Research on tool wear factors for milling wood-plastic composites based on response surface methodology

Abstract

A high-speed milling experiment on wood-plastic composites was performed using cemented carbide tools, and the resulting wear pattern was studied. The influence of the cutting parameters, the cutting speed, feed speed, and axial cutting depth on the tool wear was studied via response surface methodology, and the influence of the interaction of the cutting parameters on tool wear was analyzed. Three-dimensional surface graphs and contour plots of the tool wear results were established. According to the experimental results, a mathematical model of the tool wear based on the second-order response surface methodology was established, and the model was utilized to verify its feasibility. The results show that the nose width (NW) increases with the increase of the cutting speed and axial cutting depth and decreases with the increase of feed speed. Among the factors affecting tool wear, the cutting speed had the greatest influence, followed by the feed rate, with the axial cutting depth affecting tool wear the least. According to the results of the interaction between the tool wear and the cutting parameters, a low feed speed and small axial cutting depth can be selected to ensure long tool life; for low-speed cutting, a high feed speed and large axial cutting depth can be adopted to ensure tool life while improving machining efficiency.

Design and test of a positioning system for a greenhouse electric micro-tiller based on ultra-wideband

“…Clock synchronization is easily achieved in actual situations through the positioning base station. Thus, calculating the time difference between the tag and the two base stations is convenient, which is consistent with the actual positioning situation (Wei et al, 2021b).…”

Machinability of Different Wood-Plastic Composites during Peripheral Milling

“…In other words, the wear of cutting tools when machining WPPC was more severe when compared with WPEC and WPVCC (Figure 6). It is known that tool wear is mainly affected by thermal-mechanical coupling [25]. As described in Section 2 in the present work, peripheral milling was adopted, which is an intermittent cutting process.…”

An Innovative Process Design Model for Machined Surface Error Distribution Consistency in High-Efficiency Milling

“…where y 0 is the initial to-be-determined value, p i is the influence coefficient of x i , p ij is the coefficient of the interaction effect of x i and x j , and Q is the effect of fitting errors and noise, which obeys normal distribution in this model. Simulation of each experiment according to the model of solving the formation process of machined surface errors, using the error correlation of the machined surface positional errors and the machined surface angular errors with the design reference planes as the response result of the experiment, according to Equation ( 18), and using Design-Expert 13.0Trial response surface model analysis software, along with response surface model construction for the simulation solution results of machined surface positional errors and machined surface shape errors, the mathematical model of the response surface methodology can be determined as shown in Equations ( 19) and (20):…”

“…According to the results of the significance analysis of the machined surface errors, it can be seen that the interactions of the rotational speed of the milling cutter and cutter tooth error, cutter tooth error and milling vibration, and the feed rate per tooth and milling vibration have obvious effects on the various characteristics of the machined surface positional errors; therefore, these three sets of interaction variables were analyzed separately, and the response surface is shown in Figure 9 according to the response surface methodology [20]. As shown in Figure 8, where "×" indicates the interaction between the two, among the design variables of machined surface errors' variation characteristics, probability levels in the range of 0~0.01 are highly influential; probability levels between 0.01 and 0.05 were considered obvious effects, while probability levels greater than 0.05 indicated weak or non-existent interaction with respect to the various characteristics of the machined surface position errors.…”

“…According to the results of the significance analysis of the machined surface errors, it can be seen that the interactions of the rotational speed of the milling cutter and cutter tooth error, cutter tooth error and milling vibration, and the feed rate per tooth and milling vibration have obvious effects on the various characteristics of the machined surface positional errors; therefore, these three sets of interaction variables were analyzed separately, and the response surface is shown in Figure 9 according to the response surface methodology [20]. To analyze the specific extent of the interaction of the influencing factors, the average curvature of the surface reflects the average degree of curvature of the surface in the region.…”