Frontiers in Manufacturing Technology最新文献

{"title":"A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries","authors":"D. Cameron, A. Waaler, Erlend Fjøsna, Monica Hole, F. Psarommatis","doi":"10.3389/fmtec.2022.945717","DOIUrl":"https://doi.org/10.3389/fmtec.2022.945717","url":null,"abstract":"The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122134594","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}

{"title":"Evolution of PEKK crystallization measured in laser sintering","authors":"L. Benedetti, B. Brûlé, N. Decraemer, K. Evans, O. Ghita","doi":"10.3389/fmtec.2022.964450","DOIUrl":"https://doi.org/10.3389/fmtec.2022.964450","url":null,"abstract":"The rising popularity of laser sintering (LS) technology has increased by the broadening of available materials for this process. Kepstan 6002 poly (ether ketone ketone) (PEKK) was recently launched as a high-performance polymer grade with a lower processing temperature and unique crystallization kinetics. This study aims to understand the progress of crystallization on samples manufactured throughout the laser sintering process. These results were compared with isothermal and dynamic differential scanning calorimetry (DSC) experiments with different cooling rates. Kepstan 6002 PEKK processed by high-temperature laser sintering (HT-LS) presents a kinetics of crystallization in the order of ∼10 times slower than its crystallized samples in the DSC. This result highlights the need for a part-based crystallization investigation rather than isothermal models to describe the crystallization in LS. The transmission electron microscopy (TEM) analysis reveals smaller spherulites in the samples subjected to prolonged cooling times and an almost amorphous structure for the PEKK samples exposed to almost no cooling. This experiment identified the surroundings of laser sintered particles as preferential sites for crystallization initiation, which grows as the particles penetrate the molten layers and spherulites are formed. The slower kinetics of crystallization of Kepstan 6002 PEKK grade improve the adhesion between layers in laser sintering and enable tailoring its properties according to the application. Understanding the relationship between intrinsic material characteristics and the resulting final properties is vital to optimizing the process and controlling the final performance of PEKK for different applications.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126169406","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}

{"title":"Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study","authors":"G. Moustris, G. Kouzas, Spyros Fourakis, Georgios Fiotakis, Apostolos Chondronasios, Abd Al Rahman M. Abu Ebayyeh, Alireza Mousavi, Kostas Apostolou, J. Milenkovic, Zoi Chatzichristodoulou, E. Beckert, J. Butet, S. Blaser, O. Landry, A. Müller","doi":"10.3389/fmtec.2022.946452","DOIUrl":"https://doi.org/10.3389/fmtec.2022.946452","url":null,"abstract":"This paper presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects. The system provides two image-based defect detection pipelines. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. At the last step of the process, a Decision Support System (DSS) collects all information, processes it and labels it with additional defect type categories, in order to provide recommendations to the optoelectronical engineer. The proposed solution has been implemented on a real industrial use-case in laser manufacturing. Analysis shows that chips validated through the proposed process have a probability to lase at a specific frequency six times higher than the fully rejected ones.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133309204","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}

{"title":"Discriminating different materials by means of vibrations","authors":"Tommaso Lisini Baldi, S. Marullo, N. D’Aurizio, D. Prattichizzo","doi":"10.3389/fmtec.2022.939755","DOIUrl":"https://doi.org/10.3389/fmtec.2022.939755","url":null,"abstract":"Material characterization and discrimination is of interest for multiple applications, ranging from mechanical engineering to medical and industrial sectors. Despite the need for automated systems, the majority of the existing approaches necessitate expensive and bulky hardware that cannot be used outside ad-hoc laboratories. In this work, we propose a novel technique for discriminating between different materials and detecting intra-material variations using active stimulation through vibration and machine learning techniques. A voice-coil actuator and a tri-axial accelerometer are used for generating and sampling mechanical vibration propagated through the materials. Results of the present analysis confirm the effectiveness of the proposed approach. Processing a mechanical vibration signal that propagates through a material by means of a neural network is a viable means for material classification. This holds not only for distinguishing materials having gross differences, but also for detecting whether a material underwent some slight changes in its structure. In addition, mechanical vibrations at 500 Hz demonstrated an ability to provide a compact and meaningful representation of the data, sufficient to categorize 8 different materials, and to distinguish reference materials from other defective materials, with an average accuracy greater than 90%.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706039","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}

