-based powder bed fusion process using machine learning classification methods
IOP Conference Series: Materials Science and Engineering, Nov 30, 2023
The aim of this study is to deploy machine learning (ML) classification methods to detect defecti... more The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design-and shape-agnostic.
Progress in Additive Manufacturing, Dec 18, 2023
Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and ... more Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and wide-ranging applications. Understanding the bending shape recovery characteristics of 3D printed SMPCs is crucial for optimizing their performance. This study focuses on investigating the influence of different fiber orientations of continuous carbon fiber (CCF) in thermally stimulated SMPC. By controlling printing parameters and fiber orientation during the 3D printing process, we fabricate tailor-made rectangular composite test specimens. These specimens are subjected to controlled bending above the glass transition temperature of the polymer, inducing temporary deformation. The subsequent shape recovery process is carefully captured through high-speed video. Precise measurements of the bending curvature over time are obtained using the row-by-row image processing technique and analyzed. The shape recovery rate, shape recovery ratio, and shape fixity ratio of the test specimens were evaluated as a function of three CCF layout arrangements as well as fiber infill density embedded in Shape Memory Polymer (SMP) test specimens. The results revealed that the addition of CCF in the polymer matrix has a significant impact on shape memory behavior. Vertically aligned CCF in the SMP matrix improves the shape recovery ratio (92.97% compared to 78.77% of the pure SMP sample), while horizontal alignment of CCF ensures maximum shape fixity ratio (91.78% compared to 66.22% of the pure SMP sample). The cross-aligned CCF sample provides good recovery as well as fixity values. Further, it was observed that the horizontal alignment of CCF yields the fastest recovery performance. The outcome confirms that optimizing the fiber orientation enhances shape memory performance. Also, 40% of fiber infill density had greater shape fixity and overall recovery performance when compared to 30% and 50%. These findings have implications for tailored and high-performance SMPCs in biomedical devices, aerospace components, and robotics. Understanding temporal curvature behavior enables optimizing the design of materials with precise control over shape recovery. This research contributes to the design and optimization of SMPCs for diverse applications. Shape memory polymer composites • 4D printing • Continuous carbon fiber-reinforced SMP • Fiber orientation and layout • Bending recovery performance • Material extrusion (MEX) • Fused filament fabrication (FFF) • Fused deposition modeling (FDM) * S. Siddharth Kumar
Virtual and Physical Prototyping, 2024
The objective of this study is to develop and evaluate self-sensing capabilities in additively ma... more The objective of this study is to develop and evaluate self-sensing capabilities in additively manufactured parts by embedding conductive elements that are copper and continuous carbon fiber. Two sets of test specimen were manufactured using a custom g-code on material extrusion-based Anisoprint A4 machine. Each set contained copper and continuous carbon fiber in an amorphous thermoplastic matrix. A tailor-made test setup was developed by improvising the American Society for Testing and Materials (ASTM D790) three-point loading system. Electrical resistance measurements were conducted under flexural loads to evaluate the selfsensing capability of each test specimen. The results confirmed that material extrusion technology can allow production of self-sensing parts. The electrical resistance increases linearly (Sensing tolerance <±2.6%, R^2 >93.8% p-value < 0.005), establishing a strong correlation with applied force and strain. The work allows for creating smart parts that can facilitate big data collection, analysis, and evidence-based decision-making for condition monitoring and preventive maintenance needed for Industry 4.0.
IOP Conference Series: Materials Science and Engineering
Jatropha Curcas oil is a non-edible oil which is used for Jatropha biodiesel (JBD) production. Ja... more Jatropha Curcas oil is a non-edible oil which is used for Jatropha biodiesel (JBD) production. Jatropha biodiesel is produced using transesterification technique and it is used as an alternative fuel in CI diesel engine without any hardware modification. Jatropha biodiesel is used in CI diesel engine with various volumetric concentrations (blends) such as JBD5, JBD15, JBD25, JBD35 and JBD45. The combustion parameters such as in-cylinder pressure, rate of pressure rise, net heat release, cumulative heat release, mass fraction burned are analyzed and compared for all blends combustion data with mineral diesel fuel (D100).
