Pub Date : 2023-09-11DOI: 10.1080/24725854.2023.2257245
Ragnar Eggertsson, Rob Basten, Geert-Jan van Houtum
–We study the problem of inspection and maintenance planning of capital goods based on observations of the capital good’s degradation state. However, the observations are imprecise, and their quality depends on the environment. For example, when performing maintenance for heating, ventilation, and air-conditioning units (HVACs) in trains, the health of the cooling component of an HVAC can be assessed from temperature readouts of the car in which the HVAC is mounted. Temperature information is useful in the summer when high car temperatures can indicate a failed cooling component, but this information has limited value during the winter. We model the problem as a partially observable Markov decision process with a fully observed environment. We analytically show that an environment-dependent monotonic at-most-4-region policy is optimal. Furthermore, we numerically analyze an example motivated by HVAC maintenance at Dutch Railways. This analysis shows that, in many cases, including the environment in the model can lead to cost savings of more than 10%. In a broad numerical experiment, we show that a simple policy cannot always substitute an optimal policy.
{"title":"Maintenance optimization for capital goods when information is incomplete and environment-dependent","authors":"Ragnar Eggertsson, Rob Basten, Geert-Jan van Houtum","doi":"10.1080/24725854.2023.2257245","DOIUrl":"https://doi.org/10.1080/24725854.2023.2257245","url":null,"abstract":"–We study the problem of inspection and maintenance planning of capital goods based on observations of the capital good’s degradation state. However, the observations are imprecise, and their quality depends on the environment. For example, when performing maintenance for heating, ventilation, and air-conditioning units (HVACs) in trains, the health of the cooling component of an HVAC can be assessed from temperature readouts of the car in which the HVAC is mounted. Temperature information is useful in the summer when high car temperatures can indicate a failed cooling component, but this information has limited value during the winter. We model the problem as a partially observable Markov decision process with a fully observed environment. We analytically show that an environment-dependent monotonic at-most-4-region policy is optimal. Furthermore, we numerically analyze an example motivated by HVAC maintenance at Dutch Railways. This analysis shows that, in many cases, including the environment in the model can lead to cost savings of more than 10%. In a broad numerical experiment, we show that a simple policy cannot always substitute an optimal policy.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135938019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/24725854.2023.2257255
Fabio Caltanissetta, Luisa Bertoli, Bianca Maria Colosimo
AbstractThe amount of attention paid to in-situ monitoring in Additive Manufacturing (AM) has significantly increased over the last few years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images, and videos. In-situ quality monitoring represents an opportunity for waste reduction and first-time-right production via inline detection of process flaws, which allows early identification of scraps and the possibility of correcting actions for first-time-right production. This article presents a solution for in-situ monitoring of images taken layerwise in material extrusion AM. Compared with the existing solutions, mainly focusing on monitoring the shape deviation observed at each layer with respect to the nominal shape, this article focuses on monitoring the internal surface texture with the aim of detecting over- and under-extrusion flaws. Inspired by an approach reported in the literature that was developed for textile image monitoring, we propose a solution for in-situ monitoring of textured surfaces which is based on combining Random Forests with clustering to automatically identify defective locations layerwise. A real case study based on Fused Filament Fabrication is used to compare the performance of the novel proposed solution with the original one and identify an appropriate direction for future research.Keywords: Statistical quality monitoringin-situ monitoringimagerandom forestsclusteringadditive manufacturing Data availabilityThe data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.24042891.v1.Additional informationFundingThe present research was partially funded by ACCORDO Quadro ASI-POLIMI “Attività di Ricerca e Innovazione” n. 2018-5-HH.0, collaboration agreement between the Italian Space Agency and Politecnico di Milano.Notes on contributorsFabio CaltanissettaFabio Caltanissetta received his doctoral degree in industrial engineering from Politecnico di Milano (while completing this research work), after completing an MSc in industrial engineering at the same university. He is currently a Process R&D Specialist at Caracol AM.Luisa BertoliLaura Bertoli completed a Master of Science in industrial engineering at Politecnico di Milano, Italy (while completing this research work). She is currently a business data product specialist at UniCredit.Bianca Maria ColosimoBianca Maria Colosimo is a professor in the Department of Mechanical Engineering of Politecnico di Milano. Her research interest is mainly in the area of big data mining for Industry 4.0, with special focus on advanced manufacturing. She is currently a department editor of IISE Transactions, senior editor of Informs Journal of Data Science, associate editor of Progress in Additive Manufacturing and Additive Manufacturing Letters. She has been editor-in-chief of the Journal of Quality Technology (2018-2021). She is included among the top 100 Italian woman scien