{"title":"Anomaly detection as vision-based obstacle detection for vehicle automation in industrial environment","authors":"Marius Wenning, T. Adlon, P. Burggräf","doi":"10.3389/fmtec.2022.918343","DOIUrl":"https://doi.org/10.3389/fmtec.2022.918343","url":null,"abstract":"Nowadays, produced cars are equipped with mechatronical actuators as well as with a wide range of sensors in order to realize driver assistance functions. These components could enable cars’ automation at low speeds on company premises, although autonomous driving in public traffic is still facing technical and legal challenges. For automating vehicles in an industrial environment a reliable obstacle detection system is required. State-of-the-art solution for protective devices in Automated Guided Vehicles is the distance measuring laser scanner. Since laser scanners are not basic equipment of today’s cars in contrast to monocameras mounted behind the windscreen, we develop a computer vision algorithm that is able to detect obstacles in camera images reliably. Therefore, we make use of our well-known operational design domain by teaching an anomaly detection how the vehicle path should look like. The result is an anomaly detection algorithm that consists of a pre-trained feature extractor and a shallow classifier, modelling the probability of occurrence. We record a data set of a real industrial environment and show a robust classifier after training the algorithm with images of only one run. The performance as an obstacle detection is on par with a semantic segmentation, but requires a fraction of the training data and no labeling.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574602","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}

{"title":"Modeling Energy Consumption Using Machine Learning","authors":"Sai Aravind Sarswatula, Tanna Pugh, V. Prabhu","doi":"10.3389/fmtec.2022.855208","DOIUrl":"https://doi.org/10.3389/fmtec.2022.855208","url":null,"abstract":"Electrical, metal, plastic, and food manufacturing are among the major energy-consuming industries in the U.S. Since 1981, the U.S. Department of Energy Industrial Assessments Centers (IACs) have conducted audits to track and analyze energy data across several industries and provided recommendations for improving energy efficiency. In this article, we used statistical and machine learning techniques to draw insights from this IAC dataset with over 15,000 samples collected from 1981 to 2013. We developed predictive models for energy consumption using machine learning techniques such as Multiple Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Extreme Gradient Boost Regressor. We also developed classifier models using Support Vector Machines, Random Forest, K-Nearest Neighbor (KNN), and deep learning. Results using this data set indicate that Random Forest Regressor is the best prediction technique with an R 2 of 0.869, and the Random Forest classifier is the best technique with precision, recall, F1 score, and accuracy of 0.818, 0.884, 0.844, and 0.883, respectively. Deep learning also performed competitively with an accuracy of about 0.88 in training and testing after 10 epochs. The machine learning models could be useful in benchmarking the energy consumption of factories and identifying opportunities to improve energy efficiency.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293827","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}

{"title":"Advances in Adaptive Scheduling in Industry 4.0","authors":"D. Mourtzis","doi":"10.3389/fmtec.2022.937889","DOIUrl":"https://doi.org/10.3389/fmtec.2022.937889","url":null,"abstract":"The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129395593","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}

