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Collaborative Research: Specific Energy-Based Prognosis for Machining Surface Integrity through Integration of Process Physics and Machine Learning

合作研究：通过过程物理和机器学习的集成，基于特定能量的加工表面完整性预测

基本信息

批准号：
2040358
负责人：
Yuebin Guo
金额：
$ 34.26万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040358&HistoricalAwards=false
关键词：
Collaborative Research Specific Energy Based

项目摘要

Manufacturing employs more than 12 million jobs and contributes over $2 trillion to the Gross Domestic Product (GDP) annually. At the same time, manufacturing accounts for about 28 percent of the annual total energy consumed in the U.S. This is particularly true for metal cutting and machining processes, which have been a major contributor to the national economy in value creation, education, workforce development and employment. Despite rapid advancement in sensing and communication technologies, real-time process monitoring and prediction of the surface integrity of machined parts have remained a challenge for energy efficient, high-quality machining. Although the incorporation of real-time sensing data into physics-based machining models has the potential for model updating and calibration, and emerging machine learning (ML) techniques have demonstrated the effectiveness in data analysis for manufacturing, the general black-box nature of ML models has limited rigorous, physics-based interpretations of ML outcomes. This award addresses this existing gap by introducing a physics-guided learning method for machining surface integrity prediction with improved accuracy and transparency, through the complementary strengths of data science and process physics. The outcome of this project impacts multiple industry sectors, from aerospace to automotive, energy, and healthcare. The project’s interdisciplinary nature helps train the next generation of manufacturing workforce by broadening participation of women and underrepresented minority groups in research and education.This research investigates the compounding effects of machining process parameters on the surface integrity of machined parts. The research approach is multifold. (1) Develop physical models for the specific energy associated with machining surface integrity; (2) Develop a data generative method to synthesize images of cutting tool wear and machined surfaces by automatic characterization; (3) Integrate cutting physics into a recurrent neural network (RNN) for physics-guided surface integrity prediction to improve the interpretability and transparency of the ML outcomes; and (4) Experimentally evaluate the developed methods on a production-grade machine. The resulting methodology reduces the time and cost for post-machining product quality inspection, and creates new knowledge in three areas: (1) Introducing a new, energy-centric learning method that characterizes the machining surface integrity by means of specific energy; (2) Developing a new data synthesis method to address limitations in surface integrity data availability for model construction and evaluation; and (3) Demonstrating an effective pathway to integrate machine learning with physical knowledge for improved interpretation of the network structure and its prediction logic, thereby enhancing the network’s transparency and acceptance by the manufacturing industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

制造业雇佣了超过1200万个工作岗位，每年对国内生产总值(GDP)的贡献超过2万亿美元。与此同时，制造业约占美国年总能源消耗的28%。在金属切割和机械加工过程中尤其如此，它们在价值创造、教育、劳动力发展和就业方面对国民经济做出了重大贡献。尽管传感和通信技术发展迅速，但加工零件表面完整性的实时过程监控和预测仍然是高能效、高质量加工的挑战。尽管将实时传感数据结合到基于物理的加工模型中具有更新和校准模型的潜力，并且新兴的机器学习(ML)技术已经证明了在制造业数据分析中的有效性，但ML模型的一般黑箱性质限制了对ML结果的严格的、基于物理的解释。该奖项通过引入物理指导的学习方法，通过数据科学和过程物理的互补优势，提高了加工表面完整性预测的准确性和透明度，从而解决了这一现有差距。该项目的结果影响到多个行业，从航空航天到汽车、能源和医疗保健。该项目的跨学科性质有助于通过扩大妇女和代表不足的少数群体在研究和教育方面的参与来培训下一代制造业劳动力。本研究调查了机械加工工艺参数对机械加工零件表面完整性的综合影响。研究方法是多重的。(1)开发与加工表面完整性相关的比能量的物理模型；(2)开发通过自动表征合成刀具磨损和已加工表面图像的数据生成方法；(3)将切割物理集成到用于物理制导的表面完整性预测的递归神经网络(RNN)中，以提高ML结果的可解释性和透明度；以及(4)在生产级机器上对所开发的方法进行实验评估。该方法减少了加工后产品质量检测的时间和成本，并在三个方面创造了新的知识：(1)引入了一种新的以能量为中心的学习方法，以比能量来表征加工表面完整性；(2)开发了一种新的数据合成方法，以解决用于建模和评估的表面完整性数据可用性的局限性；以及(3)展示了一种将机器学习与物理知识相结合的有效途径，以改进对网络结构及其预测逻辑的解释，从而提高网络的透明度和制造业的接受度。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。