摘要在过去几年中，对增材制造(AM)现场监测的关注程度显著增加，为通过信号、图像和视频的大数据分析进行质量监测和控制的范式转变铺平了道路。通过在线检测工艺缺陷，现场质量监测为减少浪费和第一次正确生产提供了机会，这可以早期识别废料，并为第一次正确生产提供纠正措施的可能性。本文提出了一种材料挤压增材制造分层图像的现场监测方案。与现有的解决方案主要关注于监测每层观察到的形状相对于标称形状的偏差相比，本文主要关注于监测内部表面纹理，目的是检测过度和欠挤压缺陷。受文献报道的纺织品图像监测方法的启发，我们提出了一种基于随机森林和聚类相结合的纹理表面原位监测解决方案，以分层自动识别缺陷位置。通过一个基于熔丝制造的实际案例研究，比较了新提出的解决方案与原始解决方案的性能，并确定了未来研究的合适方向。关键词:统计质量监测原位监测图像随机森林聚类增材制造数据可用性支持本研究结果的数据可公开获取，共享网址:https://doi.org/10.6084/m9.figshare.24042891.v1.Additional information资助本研究部分由ACCORDO Quadro ASI-POLIMI“atitivitondi Ricerca e Innovazione”资助，2018-5-HH。意大利航天局与米兰理工大学之间的合作协议。fabio Caltanissetta在米兰理工大学获得工业工程硕士学位后，获得了工业工程博士学位(同时完成了这项研究工作)。他目前是Caracol AM的工艺研发专家。Luisa BertoliLaura Bertoli在意大利米兰理工大学(Politecnico di Milano)获得工业工程硕士学位(同时完成了这项研究工作)。她目前是UniCredit的商业数据产品专家。Bianca Maria Colosimo是米兰理工大学机械工程系的教授。主要研究方向为面向工业4.0的大数据挖掘，重点关注先进制造业。她目前是IISE Transactions的部门编辑，Informs Journal of Data Science的高级编辑，《增材制造进展》和《增材制造快报》的副主编。她曾担任Journal of Quality Technology(2018-2021)主编。她被列入意大利STEM领域前100名女科学家之一
{"title":"In-situ monitoring of image texturing via random forests and clustering with applications to additive manufacturing","authors":"Fabio Caltanissetta, Luisa Bertoli, Bianca Maria Colosimo","doi":"10.1080/24725854.2023.2257255","DOIUrl":"https://doi.org/10.1080/24725854.2023.2257255","url":null,"abstract":"AbstractThe amount of attention paid to in-situ monitoring in Additive Manufacturing (AM) has significantly increased over the last few years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images, and videos. In-situ quality monitoring represents an opportunity for waste reduction and first-time-right production via inline detection of process flaws, which allows early identification of scraps and the possibility of correcting actions for first-time-right production. This article presents a solution for in-situ monitoring of images taken layerwise in material extrusion AM. Compared with the existing solutions, mainly focusing on monitoring the shape deviation observed at each layer with respect to the nominal shape, this article focuses on monitoring the internal surface texture with the aim of detecting over- and under-extrusion flaws. Inspired by an approach reported in the literature that was developed for textile image monitoring, we propose a solution for in-situ monitoring of textured surfaces which is based on combining Random Forests with clustering to automatically identify defective locations layerwise. A real case study based on Fused Filament Fabrication is used to compare the performance of the novel proposed solution with the original one and identify an appropriate direction for future research.Keywords: Statistical quality monitoringin-situ monitoringimagerandom forestsclusteringadditive manufacturing Data availabilityThe data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.24042891.v1.Additional informationFundingThe present research was partially funded by ACCORDO Quadro ASI-POLIMI “Attività di Ricerca e Innovazione” n. 2018-5-HH.0, collaboration agreement between the Italian Space Agency and Politecnico di Milano.Notes on contributorsFabio CaltanissettaFabio Caltanissetta received his doctoral degree in industrial engineering from Politecnico di Milano (while completing this research work), after completing an MSc in industrial engineering at the same university. He is currently a Process R&D Specialist at Caracol AM.Luisa BertoliLaura Bertoli completed a Master of Science in industrial engineering at Politecnico di Milano, Italy (while completing this research work). She is currently a business data product specialist at UniCredit.Bianca Maria ColosimoBianca Maria Colosimo is a professor in the Department of Mechanical Engineering of Politecnico di Milano. Her research interest is mainly in the area of big data mining for Industry 4.0, with special focus on advanced manufacturing. She is currently a department editor of IISE Transactions, senior editor of Informs Journal of Data Science, associate editor of Progress in Additive Manufacturing and Additive Manufacturing Letters. She has been editor-in-chief of the Journal of Quality Technology (2018-2021). She is included among the top 100 Italian woman scien","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135983331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1080/24725854.2023.2255643
E. C. Barbee, Aaron Ferber, Lucas Lafferty, B. Keskin, B. Dilkina, Meredith Gore
{"title":"Interdiction of Wildlife Trafficking Supply Chains: An Analytical Approach1","authors":"E. C. Barbee, Aaron Ferber, Lucas Lafferty, B. Keskin, B. Dilkina, Meredith Gore","doi":"10.1080/24725854.2023.2255643","DOIUrl":"https://doi.org/10.1080/24725854.2023.2255643","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48377994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.1080/24725854.2023.2253869
B. Tan, A. Matta
{"title":"The Digital Twin Synchronization Problem: Framework, Formulations, and Analysis","authors":"B. Tan, A. Matta","doi":"10.1080/24725854.2023.2253869","DOIUrl":"https://doi.org/10.1080/24725854.2023.2253869","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46236291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-29DOI: 10.1080/24725854.2023.2253294
Geoffrey A. Chua, Yan Feng, Juan Ramon L. Senga, S. Viswanathan
{"title":"An Efficient Approximation to the Pull Policy for Hybrid Manufacturing and Remanufacturing Systems with Setup Costs","authors":"Geoffrey A. Chua, Yan Feng, Juan Ramon L. Senga, S. Viswanathan","doi":"10.1080/24725854.2023.2253294","DOIUrl":"https://doi.org/10.1080/24725854.2023.2253294","url":null,"abstract":"","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47395032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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