IOP Conference Series: Materials Science and Engineering, 2023
The aim of this study is to deploy machine learning (ML) classification methods to detect defecti... more The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design-and shape-agnostic.
Progress in Additive Manufacturing, 2023
Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and ... more Shape memory polymer composites (SMPCs) have gained attention for their shape memory effects and wide-ranging applications. Understanding the bending shape recovery characteristics of 3D printed SMPCs is crucial for optimizing their performance. This study focuses on investigating the influence of different fiber orientations of continuous carbon fiber (CCF) in thermally stimulated SMPC. By controlling printing parameters and fiber orientation during the 3D printing process, we fabricate tailor-made rectangular composite test specimens. These specimens are subjected to controlled bending above the glass transition temperature of the polymer, inducing temporary deformation. The subsequent shape recovery process is carefully captured through high-speed video. Precise measurements of the bending curvature over time are obtained using the row-by-row image processing technique and analyzed. The shape recovery rate, shape recovery ratio, and shape fixity ratio of the test specimens were evaluated as a function of three CCF layout arrangements as well as fiber infill density embedded in Shape Memory Polymer (SMP) test specimens. The results revealed that the addition of CCF in the polymer matrix has a significant impact on shape memory behavior. Vertically aligned CCF in the SMP matrix improves the shape recovery ratio (92.97% compared to 78.77% of the pure SMP sample), while horizontal alignment of CCF ensures maximum shape fixity ratio (91.78% compared to 66.22% of the pure SMP sample). The cross-aligned CCF sample provides good recovery as well as fixity values. Further, it was observed that the horizontal alignment of CCF yields the fastest recovery performance. The outcome confirms that optimizing the fiber orientation enhances shape memory performance. Also, 40% of fiber infill density had greater shape fixity and overall recovery performance when compared to 30% and 50%. These findings have implications for tailored and high-performance SMPCs in biomedical devices, aerospace components, and robotics. Understanding temporal curvature behavior enables optimizing the design of materials with precise control over shape recovery. This research contributes to the design and optimization of SMPCs for diverse applications.
The International Journal of Advanced Manufacturing Technology, 2023
A finite element-based thermomechanical modeling approach is developed in this study to provide a... more A finite element-based thermomechanical modeling approach is developed in this study to provide a prediction of the mesoscale melt pool behavior and part-scale properties for AlSi10Mg alloy. On the mesoscale, the widely adopted Goldak heat source model is used to predict melt pool formed by laser during powder bed fusion process. This requires the determination of certain parameters as they control temperature distribution and, hence, melt pool boundaries. A systematic parametric approach is proposed to determine parameters, i.e., absorption coefficient and transient temperature evolution. The simulation results are compared in terms of morphology of melt pool with the literature results. Considering the part-scale domain, there is increasing demand for predicting geometric distortions and analyzing underlying residual stresses, which are highly influenced by the mesh size and initial temperature setup. This study aims to propose a strategy for evaluating the correlation between the mesh size and the initial temperature to provide correct residual stresses when increasing the scale of the model for efficiency. The outcomes revealed that the predicted melt pool error produced by optimal Goldak function parameters is between 5 and 12%. On the part-scale, the finite element model is less sensitive to mesh size for distortion prediction, and layer-lumping can be used to increase the speed of simulation. The effect of large time increments and layer lumping can be compensated by appropriate initial temperature value for AlSi10Mg. The study aids practitioners and researchers to establish and validate design for additive manufacturing within the scope of desired part quality metrics.
Annals of Work Exposures and Health
Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were... more Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were studied in three different work environments. Workplaces utilized powder bed fusion, material extrusion, and binder jetting techniques with metal and polymer powders, polymer filaments, and gypsum powder, respectively. The AM processes were studied from operator’s point of view to identify exposure events and possible safety risks. Total number of particle concentrations were measured in the range of 10 nm to 300 nm from operator’s breathing zone using portable devices and in the range of 2.5 nm to 10 µm from close vicinity of the AM machines using stationary measurement devices. Gas-phase compounds were measured with photoionization, electrochemical sensors, and an active air sampling method which were eventually followed by laboratory analyses. The duration of the measurements varied from 3 to 5 days during which the manufacturing processes were practically continuous. We identified s...
