A fuzzy based decision making approach for metal additive manufacturing (original) (raw)
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Fuzzy logic in manufacturing: A review of literature and a specialized application
International Journal of Production Economics, 2011
Manufacturing decisions inherently face uncertainties and imprecision. Fuzzy logic, and tools based on fuzzy logic, allow for the inclusion of uncertainties and imperfect information in decision making models, making them well suited for manufacturing decisions. In this study, we first review the progression in the use of fuzzy tools in tackling different manufacturing issues during the past two decades. We then apply fuzzy linear programming to a less emphasized, but important issue in manufacturing, namely that of product mix prioritization. The proposed algorithm, based on linear programming with fuzzy constraints and integer variables, provides several advantages to existing algorithm as it carries increased ease in understanding, in use, and provides flexibility in its application.
Data-driven Uncertainty Quantification Framework in Metal Additive Manufacturing
This paper presents the uncertainty quantification (UQ) framework with a data-driven approach using experimental data in metal additive manufacturing (AM). This framework consists of four steps. First, the experimental data, including process parameters and signatures, are obtained by performing tests in various conditions. Next, the model is constructed by surrogate modeling and a machine learning algorithm using the obtained data. Then, the uncertainties in a quantity of interest (QoI), such as bead geometry, surface roughness, or mechanical properties, are quantified. Lastly, the UQ is verified and validated using the experimental data. The proposed framework is demonstrated with the data-driven UQ of the bead geometry in gas tungsten arc welding (GTAW)-based wire + arc additive manufacturing (WAAM). In this case study, the uncertainty sources are process parameters and signatures, and the QoI is bead geometry. The process parameters are wire feed rate (WFR), travel speed (TS), a...
Acta Mechanica Slovaca
This article has presented a technical concept for producing precisely desired Additive Manufactured (AM) metallic products using Artificial Intelligence (AI). Due to the stochastic nature of the metallic AM process, which causes a greater variance in product properties compared to traditional manufacturing processes, significant inaccuracies in metallurgical properties, as well as geometry, occur. The physics behind these phenomena are related to the melting process, bonding, cooling rate, shrinkage, support condition, part orientation. However, by controlling these phenomena, a wide range of product features can be achieved using the fabricating parameters. A variety of fabricating parameters are involved in the metal AM process, but an appropriate combination of these parameters for a given material is required to obtain an accurate and desired product. Zero defect product can be achieved by controlling these parameters by implementing Knowledge-Based System (KBS). A suitable combination of manufacturing parameters can be determined using mathematical tools with AI, considering the manufacturing time and cost. The knowledge required to integrate AM manufacturing characteristics and constraints into the design and fabricating process is beyond the capabilities of any single engineer. Concurrent Engineering enables the integration of design and manufacturing to enable trades based not only on product performance, but also on other criteria that are not easily evaluated, such as production capability and support. A decision support system or KBS that can guide manufacturing issues during the preliminary design process would be an invaluable tool for system designers. The main objective of this paper is to clearly describe the metal AM manufacturing process problem and show how to develop a KBS for manufacturing process determination.
An ad hoc decision support method over additive vs. conventional manufacturing
MATEC Web of Conferences
The mechanical design process considers numerous factors. Requirements related to performance and quality, limitations by legislation, standards, methods utilized or technological boundaries, urgency, cost, data preparation and preservation, design flexibility and organizational aspects. Successful design consists of proper decisions on form, geometry, materials, manufacturing methods, quality, reliability and more. Nowadays, a critical decision during design and realization of technological objects is whether they should be made conventionally or with Additive Manufacturing (AM)/3D Printing methods. Such a decision occurs under time-pressure or via a broader strategy for technological switch, is complex, multi-parametric and bears uncertainty and risk. A simple, effective and substantiated method to assist decisions for switching from conventional to AM could prove very useful. This paper refers to recent trends and activity in international AM-related standards, then presents and ...
