Pub Date : 2024-02-13DOI: 10.3389/fmtec.2024.1320166
Vishnupriya Buggineni, Cheng Chen, Jaime Camelio
Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.
{"title":"Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility","authors":"Vishnupriya Buggineni, Cheng Chen, Jaime Camelio","doi":"10.3389/fmtec.2024.1320166","DOIUrl":"https://doi.org/10.3389/fmtec.2024.1320166","url":null,"abstract":"Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"30 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fmtec.2024.1320166
Vishnupriya Buggineni, Cheng Chen, Jaime Camelio
Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.
{"title":"Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility","authors":"Vishnupriya Buggineni, Cheng Chen, Jaime Camelio","doi":"10.3389/fmtec.2024.1320166","DOIUrl":"https://doi.org/10.3389/fmtec.2024.1320166","url":null,"abstract":"Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"59 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.
本研究概述了各种成像技术在智能制造领域的变革能力，并介绍了一些案例研究，主要侧重于各种原位和非原位成像方法，用于监测基于熔融的金属增材制造（AM）工艺，如定向能沉积（DED）、选择性激光熔化（SLM）和电子束熔化（EBM）。包括高速相机、热像仪和数码相机在内的原位成像技术越来越经济实惠、互补性强，对于实时监控、持续评估制造质量至关重要。例如，高速相机可以捕捉到激光与材料之间的动态相互作用，迅速检测出缺陷，而热像仪则可以识别熔池的热分布和潜在的异常情况。然后，利用现场成像收集的数据提取相关特征，以便有效控制工艺参数，从而优化 AM 工艺并最大限度地减少缺陷。另一方面，原位成像技术在综合部件分析中发挥着至关重要的作用。扫描电子显微镜 (SEM)、光学显微镜和三维纤度仪可以详细描述微结构特征、表面粗糙度、孔隙率和尺寸精度。利用人工智能（AI）算法，可以融合来自不同成像和其他多模态数据源的信息，从而更全面地了解制造过程。通过这种整合，人工智能算法可以分析综合数据，提取相关的见解和模式，从而为流程优化和质量保证做出明智的决策。最终，成像技术在增材制造中的威力在于它能够提供实时监控、精确控制和全面分析，使制造商能够在部件生产中实现最高水平的精度、可靠性和生产率。
{"title":"Imaging systems and techniques for fusion-based metal additive manufacturing: a review","authors":"Himanshu Balhara, Adithyaa Karthikeyan, Abhishek Hanchate, Tapan Ganatma Nakkina, S. Bukkapatnam","doi":"10.3389/fmtec.2023.1271190","DOIUrl":"https://doi.org/10.3389/fmtec.2023.1271190","url":null,"abstract":"This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"63 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.3389/fmtec.2023.1282843
Ava Recchia, Jill Urbanic
Leveraging Computer-Aided Design (CAD) and Manufacturing (CAM) tools with advanced Industry 4.0 (I4.0) technologies presents numerous opportunities for industries to optimize processes, improve efficiency, and reduce costs. While certain sectors have achieved success in this effort, others, including agriculture, are still in the early stages of implementation. The focus of this research paper is to explore the potential of I4.0 technologies and CAD/CAM tools in the development of pick and place solutions for harvesting produce. Key technologies driving this include Internet of Things (IoT), machine learning (ML), deep learning (DL), robotics, additive manufacturing (AM), and simulation. Robots are often utilized as the main mechanism for harvesting operations. AM rapid prototyping strategies assist with designing specialty end-effectors and grippers. ML and DL algorithms allow for real-time object and obstacle detection. A comprehensive review of the literature is presented with a summary of the recent state-of-the-art I4.0 solutions in agricultural harvesting and current challenges/barriers to I4.0 adoption and integration with CAD/CAM tools and processes. A framework has also been developed to facilitate future CAD/CAM research and development for agricultural harvesting in the era of I4.0.
