Intelligent Manufacturing Systems Research Papers (original) (raw)

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- by Nathan Proudlove and +1
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- Algorithms, Artificial Intelligence, Manufacturing, Intelligent Manufacturing Systems
- by I. Jawahir and +1
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- Materials Engineering, Mechanical Engineering, Intelligent Manufacturing Systems, Materials

This article presents an overview of a neutral reference architecture and data model for integrating distributed manufacturing simulation systems with each other, with other manufacturing software applications, and with manufacturing data repositories. Other manufacturing software applications include but are not limited to systems used to design products, specify processes, engineer manufacturing systems, and manage production. The architecture identifies the software building blocks and interfaces that will facilitate the integration of distributed simulation systems and enable the integration of those systems with other manufacturing software applications. The architecture builds on the High Level Architecture standard for simulation. The National Institute of Standards and Technology and its collaborators have created several implementations of portions of the architecture using commercial off-the-shelf simulators. These implementations demonstrated the feasibility of the archit...

- by Frank Riddick
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- Cognitive Science, XML, Intelligent Manufacturing Systems, Simulation

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively.

- by Esra Tekez
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- Engineering, Knowledge Management, Quality Management, Strategic Management

Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications.

A system devoted to the planning of motion and assembly operations n robotized assembly cells at abstract level is presented. The goal is to achieve an interactive planning system for assembly tasks. Automatic plan generation techniques are interplayed with external interactive help from the human expert. The interaction is realized through a graphical simulation system. For instance, a collision-free skeleton-trajectory can be specified by the human expert via the simulation system. Proposed architecture for a cell programming system has a multilevel structure in which a task can
be. described at multiple abstraction levels. The problem is analyzed
in a CIM context in which the integration of information and knowledge plays an important role. A detailed description of the planning activity is m,de and experimental results already achieved are described. Finally, current status of work and future plan are presented.

The path towards Industrie 4.0, requires that factory automation problems cope with the cyber-physical system complexity and its challenges. Some practical experiences and literature in the field testify that the role of the database management systems is becoming central for control and automation technology in the new industrial scenario. This article proposes database-centric technology and architectures that seamlessly integrate networking, artificial intelligence and real-time control issues into a unified model of computing. The proposed methodology is also viable for the development of simulation and rapid prototyping tools for smart and advanced industrial automation.

- by Andrea Bonci and +2
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- Embedded Systems, Intelligent Manufacturing Systems, Computational Methods, Database Management Systems

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP.

- by Petr Kadera
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- Engineering, Decision Making, Intelligent Manufacturing Systems, Control Systems

In distributed manufacturing systems, the level of interoperability of hardware and software components depends on the quality and flexibility of their information models. Syntactic descriptions of input and output parameters, e.g., using interface description languages (IDL), are not sufficient when it comes to evaluating whether a manufacturing resource provides the capabilities that are required for performing a particular process step on a product. The semantics of capabilities needs to be explicitly modelled and must be provided together with manufacturing resources. In this paper, we introduce concepts developed by the German BaSys 4.0 initiative dealing with semantically describing manufacturing skills, orchestrating higher-level skills from basic skills, and using them in a cognitive manufacturing framework.

- by Alexander Perzylo and +3
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- Systems Engineering, Ontology (Computer Science), Intelligent Manufacturing Systems, Cognitive Robotics

Understanding the variations in surface finish due to machining is a non-trivial task and cannot be very easily estimated even for a given set of machining parameters and operating conditions due to the complexity of interactions involved. In this work, an attempt has been made to propose an automated intelligent manufacturing system for the estimation and control of surface finish

In micro-nano systems technology (MNST), application of mechanical based machining operations such as micro turning, micro milling, micro EDM have shown promising trends to produce micro parts in batch scale. In order to ensure reproducibility better understanding on micro cutting process dynamics and sensitivity of machine stiffness and material characteristics becomes critical. In this paper, a methodology has been developed to assess machine stiffness and material dependent characteristics and demonstrated for micro turning operations conducted on DT-110 micro machining center. In this method, authors incorporate pattern matching algorithm to compare run data image of cutting force plots with that of reference plot. The reference plots of cutting forces v/s time were drawn from simulation run data computed from the micro turning process models. The run data plots of cutting force v/s time were drawn from the processed signal data obtained from the dynamometer during machining operation. The plots were fragmented into patterns and Euclidean distance computed between pair patterns of reference and measured cutting forces v/s time plot image represents the changes happened in machining conditions. This has been used to perform backward calculation to assess the machine stiffness response and material characteristic constants variations over machining time. In order to perform these comparative pattern error adjustments between reference and measured cutting force plots a fuzzy rule based algorithm has been developed.► Dynamic assessment of micro machine stiffness response and material characteristic constants. ► Micro turning force v/s time plots image pattern error adjustment methodology. ► Euclidean distance between patterns of theoretical and experimental plots is used as reference. ► Fuzzy rule based algorithm has been developed to facilitate pattern error comparison. ► Implemented on DT-110: micro machining center.