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This research developed mathematical models to optimize process performance for multiple pentagon fuzzy quality responses. Initially, each quality response was represented by a pentagon membership function. Then, the combination of optimal factor levels was obtained for each response replicate. Those optimal combinations were then used to construct pentagon regression models for each response. A pentagon fuzzy optimization model was formulated and solved to determine the combination of optimal factor levels at each element of pentagon response's fuzzy number. Two real case studies, i.e. wireelectrical discharge machining and sputtering process, were provided for illustration. Optimal results of the two case studies revealed that the proposed procedure effectively optimized performance under uncertainty and provided larger improvement in multiple quality characteristics. In conclusion, the proposed procedure may enhance the process engineer's knowledge about effects of uncertainty on process/product performance and help practitioners decide the proper adjustments of factor levels in order to enhance performance of electrical discharge machining and sputtering process.
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Rapid Prototyping Journal, 2020
Purpose-The decision-making for additive manufacturing (AM) process selection is typically applied in the end of the product design stages based upon an already finished design. However, due to unique characteristics of AM processes, the part needs to be designed for the specific AM process. This requires potentially feasible AM techniques to be identified in early design stages. This study aims to develop such a decision-making methodology that can seamlessly be integrated in the product design stages to facilitate AM process selection and assist product/part design. Design/methodology/approach-The decision-making methodology consists of four elements, namely initial screening, technical evaluation and selection of feasible AM processes, re-evaluation of the feasible process, and production machine selection. Prior to the design phase, the methodology determines whether AM production is suitable based on the given design requirements. As the design progresses, a more accurate process selection in terms of technical and economic viability is performed using the Analytic Hierarchy Process technique. Features that would cause potential manufacturability issues and increased production costs will be identified and modified. Finally, a production machine that is best suited for the finished product design is identified. Findings-The methodology was found to be able to facilitate the design process by enabling designers to identify appropriate AM technique and production machine, which was demonstrated in the case study. Originality/value-This study addresses the gap between the isolated product design and process selection stages by developing the decision-making methodology that can be integrated in product design stages.
Robotics and Computer-Integrated Manufacturing
Market dynamics of today are constantly evolving in the presence of emerging technologies such as Additive Manufacturing (AM). Drivers such as mass customization strategies, high part-complexity needs, shorter product development cycles, a large pool of materials to choose from, abundant manufacturing processes, diverse streams of applications (e.g. aerospace, motor vehicles, and health care) and high cost incurred due to manufacturability of the part have made it essential to choose the right compromise of materials, manufacturing processes and associated machines in early stages of design considering the Design for Additive Manufacturing guidelines. There exists a complex relationship between AM products and their process data. However, the literature to-date shows very less studies targeting this integration. As several criteria, material attributes and process functionality requirements are involved for decision making in the industries, this paper introduces a generic decision methodology, based on multi-criteria decision-making tools, that will not only provide a set of compromised AM materials, processes and machines but will also act as a guideline for designers to achieve a strong foothold in the AM industry by providing practical solutions containing design oriented and feasible material-machine combinations from a current database of 38 renowned AM vendors in the world. An industrial case study, related to aerospace, has also been tested in detail via the proposed methodology.
Application of a fuzzy-logic based model for risk assessment in additive manufacturing R&D projects
Computers & Industrial Engineering, 2020
Experts from industry and academics have highlighted Additive Manufacturing (AM) as a technology that is revolutionizing manufacturing. AM is a process that consists of creating a three-dimensional object by incorporating layers of a material such as metal or polymer. This research studies risks associated with AM R&D Project Management. A significant set of risks with a potential negative impact on project objectives in terms of scope, schedule, cost and quality are identified through an extensive literature review. These risks are assessed through a survey answered by ninety academics and professionals with noteworthy sector expertise. This process is made by the measurement of two parameters: likelihood of occurrence and impact on project objectives. According to the responses of the experts, the level of relevance of each risk is calculated, innovatively, through a fuzzy logic-based model, specifically developed for this study, implemented in MATLAB Fuzzy Logic Toolbox. The results of this study show that the risks “Defects occurring during the manufacturing process”, “Defective design”, “Poor communication in the project team” and “Insufficient financing” are determined as the most critical in AM R&D Project Management. The proposed model is presented as a powerful new tool for organizations and academics, to prioritize the risks that are more critical to develop appropriate response strategies to achieve the success of their projects.