[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121639-en":3,"doc-seo-121639-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121639,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability","Sustainable processes in materials science and manufacturing require environmentally friendly decision-making. This work advances AI-based decision support for agile manufacturing that uses recycled and reclaimed materials, converting waste streams into value-added products. Data-driven machine learning enables predictive analysis: artificial neural networks for modeling heat-treatment parameters and property impacts, convolutional neural networks for grain-size detection, and Random Forests for phrase-fraction detection. Results are promising, with ANN accuracy near 90% for predicting micro-structure development under quench tempering, and future efforts target improved vision models and sustainability metrics.","Machine Learning Approaches in Agile Manufacturing with Recycled Materials  \nfor Sustainability  \nAparna S. Varde  \nDepartment of Computer Science Clean Energy & Sustainability Analytics Center Montclair State University, NJ, USA [vardea@montclair.edu](vardea@montclair.edu), ORCID ID: 0000-0002-3170-2510  \nJianyu Liang  \nMechanical and Materials Engineering Worcester Polytechnic Institute Worcester, MA, USA [jianyul@wpi.edu](jianyul@wpi.edu)  \narXiv :2303 .08291v1 [ cs .AI] 15 Mar 2023  \nAbstract  \nIt is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties; deep learning via advances such as convolutional neural networks to explore grain size detection; and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes; creating domain-specific tools using machine learning for decision support in agile manufacturing; and assessing impactson sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science.  \nIntroduction  \nThe role of AI in the overall realm of sustainability is critical in recent times. This is because we truly need to live in a green, clean and sustainable manner in order to save the planet, wherein machine learning techniques can help to make adequate predictions for decision support in scientific processes. This is pertinent to various scientific domains as evident from a plethora of studies in the literature (Butler et al. 2018),(Kotthoff et al. 2022), (Nishant,  \nFigure 1: AutoDomainMine for computational estimation  \nKennedy, and Corbett 2020),(Ai et al. 2011),(Hutter, Kotthoff, and Vanschoren 2019),(Rajasekar and Weng 2009),(Liu et al. 2022), (Tsolakis et al. 2021),(Weikum et al. 2021),(Zaki, Ramakrishnan, and Zhao 2010),(Zadeh, Abbasov, and Shahbazova 2015) . In this paper, we address the domain of Materials Science & Manufacturing, claiming that sustainable processes are imperative.  \nMore specifically, our problem is defined as follows. There is a necessity to develop agile manufacturing techniques using recycled & reclaimed metals. It is a safe & responsible method of turning a specific waste stream to value-added products, in line with environmental sustainability. Note that agile manufacturing is “a manufacturing methodology that places an extremely strong focus on rapid response to the customer-turning speed and agility into a key competitive advantage”(Lean-Production 2011) . Therefore, agile manufacturing is a recommended approach for acquiring a “competitive advantage in the fast-moving marketplace” of the modern day. We investigate the c","cbCaiqx9Fn2gs4EB","https://ap.wps.com/l/cbCaiqx9Fn2gs4EB","pdf",2422223,1,6,"English","en",105,"# Abstract\n# Introduction\n# Earlier Research: AutoDomainMine\n# Proposed Approaches","[{\"question\":\"How does this research use AI for sustainable agile manufacturing?\",\"answer\":\"It uses data-driven machine learning models to provide decision support in agile manufacturing by guiding choices using recycled and reclaimed materials.\"},{\"question\":\"Which machine learning methods are proposed, and what are they used for?\",\"answer\":\"The study proposes artificial neural networks to learn heat-treatment parameter effects, convolutional neural networks for grain size detection, and Random Forests for phrase fraction detection.\"},{\"question\":\"What results are reported for micro-structure prediction?\",\"answer\":\"The abstract reports that an ANN achieves about 90% accuracy for predicting micro-structure development for quench tempering, a heat treatment process.\"}]","Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability | 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does this research use AI for sustainable agile manufacturing?","Question",{"text":75,"@type":76},"It uses data-driven machine learning models to provide decision support in agile manufacturing by guiding choices using recycled and reclaimed materials.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are proposed, and what are they used for?",{"text":80,"@type":76},"The study proposes artificial neural networks to learn heat-treatment parameter effects, convolutional neural networks for grain size detection, and Random Forests for phrase fraction detection.",{"name":82,"@type":73,"acceptedAnswer":83},"What results are reported for micro-structure prediction?",{"text":84,"@type":76},"The abstract reports that an ANN achieves about 90% accuracy for predicting micro-structure development for quench tempering, a heat treatment 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