{"title":"Leveraging I4.0 smart methodologies for developing solutions for harvesting produce","authors":"Ava Recchia, Jill Urbanic","doi":"10.3389/fmtec.2023.1282843","DOIUrl":"https://doi.org/10.3389/fmtec.2023.1282843","url":null,"abstract":"Leveraging Computer-Aided Design (CAD) and Manufacturing (CAM) tools with advanced Industry 4.0 (I4.0) technologies presents numerous opportunities for industries to optimize processes, improve efficiency, and reduce costs. While certain sectors have achieved success in this effort, others, including agriculture, are still in the early stages of implementation. The focus of this research paper is to explore the potential of I4.0 technologies and CAD/CAM tools in the development of pick and place solutions for harvesting produce. Key technologies driving this include Internet of Things (IoT), machine learning (ML), deep learning (DL), robotics, additive manufacturing (AM), and simulation. Robots are often utilized as the main mechanism for harvesting operations. AM rapid prototyping strategies assist with designing specialty end-effectors and grippers. ML and DL algorithms allow for real-time object and obstacle detection. A comprehensive review of the literature is presented with a summary of the recent state-of-the-art I4.0 solutions in agricultural harvesting and current challenges/barriers to I4.0 adoption and integration with CAD/CAM tools and processes. A framework has also been developed to facilitate future CAD/CAM research and development for agricultural harvesting in the era of I4.0.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"26 60","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.3389/fmtec.2023.1172564
Marco Melani, R. Furferi, F. Rotini, Luca Barbieri
The effects of global concerns such as climate change and environmental pollution must be considered also when dealing with the design or revamping of products or services nowadays. Existing practices, such as the 3R recovery approach and the Circular Economy approach, which aim to reduce waste and increase recycling, regeneration, and reusability, need to be applied during this process. This paper is embedded in this context, and it presents a study that was conducted within a manufacturing company on the possibility of reusing end-of-life mosquito nets, which are difficult to recycle, with the aim of reducing their environmental impact and creating new business opportunities. To achieve this goal, several methods for identifying new product applications have been evaluated from the literature and the most suitable one has been selected. The method is based on the identification of the product functions; then, with a series of patent searches, the Cooperative Patent Classifications (CPCs) of the resulting patents are extracted to be used as external stimuli during the design process. The results obtained in terms of ideas generated are then shown at the end of the paper, suggesting the actual effectiveness of the method applied.
{"title":"End of life of mosquito nets: searching for alternative uses through patent classification","authors":"Marco Melani, R. Furferi, F. Rotini, Luca Barbieri","doi":"10.3389/fmtec.2023.1172564","DOIUrl":"https://doi.org/10.3389/fmtec.2023.1172564","url":null,"abstract":"The effects of global concerns such as climate change and environmental pollution must be considered also when dealing with the design or revamping of products or services nowadays. Existing practices, such as the 3R recovery approach and the Circular Economy approach, which aim to reduce waste and increase recycling, regeneration, and reusability, need to be applied during this process. This paper is embedded in this context, and it presents a study that was conducted within a manufacturing company on the possibility of reusing end-of-life mosquito nets, which are difficult to recycle, with the aim of reducing their environmental impact and creating new business opportunities. To achieve this goal, several methods for identifying new product applications have been evaluated from the literature and the most suitable one has been selected. The method is based on the identification of the product functions; then, with a series of patent searches, the Cooperative Patent Classifications (CPCs) of the resulting patents are extracted to be used as external stimuli during the design process. The results obtained in terms of ideas generated are then shown at the end of the paper, suggesting the actual effectiveness of the method applied.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114592428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-09DOI: 10.3389/fmtec.2023.1009633
Silvan Zahno, J. Corre, Darko Petrovic, Gilles Mottiez, Loïc Fracheboud, Axel Amand, Steve Devènes, Gilbert Maître, F. Carrino
The digital twin (DT) concept plays a crucial role in Industry 4.0 and the digitalization of manufacturing processes. A DT is a virtual representation of a physical object, system, or process, designed to accurately reflect its real-world counterpart. In manufacturing, existing process data are often incomplete and do not qualify as a DT. However, with the help of specialized communication frameworks and cheaper, easier-to-use sensors, it is possible to integrate the existing manufacturing execution system (MES) and enterprise resource planning (ERP) data with the missing data gathered from the shop floor to create a comprehensive DT. In this paper, we present a digital shop floor decision support system (DSS) for non-linear aluminum manufacturing production. The system is split into five main components: digitization of shop floor orders; merging and sorting of MES, ERP, and shop floor data; custom and genetic optimization algorithms for the aging furnace production step; layout construction mechanism for optimal placement and stacking of orders in the furnace; and a user-friendly graphical user interface (GUI). The system’s performance was evaluated through three tests. The first test measured the efficiency of digitization, the second aimed to quantify time saved in finding packets in the hall, and the last test measured the impact of the optimizer on furnace productivity. The results revealed a 23.5% improvement in furnace capacity, but limitations were identified due to usability and human intervention.
