Study of Cutting Power and Power Efficiency during Straight-Tooth Cylindrical Milling Process of Particle Boards

Abstract

The cutting power consumption of milling has direct influence on the economic benefits of manufacturing particle boards. The influence of the milling parameters on the cutting power were investigated in this study. Experiments and data analyses were conducted based on the response surface methodology. The results show that the input parameters had significant effects on the cutting power. The high rake angle reduced the cutting force. Thus, the cutting power decreased with the increase in the rake angle and the cutting energy consumption was also reduced. The cutting power increased with the rotation speed of the main shaft and the depth of milling induced the impact resistance between the milling tool and particle board and the material removal rate. The P-values of the created models and input parameters were less than 0.05, which meant they were significant for cutting power and power efficiency. The depth of milling was the most important factor, followed by the rotation speed of the main shaft and then the rake angle. Due to the high values of R2 of 0.9926 and 0.9946, the quadratic models were chosen for creating the relationship between the input parameters and response parameters. The predicted values of cutting power and power efficiency were close to the actual values, which meant the models could perform good predictions. To minimize the cutting power and maximize the power efficiency for the particle board, the optimized parameters obtained via the response surface methodology were 2°, 6991.7 rpm, 1.36 mm for rake angle, rotation speed of the main shaft and depth of milling, respectively. The model further predicted that the optimized parameters combination would achieve cutting power and power efficiency values of 52.4 W and 11.9%, respectively, with the desirability of 0.732. In this study, the influence of the input parameters on the cutting power and power efficiency are revealed and the created models were useful for selecting the milling parameters for particle boards, to reduce the cutting power.

Investigation on the optimal machining of Mg-based composites considering surface roughness, tool life, cutting forces, and productivity

“…To investigate the variation of a response versus the parameters, perturbation analysis can be done. 31 Figure 12 shows the perturbation analysis at the optimized point of machining. In this analysis, the more slope indicates the more effectiveness.…”

Optimization of cutting power and power efficiency during particleboard helical milling

“…The reason is that the spindle speed increases, the current of the spindle motor and the mechanical friction of the motor rotation increase, which resulted in an increase in the milling power. On the other hand, the higher spindle speed increased the impact force between the milling cutter and particleboard, which also led to the increase in milling power [47]. A decrease in milling power was been observed with the improvement of helical angle.…”

“…increase of spindle speed, P 0 and P c would increase at the same time, resulting in insignificant changes in the value of power efficiency. The higher the value of power efficiency, the higher the proportion of energy consumed in the material removal stage [47]. Maximum milling depth, maximum spindle speed and minimum helical angle would result in maximum power efficiency, Figure 5.…”

“…Research of the effects of cutting speed and feed rate on cutting power in medium density fiberboard (MDF) and plywood machining showed that the increasing cutting speed and feed rate lead to an increase in cutting power [16]. For the parameter of milling depth, the cutting power increased with the increasing of milling depth during medium density fiberboard and Scot's pine helical milling process [17, 18].…”

Prediction of surface roughness and cutting force induced during rotary ultrasonic bone drilling via statistical and machine learning algorithms

“…This scheme is precious for getting the optimized set of parameters. [34][35][36] The experimental trials were decided based on the number of parameters and their different levels. A total of 27 experiments (nine order runs with three repetitions) were performed.…”