Abstract
In a modern machining system, tool wear monitoring systems are needed to get higher quality production. In precision machining processes, especially surface quality of the manufactured part can be related to tool wear. This increases industrial interest for in-process tool wear monitoring systems. For the modern unmanned manufacturing process, an integrated system composed of sensors, signal processing interface and intelligent decision making model are required. In this study, a new method for on-line tool wear monitoring is presented under varying cutting conditions. The proposed method uses wear feature extraction based on process modeling and parameter estimation. An adaptive estimation model of milling tool wear in variable cutting parameters is built based entirely on milling power. The adaptive model traces the properties of cutting process by combining process state signal, cutting conditions, power model. The tool wear feature is obtained from the estimated parameters of the model and carried on in the theoretical and experimental study. Experiment results have proved that changes of the parameters in the cutting power model significantly indicate tool wear independently of varying cutting conditions and it makes tool wear a recognized process with high precision.
Highlights
Metal-cutting tool wear directly affects the precision, efficiency and cost efficiency of machining, so the on-line monitoring tool wear is becoming increasinhgly important, and has become an important research topic of flexible manufacturing system engineering
In the previous literature [1] to [5], the identification method regarding the milling tool wear conditions is to identify the main purpose of tool wear, which reached a stage, and a different processing method is applied according to the different phases
By detecting the processing state signal and processing parameters, processing state is predicted by using power model and least squares estimate model parameters
Summary
Metal-cutting tool wear directly affects the precision, efficiency and cost efficiency of machining, so the on-line monitoring tool wear is becoming increasinhgly important, and has become an important research topic of flexible manufacturing system engineering. Many past methods were developed to monitor tool wear by measuring spindle and feed motor power (current) and proved that the tool wear is very sensitive to the change of the cutting power [9] and [10]. MLR method for monitoring tool wear by measuring spindle and feed motor power is to establish a mathematical model between milling cutting parameters and the classification by fuzzy pattern using MLR analysis. The NN method for monitoring tool wear by measuring spindle and feed motor power is to establish a Neural Network model which contains milling cutting parameters and cutting power. (2) The model based on spindle and feed motor power is used to recognize tool wear and can cause larger error in a different cutting process by using the MLR method because tool wear model coefficients are fixed, that is, of low-precision and limiting applications. An improved strategy is proposed in this study for tool wear monitoring to solve such problems faced in the nonparametric and the parametric methods
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