Pub Date : 2023-10-13DOI: 10.1007/s11740-023-01227-1
Werner Homberg, Bahman Arian, Viktor Arne, Thomas Borgert, Alexander Brosius, Peter Groche, Christoph Hartmann, Lukas Kersting, Robert Laue, Juri Martschin, Thomas Meurer, Daniel Spies, A. Erman Tekkaya, Ansgar Trächtler, Wolfram Volk, Frank Wendler, Malte Wrobel
Abstract The constantly increasing challenges of production technology for the economic and resource-saving production of metallic workpieces require, among other things, the optimisation of existing processes. Forming technology, which is confronted with new challenges regarding the quality of the workpieces, must also organise the individual processes more efficiently and at the same time more reliably in order to be able to guarantee good workpiece quality and at the same time to be able to produce economically. One way to meet these challenges is to carry out the forming processes in closed-loop control systems using softsensors. Despite the many potential applications of softsensors in the field of forming technology, there is still no definition of the term softsensor. This publication therefore proposes a definition of the softsensor based on the definition of a sensor and the distinction from the observer, which on the one hand is intended to stimulate scientific discourse and on the other hand is also intended to form the basis for further scientific work. Based on this definition, a wide variety of highly topical application examples of various softsensors in the field of forming technology are given.
{"title":"Softsensors: key component of property control in forming technology","authors":"Werner Homberg, Bahman Arian, Viktor Arne, Thomas Borgert, Alexander Brosius, Peter Groche, Christoph Hartmann, Lukas Kersting, Robert Laue, Juri Martschin, Thomas Meurer, Daniel Spies, A. Erman Tekkaya, Ansgar Trächtler, Wolfram Volk, Frank Wendler, Malte Wrobel","doi":"10.1007/s11740-023-01227-1","DOIUrl":"https://doi.org/10.1007/s11740-023-01227-1","url":null,"abstract":"Abstract The constantly increasing challenges of production technology for the economic and resource-saving production of metallic workpieces require, among other things, the optimisation of existing processes. Forming technology, which is confronted with new challenges regarding the quality of the workpieces, must also organise the individual processes more efficiently and at the same time more reliably in order to be able to guarantee good workpiece quality and at the same time to be able to produce economically. One way to meet these challenges is to carry out the forming processes in closed-loop control systems using softsensors. Despite the many potential applications of softsensors in the field of forming technology, there is still no definition of the term softsensor. This publication therefore proposes a definition of the softsensor based on the definition of a sensor and the distinction from the observer, which on the one hand is intended to stimulate scientific discourse and on the other hand is also intended to form the basis for further scientific work. Based on this definition, a wide variety of highly topical application examples of various softsensors in the field of forming technology are given.","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858227","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 : 2023-10-07DOI: 10.1007/s11740-023-01223-5
Tabea Marie Demke, Nicole Emminghaus, Ludger Overmeyer, Stefan Kaierle, Christian Klose, Susanne Elisabeth Thürer, Berend Denkena, Benjamin Bergmann, Florian Schaper, Peter Nyhuis, Vivian Katharina Kuprat
Abstract In early innovation phases, the monetary evaluation of process innovations is a challenge for companies due to a lack of data. However, an innovation evaluation is essential in an early innovation phase to ensure that process innovations deliver economic value added (EVA) in early innovation phases and to channel technology transfer expenditures in a goal-oriented manner. This paper presents an approach for a semi-quantitative procedure for the monetary evaluation of process innovations in the early innovation phase focusing on manufacturing and material costs. Exemplarily, the approach is applied to process innovations of the Collaborative Research Center 1368 on oxygen-free production. In order to ensure the net present value orientation within the innovation evaluation, the procedure developed is based on a driver tree of the EVA. To link value drivers of the EVA and innovation-driven factors influencing EVA, the EVA driver tree is further systematized with a focus on manufacturing and material costs using a literature-based impact model. Based on the last level of the impact model, a guideline for a semi-structured expert interview is developed. Using this interview guideline, data is collected in the form of innovation-driven influencing factors, which represent the input for the final monetary innovation evaluation. An adapted weighted scoring model is used to draw a semi-quantitative conclusion regarding the EVA achieved by the process innovation. The practical application of the approach developed to process innovations in oxygen-free production has shown that, in the context of three process innovations under consideration, their implementation with the aim of achieving an EVA through reduced manufacturing and material costs at the current innovation status is not effective. However, based on the impact model developed, corresponding levers can be identified to positively influence the EVA and thus also the industrialization of the process innovation. Finally, further necessary steps are identified to evolve the presented approach into a complete method for monetary innovation evaluation in early innovation phases.