Annals of Work Exposures and Health, 2023
Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were... more Particle and gaseous contaminants from industrial scale additive manufacturing (AM) machines were studied in three different work environments. Workplaces utilized powder bed fusion, material extrusion, and binder jetting techniques with metal and polymer powders, polymer filaments, and gypsum powder, respectively. The AM processes were studied from operator's point of view to identify exposure events and possible safety risks. Total number of particle concentrations were measured in the range of 10 nm to 300 nm from operator's breathing zone using portable devices and in the range of 2.5 nm to 10 µm from close vicinity of the AM machines using stationary measurement devices. Gas-phase compounds were measured with photoionization, electrochemical sensors, and an active air sampling method which were eventually followed by laboratory analyses. The duration of the measurements varied from 3 to 5 days during which the manufacturing processes were practically continuous. We identified several work phases in which an operator can potentially be exposed by inhalation (pulmonary exposure) to airborne emissions. A skin exposure was also identified as a potential risk factor based on the observations made on work tasks related to the AM process. The results confirmed that nanosized particles were present in the breathing air of the workspace when the ventilation of the AM machine was inadequate. Metal powders were not measured from the workstation air thanks to the closed system and suitable risk control procedures. Still, handling of metal powders and AM materials that can act as skin irritants such as epoxy resins were found to pose a potential risk for workers. This emphasizes the importance of appropriate control measures for ventilation and material handling that should be addressed in AM operations and environment.
Aalto University Doctoral Thesis, 2022
Additive manufacturing (AM) is rapidly developing into a general-purpose technology akin to elect... more Additive manufacturing (AM) is rapidly developing into a general-purpose technology akin to electric drives and computers serving a plethora of applications. The advent and proliferation of the additive process triggering Industry 4.0 is encouraging academics and practitioners to establish new practices, designs, and modes of creating and supplying end-use parts.
Contributing to this emerging stream of research on AM technologies, the overarching objective of this doctoral dissertation is to discover situations and ways in which companies can benefit from implementing AM in conjunction with conventional manufacturing technologies. This is addressed and limited by three sub-objectives.
First sub-objective establishes a new operational practice—dynamic supplier selection using the build-to-model mode of manufacturing—for the provision of idiosyncratic spare parts to improve the after-sales operations of a case company. Second sub-objective estimates the combined uncertainty and the worst-case error in creating an end-use part, particularly a personalized implant made by radiologic images, thresholding, digital design, and AM. Third sub-objective develops process interruption-based embedding and creates prototypes of smart parts, in particular intelligent implants using four AM technologies. The work uses a multi-methods approach combining three case studies, experiments, and research methodologies to achieve the aim of theoretical insights, practical relevance, and innovation.
The empirical evidence confirms that AM can radically shift the performance frontier for problematic parts in conventional supply. The dynamic supplier selection practice allows operations managers to choose a supplier or multiple suppliers for idiosyncratic parts both existing and new. The selection can be based on cost reduction, lead-time reduction, and trade-offs in cost and lead-time according to customer requirements without significant transaction costs.
The generative mechanism of successful outcome is triggered by the simplicity in AM process instructions. Encapsulating the design and production-process instructions reduces mundane transaction costs and enables highly interactive model-based supplier relationships for decentralized manufacturing. The accuracy of AM technologies is predominant for establishing and substantiating appropriate practices. The process interruption-based embedding opens a direction for creating smart parts facilitating condition monitoring, machine learning, and preventive maintenance for Industry 4.0.
This doctoral dissertation aids researchers and practitioners in switching parts over to AM technologies from large spare part repositories with a dynamic response as opposed to a static choice with conventional manufacturing involving increasing minimum order quantities, costs, and lead-times. It can allow a dynamic response for accurate, personalized, and smart end-use parts.
