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On-line model identification for the machining process based on multirate process data |
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| Affiliation: | 1. University of Bristol, BS8 1TR, United Kingdom;2. University of Skövde, 541 28, Skövde, Sweden;1. College of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China;2. Heavy-Duty Intelligent Manufacturing Equipment Innovation Center of Hebei Province, Qinhuangdao, 066004, China;3. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;1. Automatic Control Department, Universitat Politècnica de Catalunya, Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens i Artigas 4-6, Planta 2, 08028 Barcelona, Spain;2. Laboratory of Signals and Systems (L2S, UMR CNRS 8506), Centrale Supélec-CNRS Université Paris Sud, Université Paris-Saclay, 91190 Gif-sur-Yvette, France;1. Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, 200093, PR China;2. Department of Operations Management, Antai College of Economics & Management, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030, PR China;3. Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA |
| Abstract: | Accurate identification of the model parameters of the machining process based on on-line process data is a crucial prerequisite for its model-based control and diagnostics. A typical machining process generates multi-output and multirate data streams. Whereas various sensors provide in-process information about the process, many important process outcomes including product qualities can be only measured in postprocess manner. This paper proposes to improve the identification by using both in-process and postprocess data and by analyzing the identifiability of model parameters. The identification of the model parameters based on multirate output is formulated using the maximum-likelihood estimation and the Fisher information matrix for a multirate-sampled system is derived to study the identifiability of model parameters. A strategy is developed to improve accuracy and robustness of the model identification considering the identifiability. The proposed method is tested on two batches of multirate process data from the cylindrical grinding process. The test results demonstrate using both in-process and postprocess data improves the identifiability and the proposed identification strategy results in improved prediction performance. |
| Keywords: | Smart manufacturing Identifiability Process model |
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