{"title":"Approach for the monetary evaluation of process innovations in early innovation phases focusing on manufacturing and material costs","authors":"Tabea Marie Demke, Nicole Emminghaus, Ludger Overmeyer, Stefan Kaierle, Christian Klose, Susanne Elisabeth Thürer, Berend Denkena, Benjamin Bergmann, Florian Schaper, Peter Nyhuis, Vivian Katharina Kuprat","doi":"10.1007/s11740-023-01223-5","DOIUrl":"https://doi.org/10.1007/s11740-023-01223-5","url":null,"abstract":"Abstract In early innovation phases, the monetary evaluation of process innovations is a challenge for companies due to a lack of data. However, an innovation evaluation is essential in an early innovation phase to ensure that process innovations deliver economic value added (EVA) in early innovation phases and to channel technology transfer expenditures in a goal-oriented manner. This paper presents an approach for a semi-quantitative procedure for the monetary evaluation of process innovations in the early innovation phase focusing on manufacturing and material costs. Exemplarily, the approach is applied to process innovations of the Collaborative Research Center 1368 on oxygen-free production. In order to ensure the net present value orientation within the innovation evaluation, the procedure developed is based on a driver tree of the EVA. To link value drivers of the EVA and innovation-driven factors influencing EVA, the EVA driver tree is further systematized with a focus on manufacturing and material costs using a literature-based impact model. Based on the last level of the impact model, a guideline for a semi-structured expert interview is developed. Using this interview guideline, data is collected in the form of innovation-driven influencing factors, which represent the input for the final monetary innovation evaluation. An adapted weighted scoring model is used to draw a semi-quantitative conclusion regarding the EVA achieved by the process innovation. The practical application of the approach developed to process innovations in oxygen-free production has shown that, in the context of three process innovations under consideration, their implementation with the aim of achieving an EVA through reduced manufacturing and material costs at the current innovation status is not effective. However, based on the impact model developed, corresponding levers can be identified to positively influence the EVA and thus also the industrialization of the process innovation. Finally, further necessary steps are identified to evolve the presented approach into a complete method for monetary innovation evaluation in early innovation phases.","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135253562","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 : 2023-10-07DOI: 10.1007/s11740-023-01228-0
Adauto Lucas da Silva, Antonio Carlos Pacagnella Junior, Paulo Sérgio de Arruda Ignácio, Alessandro Lucas da Silva
{"title":"Correction: An adaptive and integrated reference model for supplier selection: application to product development and serialized component supply","authors":"Adauto Lucas da Silva, Antonio Carlos Pacagnella Junior, Paulo Sérgio de Arruda Ignácio, Alessandro Lucas da Silva","doi":"10.1007/s11740-023-01228-0","DOIUrl":"https://doi.org/10.1007/s11740-023-01228-0","url":null,"abstract":"","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135254749","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}
Abstract One of the main paradigms of Industry 5.0 is represented by human-robot collaboration (HRC), which aims to support humans in production processes. However, working entire shifts in close contact with a robotic system may introduce new hazards from a cognitive ergonomics perspective. This paper presents a methodological approach to monitor the evolution of the operator’s psychophysical state noninvasively in shifts of a repetitive assembly process, focusing on stress, mental workload, and fatigue. Through the use of non-invasive biosensors, it is possible to obtain objective information, even in real time, on the operator’s cognitive load and stress in a naturalistic manner (i.e., without interrupting or hindering the process). In the HRC setting, recognition of the operator’s psychophysical state is the first step in supporting his or her well-being and can provide clues to improve collaboration. The proposed method was applied to a case study aimed at comparing shifts performed both manually and with a cobot of a repetitive assembly process. The results showed significant differences in terms of process performance evolution and psychophysical state of the operator. In particular, the presence of the cobot resulted in fewer process failures, stress and cognitive load especially in the first phase of the work shift. The case study analyzed also showed the adequacy of noninvasively collected physiological data in providing important information on the evolution of the operator’s stress, cognitive load, and fatigue.