{"title":"Dynamic project planning with digital twin","authors":"Silvan Zahno, J. Corre, Darko Petrovic, Gilles Mottiez, Loïc Fracheboud, Axel Amand, Steve Devènes, Gilbert Maître, F. Carrino","doi":"10.3389/fmtec.2023.1009633","DOIUrl":"https://doi.org/10.3389/fmtec.2023.1009633","url":null,"abstract":"The digital twin (DT) concept plays a crucial role in Industry 4.0 and the digitalization of manufacturing processes. A DT is a virtual representation of a physical object, system, or process, designed to accurately reflect its real-world counterpart. In manufacturing, existing process data are often incomplete and do not qualify as a DT. However, with the help of specialized communication frameworks and cheaper, easier-to-use sensors, it is possible to integrate the existing manufacturing execution system (MES) and enterprise resource planning (ERP) data with the missing data gathered from the shop floor to create a comprehensive DT. In this paper, we present a digital shop floor decision support system (DSS) for non-linear aluminum manufacturing production. The system is split into five main components: digitization of shop floor orders; merging and sorting of MES, ERP, and shop floor data; custom and genetic optimization algorithms for the aging furnace production step; layout construction mechanism for optimal placement and stacking of orders in the furnace; and a user-friendly graphical user interface (GUI). The system’s performance was evaluated through three tests. The first test measured the efficiency of digitization, the second aimed to quantify time saved in finding packets in the hall, and the last test measured the impact of the optimizer on furnace productivity. The results revealed a 23.5% improvement in furnace capacity, but limitations were identified due to usability and human intervention.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126900634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.3389/fmtec.2023.988073
N. Mathur, N. Last, Katherine C. Morris
The development of secondary sources as industrial feedstocks is important to creating resilient supply chains that contribute towards diverting resources away from landfills, mitigating deleterious environmental impacts, and minimizing market volatility. A major challenge to develop secondary feedstocks is the coordination and digitalization of the large quantities of generated information at each phase of a product’s life cycle. This paper builds upon earlier work that illustrates a top-level model of the activities and information needs to integrate product manufacturing with circular practices. This paper extends the initial work to explore the cyclical nature of Circular Economy (CE) information flows specifically related to product End-of-life. Using the Integrated Definition 0, IDEF0, modeling technique this paper examines the End-of-life function envisioned under a CE manufacturing model [ISO, 2012]. This function is decomposed into subsequent child functions and is analyzed relative to other product life cycle phases. The paper reviews the current global product EoL practices and in the context of the developed IDEF0 model. The proposed framework contributes a detailed description and presentation of information flows and the drivers of change (i.e., feedback loops) that are essential for creating secondary material streams based on the critically analyzing the reviewed literature. The novelty of this study includes the identification of standards and metrics gaps to facilitate quantitative assessment and evaluation in a CE. The study further elucidates the discussion around CE in terms of resource regeneration by ‘designing out waste’ and decoupling economic growth from resource depletion.
{"title":"A process model representation of the end-of-life phase of a product in a circular economy to identify standards needs","authors":"N. Mathur, N. Last, Katherine C. Morris","doi":"10.3389/fmtec.2023.988073","DOIUrl":"https://doi.org/10.3389/fmtec.2023.988073","url":null,"abstract":"The development of secondary sources as industrial feedstocks is important to creating resilient supply chains that contribute towards diverting resources away from landfills, mitigating deleterious environmental impacts, and minimizing market volatility. A major challenge to develop secondary feedstocks is the coordination and digitalization of the large quantities of generated information at each phase of a product’s life cycle. This paper builds upon earlier work that illustrates a top-level model of the activities and information needs to integrate product manufacturing with circular practices. This paper extends the initial work to explore the cyclical nature of Circular Economy (CE) information flows specifically related to product End-of-life. Using the Integrated Definition 0, IDEF0, modeling technique this paper examines the End-of-life function envisioned under a CE manufacturing model [ISO, 2012]. This function is decomposed into subsequent child functions and is analyzed relative to other product life cycle phases. The paper reviews the current global product EoL practices and in the context of the developed IDEF0 model. The proposed framework contributes a detailed description and presentation of information flows and the drivers of change (i.e., feedback loops) that are essential for creating secondary material streams based on the critically analyzing the reviewed literature. The novelty of this study includes the identification of standards and metrics gaps to facilitate quantitative assessment and evaluation in a CE. The study further elucidates the discussion around CE in terms of resource regeneration by ‘designing out waste’ and decoupling economic growth from resource depletion.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122167293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-12DOI: 10.3389/fmtec.2023.1155735
D. Mourtzis
Digital Twins, as a technological pillar of Industry 4.0, correspond to the virtual representation and bi-fold a real-time communication of a digital counterpart of a process or a physical object. As the industrial and manufacturing landscape is shifting towards Industry 5.0, huge investments focusing on enhancing interactions between Operators and Cyber-Physical Systems (CPS) occur. Yet, Metaverse strengthens these interactions as it enables human immersion into a virtual world. Furthermore, it examines the very promising relationships between the CPS, through the digital twins of these CPS. Therefore, this short review presents the concept of the Digital Twin inception in Industrial Metaverse. Additionally, a service-oriented digital twin architecture with Metaverse-enabled platforms for added value creation and interactions with CPS towards achieving Industry 5.0 challenges and beyond is proposed.
