Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing

IF 2 3区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Adithyaa Karthikeyan, Soham Das, Satish T.S. Bukkapatnam, Ceyhun Eksin
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The dynamic model can account for both material removal and redistribution during the polishing process. In addition, the model accounts for increased material flow due to heat generated during the polishing process. We also provide an initial random surface model that matches the initial surface statistics. We propose an optimization problem for model parameter estimation based on empirical data using KL-divergence and surface roughness as two metrics of the objective. We validate the proposed model using data from polishing of a 3D printed sample. The procedures developed makes the model applicable to other 3D printed materials and polishing processes. We obtain a network formation model as a representation of the surface evolution from the heights and radii of asperities. We use the network connectivity (Fiedler number) as a metric for surface smoothness that can be used to determine whether a desired level of smoothness is reached or not.Keywords: Additive ManufacturingPolishingSurface Morphology EvolutionNetwork FormationDynamic modelsMaterial Removal and Material RedistributionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsAdithyaa KarthikeyanAdithyaa Karthikeyan is currently a PhD student specializing in Advanced Manufacturing within the Department of Industrial and Systems Engineering, Texas A&M University, USA. He received his B.Tech (Hons) degree in Mechanical Engineering from National Institute of Technology, Tiruchirappalli, India in 2017 and MS degree in Interdisciplinary Engineering from Texas A&M University in 2020. He was a summer intern with Micron Technology Inc. in 2023 with their CMP (Chemical Mechanical Planarization) process development team for semiconductor manufacturing. His research interests include mathematical modelling and data analytics for manufacturing processes and systems.Soham DasSoham Das is a PhD student in the Department of Industrial and Systems Engineering at Texas A&M University, USA. He received his B.Tech degree in Mechanical Engineering from NIT Durgapur, India in 2017. His research lies at the intersection of game theory and combinatorial optimization, such as in the control of learning in network games.Satish T.S. BukkapatnamSatish T.S. 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引用次数: 0