- by Naga Hanumaiah and +2
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- Intelligent Manufacturing Systems

Today's complex manufacturing systems operate in a changing environment rife with uncertainty. The performance of manufacturing companies ultimately hinges on their ability to rapidly adapt their production to current internal and external circumstances. On the base of a running national research project on digital enterprises and production networks, the paper illustrates how the concepts of intelligent manufacturing systems and digital

Today's complex manufacturing systems operate in a changing environment rife with uncertainty. The performance of manufacturing companies ultimately hinges on their ability to rapidly adapt their production to current internal and external circumstances. On the base of a running national research and development project (NRDP) on digital enterprises and production networks, the paper illustrates how the concepts of intelligent manufacturing

- by Robin Williams and +1
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- Mass Customisation, Intelligent Manufacturing Systems, Production Management, System Design
- by Zoran Đ Miljković and +1
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- Intelligent Manufacturing Systems, Intelligent Transport System, Mobile Robot, Source Code

The 7th EU framework programme for research (FP7) aims to provide new impetus to Europe’s growth and competitiveness, in realising that knowledge is Europe’s greatest resource. The programme places greater emphasis than in the past on research that is relevant to the needs of European industry, to help it compete internationally, and develop its role as a world leader in certain sectors. For the first time the framework programme provides support for the best in European investigator-driven research, with the creation of a European Research Council. The FP7 budget is 50% higher compared with its predecessor. The paper focuses on advances in information and communication technologies (ICT) under FP7 and on how the architecture, engineering and construction (AEC) industry and facility management (FM) can benefit through a systematic involvement. The technologies under development, ranging from wireless sensor networks, cooperative smart objects, plug-and-play control architectures, te...

The fourth industrial revolution is rapidly changing the manufacturing landscape. Due to the growing research and fast evolution in this field, no clear definitions of these concepts yet exist. This work provides a clear description of technological trends and gaps. We introduce a novel method to create a map of Industry 4.0 technologies, using natural language processing to extract technology terms from 14,667 research articles and applying network analysis. We identified eight clusters of Industry 4.0 technologies, which served as the basis for our analysis. Our results show that Industrial Internet of Things (IIoT) technologies have become the center of the Industry 4.0 technology map. This is in line with the initial definitions of Industry 4.0, which centered on IIoT. Given the recent growth in the importance of artificial intelligence (AI), we suggest accounting for AI's fundamental role in Industry 4.0 and understanding the fourth industrial revolution as an AI-powered natural collaboration between humans and machines. This article introduces a novel approach for literature reviews, and the results highlight trends and research gaps to guide future work and help these actors reap the benefits of digital transformations.

- by Benjamin Meindl and +1
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- Artificial Intelligence, Natural Language Processing, Intelligent Manufacturing Systems, Advanced Manufacturing

This paper discusses the problem of integrating human intelligence and skills into an intelligent manufacturing system. Our center has jointed the Holonic Manufacturing Systems (HMS) Project, an international consortium dedicated to developing holonic systems technologies. One of our contributions to this effort is in Work Package 6: flexible human integration. This paper focuses on one activity, namely, human integration into

Abstract: In this paper a concept of designing and building intelligent decision support systems in production management is introduced. The new approach to the design of intelligent management systems is proposed based on integration of artificial intelligence technologies (fuzzy logic, artificial neural networks, expert systems and genetic algorithms) with exact methods and models of decisions search and simulation techniques. The proposed approach allows for creating intelligent decision support systems of complex, unstructured management problems in fuzzy conditions. The systems learn based on accumulated data and adapt to changes in operation conditions.

Manufacturing enterprises play an important role in improving the economic environment of a country, The economic environment of many developed countries is under threat from emerging industrialized countries. Today, the capability of producing high quality products with shorter delivery times and the ability to produce according to the diverse customer requirements have become the characteristics required of order-qualifiers for manufacturing industries, Hence. Application of intelligent manufacturing systems and computer integrated manufacturing (CIM) has become the necessity to overcome the above issues while retaining the employment level and revenue of a country in today's highly competitive global market. With the developments taking place in CIM and its related technologies, the application of CIM in manufacturing enterprises has become a reality, This paper focuses on latest research developments in CIM and a new CIM wheel proposed to satisfy the emerging technological ...