{"title":"Analyzing psychophysical state and cognitive performance in human-robot collaboration for repetitive assembly processes","authors":"Riccardo Gervasi, Matteo Capponi, Luca Mastrogiacomo, Fiorenzo Franceschini","doi":"10.1007/s11740-023-01230-6","DOIUrl":"https://doi.org/10.1007/s11740-023-01230-6","url":null,"abstract":"Abstract One of the main paradigms of Industry 5.0 is represented by human-robot collaboration (HRC), which aims to support humans in production processes. However, working entire shifts in close contact with a robotic system may introduce new hazards from a cognitive ergonomics perspective. This paper presents a methodological approach to monitor the evolution of the operator’s psychophysical state noninvasively in shifts of a repetitive assembly process, focusing on stress, mental workload, and fatigue. Through the use of non-invasive biosensors, it is possible to obtain objective information, even in real time, on the operator’s cognitive load and stress in a naturalistic manner (i.e., without interrupting or hindering the process). In the HRC setting, recognition of the operator’s psychophysical state is the first step in supporting his or her well-being and can provide clues to improve collaboration. The proposed method was applied to a case study aimed at comparing shifts performed both manually and with a cobot of a repetitive assembly process. The results showed significant differences in terms of process performance evolution and psychophysical state of the operator. In particular, the presence of the cobot resulted in fewer process failures, stress and cognitive load especially in the first phase of the work shift. The case study analyzed also showed the adequacy of noninvasively collected physiological data in providing important information on the evolution of the operator’s stress, cognitive load, and fatigue.","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350639","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 : 2023-09-29DOI: 10.1007/s11740-023-01225-3
Martin Benfer, Valentin Heyer, Oliver Brützel, Christoph Liebrecht, Sina Peukert, Gisela Lanza
Abstract Increasing demand for individualised products has led to the concept of mass customisation, combining high product variety with production efficiency coming along with mass production. Companies are moving to matrix production systems with complex product flows for mass customisation. One challenge in such systems is the determination of optimal system configurations to fulfil future demands while minimising production costs. An approach to determine the ideal configuration is to use metaheuristics like genetic algorithms or simulated annealing to optimise simulation models. However, it is unclear which methods are ideally suited to finding the best solutions. This contribution compares the performance of genetic algorithms and simulated annealing when optimising the configuration of a company-specific matrix production system using discrete event simulation. The methods are evaluated using different objective functions. For the genetic algorithm, different observation strategies are also tested. Overall, the simulated annealing approach delivers better results with shorter solution times. The contributing factors leading to the different results are discussed, and areas for future research are pointed out.
{"title":"Analysis of metaheuristic optimisation techniques for simulated matrix production systems","authors":"Martin Benfer, Valentin Heyer, Oliver Brützel, Christoph Liebrecht, Sina Peukert, Gisela Lanza","doi":"10.1007/s11740-023-01225-3","DOIUrl":"https://doi.org/10.1007/s11740-023-01225-3","url":null,"abstract":"Abstract Increasing demand for individualised products has led to the concept of mass customisation, combining high product variety with production efficiency coming along with mass production. Companies are moving to matrix production systems with complex product flows for mass customisation. One challenge in such systems is the determination of optimal system configurations to fulfil future demands while minimising production costs. An approach to determine the ideal configuration is to use metaheuristics like genetic algorithms or simulated annealing to optimise simulation models. However, it is unclear which methods are ideally suited to finding the best solutions. This contribution compares the performance of genetic algorithms and simulated annealing when optimising the configuration of a company-specific matrix production system using discrete event simulation. The methods are evaluated using different objective functions. For the genetic algorithm, different observation strategies are also tested. Overall, the simulated annealing approach delivers better results with shorter solution times. The contributing factors leading to the different results are discussed, and areas for future research are pointed out.","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193816","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 : 2020-04-30DOI: 10.1007/s11740-020-00961-0
B. Denkena, A. Abrão, A. Krödel, K. Meyer
{"title":"Analytic roughness prediction by deep rolling","authors":"B. Denkena, A. Abrão, A. Krödel, K. Meyer","doi":"10.1007/s11740-020-00961-0","DOIUrl":"https://doi.org/10.1007/s11740-020-00961-0","url":null,"abstract":"","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141209117","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 : 2020-04-05DOI: 10.1007/s11740-020-00959-8
B. Denkena, M. Dittrich, S. Wilmsmeier, S. Stamm
{"title":"Optimization of delivery adherence based on capacity planning and bid pricing","authors":"B. Denkena, M. Dittrich, S. Wilmsmeier, S. Stamm","doi":"10.1007/s11740-020-00959-8","DOIUrl":"https://doi.org/10.1007/s11740-020-00959-8","url":null,"abstract":"","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141216364","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 : 2020-03-18DOI: 10.1007/s11740-020-00957-w
B. Mihoubi, B. Bouzouia, K. Tebani, Mehdi Gaham
{"title":"Hardware in the loop simulation for product driven control of a cyber-physical manufacturing system","authors":"B. Mihoubi, B. Bouzouia, K. Tebani, Mehdi Gaham","doi":"10.1007/s11740-020-00957-w","DOIUrl":"https://doi.org/10.1007/s11740-020-00957-w","url":null,"abstract":"","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141221990","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 : 2020-03-02DOI: 10.1007/s11740-020-00956-x
T. Heutmann, Alexander W. Tils, R. H. Schmitt
{"title":"Quantifying disturbance risks on the process time for a robust, synchronized individual production","authors":"T. Heutmann, Alexander W. Tils, R. H. Schmitt","doi":"10.1007/s11740-020-00956-x","DOIUrl":"https://doi.org/10.1007/s11740-020-00956-x","url":null,"abstract":"","PeriodicalId":20626,"journal":{"name":"Production Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141225431","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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