Effect of cutting parameters on metal cutting tool wear and life

Effect of cutting parameters on metal cutting tool wear and life
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Keywords: cutting parameters; metal cutting tools; wear; life; impact analysis

0. Introduction

Cutting is a commonly used processing method in the field of metal processing. The wear and life of cutting tools have always been important factors restricting cutting processing effects and production efficiency. Reasonable selection of cutting parameters is the key to reducing cutting tool wear and extending tool life. Therefore, studying the influence of cutting parameters on tool wear and life, and optimizing and selecting the best cutting parameter combination, has important theoretical and practical value [1]. In cutting processing, cutting speed, feed rate and cutting depth are commonly used cutting parameters. The selection of these cutting parameters will directly affect the degree of tool wear and life during the cutting process. Too high cutting speed will cause the tool surface temperature to rise and accelerate tool wear; too large feed rate will increase the lateral impact and friction of the tool, resulting in increased tool wear; too large cutting depth will increase the tool force and wear area, further accelerating tool wear [2]. Therefore, by adjusting cutting parameters reasonably, tool wear can be reduced and tool life can be extended.

At present, relevant research has achieved certain results in the influence of cutting parameters on tool wear and life. However, due to the complexity of the cutting process, tool wear and life are affected by multiple factors, so there are still some problems to be solved [3]. This paper will conduct in-depth analysis and optimization research on the influence of cutting parameters on metal cutting tool wear and life, providing important theoretical guidance and technical support for the processing field. Through experimental research and statistical analysis, this paper will quantify the relationship between tool wear and life under different cutting parameters, and establish an optimization algorithm to help engineers select the best cutting parameter combination to maximize tool life and reduce wear.

1. Cutting dynamics model

During cutting, the cutting edge of the tool will be subjected to radial and tangential cutting forces at the same time, as shown in Figure 1. The traditional cutting model believes that the cutting force mainly comes from cutting deformation and friction between the tool and the workpiece [4], and its expression is:

At present, most studies are to change the cutting vibration by controlling the normal cutting force, so as to make the cutting process smooth. However, during cutting, changes in cutting depth and cutting feed rate will cause changes in the arc length of the tool-workpiece contact, thereby affecting the number of blades actually involved in the cutting process and the magnitude of the cutting force.

Therefore, expression (3) cannot be directly applied to the cutting force analysis in the actual processing process. The cutting force is affected by many factors, such as cutting depth, cutting feed rate, tool speed, etc. The commonly used empirical formula for calculating the cutting force is:

2. The influence mechanism of cutting parameters on tool wear and life

Cutting speed, feed rate and cutting depth are commonly used cutting parameters. Their changes will cause different degrees of wear on the tool. Too high cutting speed will increase the tool surface temperature and cause tool wear to increase; too large feed rate will increase the lateral impact and friction of the tool, resulting in increased wear; too large cutting depth will increase the tool force and wear area, further accelerating tool wear. Therefore, reasonable adjustment of cutting parameters is of great significance to tool wear and life [5].

Cutting force is an important indicator to measure cutting processability. It has different degrees of influence on cutting deformation and surface quality. The research and analysis of cutting force is of great significance for the formulation of good cutting technology. Domestic and foreign scholars have shown great interest in the research of cutting force. Cutting parameters and lubrication methods are important factors affecting cutting force. Dry cutting of aluminum alloy was carried out. According to variance analysis, the order of influence of cutting parameters on cutting force is: feed rate > cutting depth > cutting speed. A linear regression model of cutting force was established, and good prediction effect was achieved. Zhang et al. explored the influence of cutting parameters on cutting force of aluminum alloy under extreme environment, and established a three-way cutting force prediction model. Zhong Weiwu explored the influence of lubrication method on milling force of aluminum alloy. Through a single factor experimental scheme, the cutting force generated by dry cutting, wet cutting and air jet assisted micro-lubrication cutting was compared. It was found that the order of cutting force was: dry cutting > wet lubrication, and the amount of cutting fluid under metal cutting conditions was only 3% of that under wet lubrication conditions. Some experts and scholars use different methods to simulate and predict the cutting force generated in the aluminum alloy processing process. Mali simulated the cutting force of aluminum alloy through Deform software. By comparing the experimental cutting force, it was found that the relative errors of the main cutting force and feed force were 16.40% and 13.79% respectively, and the simulation effect was good.

