{"title":"Robust, Co-design Exploration of Multilevel Product, Material, and Manufacturing Process Systems","authors":"","doi":"10.1007/s40192-023-00324-4","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Achieving targeted product performance requires the integrated exploration of design spaces across multiple levels of decision-making in systems comprising products, materials, and manufacturing processes—product-material-manufacturing process (PMMP) systems. This demands the capability to co-design PMMP systems, that is, share ranged sets of design solutions among distributed product, material, and manufacturing process designers. PMMP systems are subject to uncertainties in processing, microstructure, and models employed. Facilitating co-design requires support for simultaneously exploring high-dimensional design spaces across multiple levels under uncertainty. In this paper, we present the Co-Design Exploration of Multilevel PMMP systems under Uncertainty (CoDE-MU) framework to facilitate the simultaneous exploration of high-dimensional design spaces across multiple levels under uncertainty. The CoDE-MU framework is a machine learning-enhanced, robust co-design exploration framework that integrates robust, coupled compromise Decision Support Problem (rc-cDSP) construct with interpretable Self-Organizing Maps (iSOM). The framework supports multidisciplinary designers to (i) understand the multilevel interactions, (ii) identify the process mechanisms that affect material and product responses, and (iii) provide decision support for problems involving many goals with different behaviors across multiple levels and uncertainty. We use an industry-inspired hot rod rolling (HRR) steel manufacturing process chain problem to showcase the CoDE-MU framework’s efficacy in facilitating the simultaneous exploration of the product, material, and manufacturing process design spaces across multiple levels under uncertainty. The framework is generic and facilitates the co-design of multilevel PMMP systems characterized by hierarchical product-material-manufacturing process relations and many goals with different behaviors that must be realized simultaneously at individual levels.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"16 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrating Materials and Manufacturing Innovation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s40192-023-00324-4","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Achieving targeted product performance requires the integrated exploration of design spaces across multiple levels of decision-making in systems comprising products, materials, and manufacturing processes—product-material-manufacturing process (PMMP) systems. This demands the capability to co-design PMMP systems, that is, share ranged sets of design solutions among distributed product, material, and manufacturing process designers. PMMP systems are subject to uncertainties in processing, microstructure, and models employed. Facilitating co-design requires support for simultaneously exploring high-dimensional design spaces across multiple levels under uncertainty. In this paper, we present the Co-Design Exploration of Multilevel PMMP systems under Uncertainty (CoDE-MU) framework to facilitate the simultaneous exploration of high-dimensional design spaces across multiple levels under uncertainty. The CoDE-MU framework is a machine learning-enhanced, robust co-design exploration framework that integrates robust, coupled compromise Decision Support Problem (rc-cDSP) construct with interpretable Self-Organizing Maps (iSOM). The framework supports multidisciplinary designers to (i) understand the multilevel interactions, (ii) identify the process mechanisms that affect material and product responses, and (iii) provide decision support for problems involving many goals with different behaviors across multiple levels and uncertainty. We use an industry-inspired hot rod rolling (HRR) steel manufacturing process chain problem to showcase the CoDE-MU framework’s efficacy in facilitating the simultaneous exploration of the product, material, and manufacturing process design spaces across multiple levels under uncertainty. The framework is generic and facilitates the co-design of multilevel PMMP systems characterized by hierarchical product-material-manufacturing process relations and many goals with different behaviors that must be realized simultaneously at individual levels.
期刊介绍：
The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.
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