Hybrid data-driven and model-informed online tool wear detection in milling machines. (April 2022)

• Record Type: Journal Article
• Authors: Yang, Qian; Pattipati, Krishna R.; Awasthi, Utsav; Bollas, George M.
• Is Part Of: Journal of manufacturing systems. Volume 63(2022)
• Page Start: 329
• Page End: 343
• Publication Date: 2022-04
• Subjects: Online anomaly detection -- Hybrid Physics-based and Machine Learning Models -- Decision tree -- Neural network -- Cumulative sum test; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.04.001
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page's cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear. Highlights: Fusion of operating signals, such as spindle power, with domain expert knowledge. Characterizing the state of the tool and for early detection of anomalies in machine's state of operation in real time. Adapting physics-based model by adding a data-driven model to account for the errors between predictions and actual data. Optimizing the sensors via optimal feature ranking algorithm and to quantify the minimal detectable tool wear.