3. Cutting parameter optimization algorithm for metal tool wear and life

3.1 Design of PID cutting force controller

PID control is an automatic control algorithm that combines the proportional link (P), integral link (I) and differential link (D). It has a simple principle, is easy to implement, has a wide range of applications and is easy to adjust, so it is widely used. Its working principle is to calculate according to the input error value according to the proportional term, integral term and differential term, and then use the calculation result to control the output. It is widely used in industrial automation, robot control, automobile control, aircraft control and other fields [6].

If the target cutting depth, effective cutting area and tool speed remain unchanged, the main factors affecting the cutting accuracy are cutting force and cutting feed speed. In order to facilitate subsequent research, the cutting force required when the robot cuts to the target depth is set to F (d ti), and the force sensor measures

3.2 PID-based cutting parameter optimization algorithm

Because the conventional PID control coefficients Kp, Ti, and Td are constant and will not be adjusted in real time according to the change of the cutting system state, once the robot's cutting state changes, the conventional PID control model cannot quickly adapt to this change, resulting in fluctuations in cutting force and affecting the quality of lamina cutting. In order to solve this problem, based on the PID control model, fuzzy rules are added to infer and judge the cutting state. This method can adjust the parameters in the PID control model according to the actual cutting state, so that the actual control model can adapt to the change of the cutting state.

The cutting system is a nonlinear and time-varying system. Its actual processing situation will change with the change of cutting parameters. Therefore, it is difficult for a general PID controller to meet the requirements of force control during the cutting process. Here, the fuzzy adaptive PID control strategy is used to control the cutting force. The advantage of this strategy is that the controller can adjust the PID control parameters Kp, Ti, and Td in real time according to the changes in the robot's cutting state, thereby performing segmented control of the cutting force. The control structure is shown in Figure 2.

4. Experimental results and analysis

The purpose of the simulation is to observe the control effect of the fuzzy PID strategy on the cutting force. First, a simulation model should be established in the MATLAB software according to the cutting dynamics formula, and then the PID control simulation should be performed. Finally, the simulation results of the three control methods are compared. The cutting depth in the simulation parameters is set to 1 mm, the starting feed speed is set to 2 mm/s, and the target cutting force is set to 5.6 N and the tool speed is set to 5000 r/min according to the cutting force results measured in the previous cutting experiment.

Since the changes in the various control parameters of the PID will have a huge impact on the control system, it is necessary to adjust the various control parameters of the PID controller before simulation to determine the optimal parameters. The parameter adjustment method used in this paper is the critical proportionality method. First, in the closed-loop control system, the controller is placed under pure proportional action, and then a disturbance is applied to the control system, gradually changing the proportional coefficient from small to large until equal-amplitude oscillation occurs, as shown in Figure 3. At this time, Ku = 3, Tu = 13.86.

The simulation results of each control method are shown in Figure 4. It can be observed that the fuzzy PID control strategy and the PID control strategy are similar in terms of the control of the cutting force peak value, both of which are lower than the cutting force peak value during open-loop control. The cutting force will go through an oscillation process after falling from the peak value. The cutting force amplitude is about 1.9 N under fuzzy ID control, which is 1 N less than that under PID control and 4 N less than that under open-loop control. In addition, the oscillation period of the cutting force is also greatly shortened, achieving rapid convergence of the cutting impact oscillation.

5. Application of results

The application of cutting dynamics parameters in robot modeling has not been deeply considered in the modeling, analysis and optimization of robot systems from the perspective of human-machine-biological system integration, especially in the trajectory accuracy and dynamic stability of robot cutting processing. This will inevitably lead to the robot not being closely integrated with various fields. It is particularly necessary to improve the control accuracy and stability of the robot. Most of the research focuses on how to plan the robot processing trajectory and has achieved many results, but there is a lack of in-depth research on the impact of cutting deformation and vibration on cutting trajectory accuracy during robot processing, resulting in poor robot cutting trajectory accuracy and stability, and unable to give full play to the advantages of stable operation and high processing accuracy of the robot. Therefore, it is very important to study cutting dynamics parameters for metal cutting tool wear and life.