A finite-element (FE) based thermomechanical modeling approach is developed in this study to prov... more A finite-element (FE) based thermomechanical modeling approach is developed in this study to provide a prediction of the mesoscale melt-pool behavior and part-scale properties for AlSi10Mg alloy. On the mesoscale, the widely adopted Goldak heat source model is used to predict melt pool formed by laser during powder bed fusion process (PBF), which, however, requires the determination of certain parameters as they control temperature distribution and hence melt pool boundaries. An approach based on a systematic parametric study is proposed in the study to determine these parameters, such as absorption coefficient and transient temperature evolution compared with the morphology of melt pool from experiments. Focusing on the part-scale domain, there is increasing demand for predicting geometric distortions and analyzing underlying residual stresses, which are highly influenced by the mesh size and initial temperature (Tinitial) setup. This study aims to propose a strategy for the correl...
International Journal of Operations & Production Management
PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare... more PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.Design/methodology/approachIn this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.FindingsThe practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successf...
Aalto-yliopiston ja Teknologian tutkimuskeskus VTT:n yhteisjulkaisu.Digitaaliset varaosat on kons... more Aalto-yliopiston ja Teknologian tutkimuskeskus VTT:n yhteisjulkaisu.Digitaaliset varaosat on konsepti, jossa varaosat ja niihin liittyvä valmistustieto säilytetään ja siirretään digitaalisessa muodossa. Varaosien valmistus tapahtuu 3D-tulostamalla tarpeen mukaan yleensä lähellä loppukäyttäjää. Varaosien digitalisoinnilla tavoitellaan parempaa, joustavampaa ja nopeampaa varaosien saatavuutta sekä pienempiä varastointi-, valmistus- ja kuljetuskustannuksia. Nopeammalla varaosien toimittamisella voidaan myös pienentää seisokkiaikoja, mikä voi tarkoittaa merkittäviä kustannussäästöjä. Oleellista yritysten varaosien digitalisoinnissa on löytää varaosakirjastoista ne osat, joiden säilyttämisestä digitaalisessa muodossa ja valmistamisesta 3D-tulostamalla saadaan suurin hyöty. Tällaisia osia ovat etenkin vanhojen laitteiden ja koneiden osat sekä hitaasti kiertävät osat, jotka ovat geometrialtaan monimutkaisia. 3D-tulostamalla voidaan nykypäivänä valmistaa suorituskykyisiä kappaleita ja menet...
Switchover to industrial additive manufacturing: dynamic decision-making for problematic spare parts, 2022
Purpose: Introducing additive manufacturing (AM) in a multinational corporation with a global spa... more Purpose:
Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.
Design/methodology/approach:
In this explorative study, the authors develop a procedure in collaboration with the spare parts operations managers of a case company for dynamic operational decision making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.
Findings: The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.
Originality/value: The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.
Legal Aspects of Additive Manufacturing in the Military Logistics
Security Dimensions, Mar 31, 2022
Background: The use of Additive Manufacturing (AM) in military logistics is limited by the uncert... more Background: The use of Additive Manufacturing (AM) in military logistics is limited by the uncertainty about the legal risks of using the method. Objectives: The purpose of this study was to identify the potential legal risks associated with AM of military materiel and its effects. The study was conducted as a case study, where AM is considered in military logistics. Methods: Legal issues were approached from the normative point of view in the context of potential legal dispute situations in four different cases. The topic was studied by reviewing and analyzing literature sources, official sources, and Finnish Defence Forces military materiel purchasing contracts and interviews. The study interpreted and systematized the general principles of IPR and contract law when using AM in the military. Results: An identified result of the study was that the use of AM to print military materiel under normal conditions may entail risks that should be contractually managed. In other cases, e.g. crises or wartime, there are risks, but they are difficult to predict and manage or are acceptable. The results of the study concern countries with a similar military economic system to Finland in terms of military logistics. The results can be applied to the activities of different security sectors, due to the similarity of their activities. Conclusions: The use of AM can enable the material to be used in safety situations where speed and versatility are required for material readiness. The use of the method requires a legal right, the holder of which has the right to manufacture the product. In different situations, the legal basis is open to interpretation and may pose risks to the decision-maker. The research will provide information to decision-makers for the preparation of contracts for the acquisition and maintenance of military material and implementing AM to military logistics.