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

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.