{"title":"Digital twin inception in the Era of industrial metaverse","authors":"D. Mourtzis","doi":"10.3389/fmtec.2023.1155735","DOIUrl":"https://doi.org/10.3389/fmtec.2023.1155735","url":null,"abstract":"Digital Twins, as a technological pillar of Industry 4.0, correspond to the virtual representation and bi-fold a real-time communication of a digital counterpart of a process or a physical object. As the industrial and manufacturing landscape is shifting towards Industry 5.0, huge investments focusing on enhancing interactions between Operators and Cyber-Physical Systems (CPS) occur. Yet, Metaverse strengthens these interactions as it enables human immersion into a virtual world. Furthermore, it examines the very promising relationships between the CPS, through the digital twins of these CPS. Therefore, this short review presents the concept of the Digital Twin inception in Industrial Metaverse. Additionally, a service-oriented digital twin architecture with Metaverse-enabled platforms for added value creation and interactions with CPS towards achieving Industry 5.0 challenges and beyond is proposed.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117323116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-31DOI: 10.3389/fmtec.2023.1072777
Ebru Turanoglu Bekar
Total Productive Maintenance (TPM) has been widely recognized as a strategic tool and lean manufacturing practice for improving manufacturing performance and sustainability, and therefore it has been successfully implemented in many organizations. The evaluation of TPM efficiency can assist companies in improving their operations across a variety of dimensions. This paper aims to propose a comprehensive and systematic framework for the evaluation of TPM performance. The proposed total productive maintenance performance measurement system (TPM PMS) is divided into four phases (e.g., design, evaluate, implement, and review): i) the design of new performance measures, ii) the evaluation of the new performance measures, iii) the implementation of the new performance measures to evaluate TPM performance, and iv) the reviewing of the TPM PMS. In the design phase, different types of performance measures impacting TPM are defined and analyzed by decision-makers. In the evaluation phase, novel performance measures are evaluated using the Fuzzy COmplex Proportional Assessment (FCOPRAS) method. In the implementation phase, a modified fuzzy data envelopment analysis (FDEA) is used to determine efficient and inefficient TPM performance with novel performance measures. In the review phase, TPM performance is periodically monitored, and the proposed TPM PMS is reviewed for successful implementation of TPM. A real-world case study from an international manufacturing company operating in the automotive industry is presented to demonstrate the applicability of the proposed TPM PMS. The main findings from the real-world case study showed that the proposed TPM PMS allows measuring TPM performance with different indicators especially soft ones, e.g., human-related, and supports decision makers by comparing the TPM performances of production lines and so prioritizing the most important preventive/predictive decisions and actions according to production lines, especially the ineffective ones in TPM program implementation. Therefore, this system can be considered a powerful monitoring tool and reliable evidence to make the implementation process of TPM more efficient in the real-world production environment.
{"title":"Efficiency measurement based on novel performance measures in total productive maintenance (TPM) using a fuzzy integrated COPRAS and DEA method","authors":"Ebru Turanoglu Bekar","doi":"10.3389/fmtec.2023.1072777","DOIUrl":"https://doi.org/10.3389/fmtec.2023.1072777","url":null,"abstract":"Total Productive Maintenance (TPM) has been widely recognized as a strategic tool and lean manufacturing practice for improving manufacturing performance and sustainability, and therefore it has been successfully implemented in many organizations. The evaluation of TPM efficiency can assist companies in improving their operations across a variety of dimensions. This paper aims to propose a comprehensive and systematic framework for the evaluation of TPM performance. The proposed total productive maintenance performance measurement system (TPM PMS) is divided into four phases (e.g., design, evaluate, implement, and review): i) the design of new performance measures, ii) the evaluation of the new performance measures, iii) the implementation of the new performance measures to evaluate TPM performance, and iv) the reviewing of the TPM PMS. In the design phase, different types of performance measures impacting TPM are defined and analyzed by decision-makers. In the evaluation phase, novel performance measures are evaluated using the Fuzzy COmplex Proportional Assessment (FCOPRAS) method. In the implementation phase, a modified fuzzy data envelopment analysis (FDEA) is used to determine efficient and inefficient TPM performance with novel performance measures. In the review phase, TPM performance is periodically monitored, and the proposed TPM PMS is reviewed for successful implementation of TPM. A real-world case study from an international manufacturing company operating in the automotive industry is presented to demonstrate the applicability of the proposed TPM PMS. The main findings from the real-world case study showed that the proposed TPM PMS allows measuring TPM performance with different indicators especially soft ones, e.g., human-related, and supports decision makers by comparing the TPM performances of production lines and so prioritizing the most important preventive/predictive decisions and actions according to production lines, especially the ineffective ones in TPM program implementation. Therefore, this system can be considered a powerful monitoring tool and reliable evidence to make the implementation process of TPM more efficient in the real-world production environment.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133719741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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