{"title":"Identifying and Assessing the Required I4.0 Skills for Manufacturing Companies’ Workforce","authors":"F. Acerbi, M. Rossi, S. Terzi","doi":"10.3389/fmtec.2022.921445","DOIUrl":"https://doi.org/10.3389/fmtec.2022.921445","url":null,"abstract":"Nowadays, the diffusion of digital and industry 4.0 (I4.0) technologies is affecting the manufacturing sector with a twofold effect. While on one side it represents the boost fastening the competitive advantage of companies, on the other hand it is often accompanied by several challenges that companies need to face. Among all, companies are required to invest in technologies to empower their production activities on the shopfloor without lagging behind their workforce in order to undertake a linear, aware, and structured path toward digitization. The extant literature presents some research conducted to support companies toward digitization, and they usually rely on maturity models in this intention. Nevertheless, few studies included the assessment of workforce skills and competencies in the overall assessment, and in this case, they provide a high level perspective of the investigation, mainly based on check lists which may limit the objectivity of the assessment, and usually they do not customize the assessment based on companies’ requirements. Therefore, considering the importance to balance investments in technologies with those in the workforce to move toward the same direction, this contribution aims to develop a structured, customizable, and objective skill assessment model. With this intention, it has been first clarified the set of job profiles required in I4.0, together with the needed related skills based on the extant literature findings; second, it has been identified the set of key criteria to be considered while performing the assessment of the workforce; third, it has been defined the method to be integrated in the maturity model to enable the initial setting of the weights of the criteria identified according to the company needs; and fourth, based on these findings, it has been developed the assessment model. The developed model facilitates the elaboration of the proper workforce improvement plans to be put in practice to support the improvement of the skills of the whole workforce based on company’s needs.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114304978","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}

{"title":"New Trends in Aviation and Medical Technology Enabled by Additive Manufacturing","authors":"M. Wegner, Tobias S. Hartwich, E. Heyden, L. Schwan, J. Schwenke, N. Wortmann, D. Krause","doi":"10.3389/fmtec.2022.919738","DOIUrl":"https://doi.org/10.3389/fmtec.2022.919738","url":null,"abstract":"In this publication, the potentials of additive manufacturing in the field of sustainability and individualization for aviation and medical technology are presented. Design approaches for each application field as well as examples in the fields are shown. In the field of aviation, structures can be manufactured so that they are load path optimized. This has a great lightweight potential and results in a low resource consumption. The examples contain the design of an aircraft cabin partition using the Direct Energy Deposition process and the optimization of load introduction points directly integrated into the sandwich core. Furthermore, in medical technology, additive manufacturing can be used to produce patient-specific models based on original medical imaging data, which can be used for training of medical treatments, quality assurance or for the validation of new developed medical devices. As examples a stroke simulation model containing a modular aortic model as well as functional stenose models are shown. Furthermore, the use of AM molds to generate a deformable bladder shell and a prostate phantom are described.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128810758","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}

{"title":"A Review on the Advanced Maintenance Approach for Achieving the Zero-Defect Manufacturing System","authors":"H. Jun","doi":"10.3389/fmtec.2022.920900","DOIUrl":"https://doi.org/10.3389/fmtec.2022.920900","url":null,"abstract":"Recently, a revolutionary change is taking place in manufacturing and production systems thanks to the development of various advanced technologies such as IIoT (Industrial Internet of Things), CPPS (Cyber-Physical Production System), digital twins, big data analytics, AI (Artificial Intelligence), and so on. One of the change is that manufacturing and production systems are now trying to transform into the ZDM (Zero-Defect Manufacturing) system. For a manufacturing company, quality takes precedence over any other competitive factors, so the implementation of a ZDM system is very important. For the implementation of ZDM, many fundamental technologies are required. Among them, the advanced maintenance approach for the facilities/equipment of the manufacturing and production system is much more important because it could support the zero-defect and high-efficiency operation of manufacturing and production systems. The advanced maintenance approach, which is often called by various terms such as predictive maintenance, condition-based maintenance plus (CBM+), and PHM (Prognostics and Health Management), requires various interdisciplinary knowledge and systematic integration. In this study, we will review previous works mainly focusing on advanced maintenance subject among ZDM research works, and briefly discuss the challenging issues for applying PHM technologies to the ZDM.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127767568","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}