Development of Artificial Neural Network Based Status Monitoring Systems for Tool Condition Assessment of CNC Millers

  • 孫 翊淳

Student thesis: Doctoral Thesis


Machine tools play key roles in modern manufacturing industry The quality of machined products are largely depended on the status of machines in various aspects As a result appropriate condition monitoring would be essential for both quality control and life assessment With recent advancement of computer science artificial intelligence (AI) becomes an alternative choice for establishing diagnostic model AI provides a decision-making system by using multiple sensor features to predict the states of machines especially for the machines without physical model While many research works focus on the adjustment of model parameters or trying different algorithm to improve accuracy the domain knowledge of machine failures is rarely studied As a result an artificial neural network based status monitoring system which is combined with comprehensive investigation of sensor features should be developed Moreover for modern machine tools high ratio of down time is attributed to tool failure In addition the complexity of machining operation makes development of a model which can universally applied to different operations by curve fitting difficult Hence tool condition assessment is taken as a scenario in this work For achieving above addressed goal the experimental system must be setup first For establishing a low cost wireless communication system a four channels data transmission module based on Arduino board and Bluetooth is developed Meanwhile to access cutting tool condition from physical signal the multi-sensor environment and data acquisition system are configured In addition the signal processing and feature extraction schemes are also addressed Meanwhile to obtain the corresponding domain knowledge of tool failure a number of machining experiments are designed and executed Through the effort of investigating the relation between tool conditions and sensor indexes by sensor index evaluation six indexes which are more sensitive to tool condition can be listed to initially establish a diagnostic process Finally a multilayer perceptron (MLP) model is adopted to carry out condition assessment and three models trained by different input features are compared to examine the feasibility of integrating domain knowledge and AI In the near future with more data collected it is expected that more sophisticated models would be developed for better predicting the tool condition Meanwhile this concept can be further applied to other sub-systems which are also lack of physical models for establishing status diagnostic model to enhance the manufacturing reliability
Date of Award2019
Original languageEnglish
SupervisorKuo-Shen Chen (Supervisor)

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