Abstract

AbstractMany industrial components, especially those realized through 3D printing undergo surface finishing processes, predominantly, in the form of mechanical polishing. The polishing processes for custom components remains manual and iterative. Determination of the polishing endpoints, i.e., when to stop the process to achieve a desired surface finish, remains a major obstacle to process automation and in the cost-effective custom/3D printing process chains. With the motivation to automate the polishing process of 3D printed materials to a desired level of surface smoothness, we propose a dynamic model of surface morphology evolution of 3D printed materials during a polishing process. The dynamic model can account for both material removal and redistribution during the polishing process. In addition, the model accounts for increased material flow due to heat generated during the polishing process. We also provide an initial random surface model that matches the initial surface statistics. We propose an optimization problem for model parameter estimation based on empirical data using KL-divergence and surface roughness as two metrics of the objective. We validate the proposed model using data from polishing of a 3D printed sample. The procedures developed makes the model applicable to other 3D printed materials and polishing processes. We obtain a network formation model as a representation of the surface evolution from the heights and radii of asperities. We use the network connectivity (Fiedler number) as a metric for surface smoothness that can be used to determine whether a desired level of smoothness is reached or not.Keywords: Additive ManufacturingPolishingSurface Morphology EvolutionNetwork FormationDynamic modelsMaterial Removal and Material RedistributionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsAdithyaa KarthikeyanAdithyaa Karthikeyan is currently a PhD student specializing in Advanced Manufacturing within the Department of Industrial and Systems Engineering, Texas A&M University, USA. He received his B.Tech (Hons) degree in Mechanical Engineering from National Institute of Technology, Tiruchirappalli, India in 2017 and MS degree in Interdisciplinary Engineering from Texas A&M University in 2020. He was a summer intern with Micron Technology Inc. in 2023 with their CMP (Chemical Mechanical Planarization) process development team for semiconductor manufacturing. His research interests include mathematical modelling and data analytics for manufacturing processes and systems.Soham DasSoham Das is a PhD student in the Department of Industrial and Systems Engineering at Texas A&M University, USA. He received his B.Tech degree in Mechanical Engineering from NIT Durgapur, India in 2017. His research lies at the intersection of game theory and combinatorial optimization, such as in the control of learning in network games.Satish T.S. BukkapatnamSatish T.S. Bukkapatnam is the Rockwell International Professor of Industrial and Systems Engineering at Texas A&M University, and the Director of Texas A&M Engineering Experiment Station Institute of Manufacturing Systems. He received his PhD degree in Industrial and Manufacturing Engineering from Pennsylvania State University (1997). His research interests are broadly in smart manufacturing systems, and ultraprecision manufacturing. Dr. Bukkapatnam is a Fellow of IISE and SME, Associate Member of CIRP, and was a Fulbright-Tocqueville Distinguished Chair.Ceyhun EksinCeyhun Eksin received his B.S. degree in control engineering from Istanbul Technical University, Istanbul, Turkey, in 2005, the M.S. degree in industrial engineering from Boğaziçi University, Istanbul, Turkey in 2008, the M.A. degree in statistics from Wharton Statistics Department, University of Pennsylvania, Philadelphia, PA, 19103, USA in 2015, and the Ph.D. degree in electrical and systems engineering from the Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA in 2015. He was a postdoctoral researcher jointly affiliated with Scholls of Biological Sciences, and Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. He is currently an assistant professor in the Department of Industrial and Systems Engineering at Texas A&M University, College Station, TX, USA. He is a recipient of NSF CAREER award in 2023. His research interests are in the areas of networks, game theory, control theory, and distributed optimization. His current research focuses on game theoretic learning and decentralized optimization with applications to autonomous teams, epidemics and energy systems.
增材制造抛光过程中表面形貌演变的统计与动态模型
摘要许多工业部件,特别是那些通过3D打印实现的部件,主要以机械抛光的形式进行表面加工。定制组件的抛光过程仍然是手动和迭代的。抛光终点的确定,即何时停止过程以达到所需的表面光洁度,仍然是过程自动化和具有成本效益的定制/3D打印工艺链中的主要障碍。为了使3D打印材料的抛光过程自动化,达到所需的表面光滑度水平,我们提出了3D打印材料在抛光过程中表面形态演变的动态模型。该动态模型可以考虑抛光过程中材料的去除和再分布。此外,该模型考虑了由于抛光过程中产生的热量而增加的物料流。我们还提供了一个与初始表面统计相匹配的初始随机表面模型。我们提出了一个基于经验数据的模型参数估计的优化问题,使用kl -散度和表面粗糙度作为目标的两个度量。我们使用3D打印样品的抛光数据验证了所提出的模型。开发的程序使模型适用于其他3D打印材料和抛光工艺。我们从凸起的高度和半径得到一个网络形成模型作为表面演化的表示。我们使用网络连通性(费德勒数)作为表面平滑度的度量,可用于确定是否达到所需的平滑度。关键词:增材制造抛光表面形貌进化网络形成动态模型材料去除和材料再分配免责声明作为对作者和研究人员的服务,我们提供此版本的接受手稿(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。adithyaa Karthikeyan目前是美国德克萨斯农工大学工业与系统工程系高级制造专业的博士生。他于2017年获得印度国立理工学院机械工程(荣誉)学士学位,并于2020年获得德克萨斯A&M大学跨学科工程硕士学位。他于2023年在美光科技公司(Micron Technology Inc.)的半导体制造CMP(化学机械平面化)工艺开发团队担任暑期实习生。他的研究兴趣包括制造过程和系统的数学建模和数据分析。Soham DasSoham Das是美国德州农工大学工业与系统工程系的博士生。他于2017年获得印度NIT杜尔加普尔机械工程学士学位。他的研究方向是博弈论与组合优化的交叉,如网络游戏中的学习控制。Satish T.S. Bukkapatnam是德克萨斯A&M大学工业和系统工程的罗克韦尔国际教授,也是德克萨斯A&M工程实验站制造系统研究所的主任。他于1997年获得宾夕法尼亚州立大学工业与制造工程博士学位。他的研究兴趣广泛在智能制造系统和超精密制造。Bukkapatnam博士是IISE和SME的研究员,CIRP的准会员,并且是富布赖特-托克维尔杰出主席。Ceyhun eksin2005年在土耳其伊斯坦布尔伊斯坦布尔技术大学获得控制工程学士学位,2008年在土耳其伊斯坦布尔Boğaziçi大学获得工业工程硕士学位,2015年在美国宾夕法尼亚州费城宾夕法尼亚大学沃顿统计系获得统计学硕士学位,2015年在电气与系统工程系获得电气与系统工程博士学位。2015年美国宾夕法尼亚州费城宾夕法尼亚大学。他是美国佐治亚理工学院生物科学学院和电子与计算机工程学院的博士后研究员。他目前是美国德州农工大学(College Station, Texas A&M University)工业与系统工程系助理教授。他是2023年美国国家科学基金会职业奖的获得者。主要研究领域为网络、博弈论、控制理论和分布式优化。 他目前的研究重点是博弈论学习和分散优化及其在自治团队,流行病和能源系统中的应用。
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来源期刊
IISE Transactions
IISE Transactions Engineering-Industrial and Manufacturing Engineering
CiteScore
5.70
自引率
7.70%
发文量
93
期刊介绍: IISE Transactions is currently abstracted/indexed in the following services: CSA/ASCE Civil Engineering Abstracts; CSA-Computer & Information Systems Abstracts; CSA-Corrosion Abstracts; CSA-Electronics & Communications Abstracts; CSA-Engineered Materials Abstracts; CSA-Materials Research Database with METADEX; CSA-Mechanical & Transportation Engineering Abstracts; CSA-Solid State & Superconductivity Abstracts; INSPEC Information Services and Science Citation Index. Institute of Industrial and Systems Engineers and our publisher Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, Institute of Industrial and Systems Engineers and our publisher Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Institute of Industrial and Systems Engineers and our publisher Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Institute of Industrial and Systems Engineers and our publisher Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions .
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