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

制造业提供了超过1200万个就业岗位，每年为国内生产总值（GDP）贡献超过2万亿美元。与此同时，制造业约占美国年度总能源消耗的28%，特别是金属切割和机械加工过程，这是国民经济在价值创造、教育、劳动力发展和就业方面的主要贡献者。尽管传感和通信技术迅速发展，但对加工零件表面完整性的实时过程监测和预测仍然是节能、高质量加工的一个挑战。尽管将实时传感数据整合到基于物理的加工模型中具有模型更新和校准的潜力，并且新兴的机器学习（ML）技术已经证明了制造数据分析的有效性，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学的互补优势，提高加工表面完整性预测的准确性和透明度，解决了这一现有差距。该项目的成果将影响多个行业，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未被充分代表的少数群体在研究和教育中的参与来培训下一代制造业劳动力。研究了加工工艺参数对被加工零件表面完整性的复合影响。研究方法是多方面的。(1)建立与加工表面完整性相关的比能物理模型；(2)开发一种数据生成方法，通过自动表征合成刀具磨损和加工表面图像；(3)将切割物理整合到递归神经网络（RNN）中，用于物理指导的表面完整性预测，以提高机器学习结果的可解释性和透明度；(4)在生产级机器上对所开发的方法进行实验评估。由此产生的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，该方法通过比能量来表征加工表面的完整性；(2)开发一种新的数据综合方法，以解决模型构建和评估中地表完整性数据可用性的限制；(3)展示了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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.