Energy Efficiency Optimization for Machining of Wood Plastic Composite

Abstract

Enhancing energy efficiency is the key to realizing green manufacturing. One major area of interest in this regard is the improvement of energy efficiency of machine tools during the production of building materials. This project focuses on energy efficiency during the spiral milling of wood plastic composites. To this end, a response surface method was adopted to develop a model and establish the relationship between energy efficiency and milling conditions. Analysis of variance based on individual factors as well as two-factor interactions was performed to gauge their effects on energy efficiency. It was found that milling depth was positively correlated to power efficiency, while spiral angle and feed per tooth displayed non-monotonic behavior. An attempt was made to predict milling conditions that will yield the greatest material removal rate and power efficiency. For wood plastic composites subjected to up-milling, it was determined that a feed per tooth of 0.1 mm, milling depth of 1.5 mm, and spiral angle of 70° were ideal. Considering the potential improvements in energy efficiency and surface quality that these process parameters will bring, it is strongly recommended for use in the industrial machining of wood plastic composites.

Testing and modeling for the tensile strength of nylon badminton string at different processing conditions

“…The standard deviation is 1.39 and the coefficient of variation is 0.24, also indicating that the error was low. Finally, the signal‐to‐noise ratio is 15.5 which indicates an adequate value compared to an adequate threshold of 4 [24]. In conclusion, the model (Equation 3) is adequate to predict the R m values for the range of conditions investigated.…”

Optimization of cutting power and power efficiency during particleboard helical milling

“…The output power of machine tool milling processing is not constant in different periods, which can be divided into machine tool startup status, machine tool standby state, spindle motor startup status, machine tool no‐load status, machine tool running at no load status and machine tool milling energy consumption status [40, 41]. The milling power was equal to the difference between machine tool milling energy consumption status and machine tool running energy consumption status, Equation (1) [42]. $$$\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr {P}_{c}={P}_{t}-{P}_{o}\hfill\cr}}$$$ …”

“…The higher the power efficiency, the higher the energy utilization rate in the milling process. The following was the equation for calculating the power efficiency, Equation (2) [42]. $$$\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr \eta ={{{P}_{c}}\over{{P}_{t}}}={{{P}_{c}}\over{{P}_{o}+{P}_{c}}}={{1}\over{1+{{{P}_{o}}\over{{P}_{c}}}}}\hfill\cr}}$$$ …”

Optimization of wood machining parameters using artificial neural network in CNC router

“…Salca [49] obtained the best results in machining with low feed speeds and cutting depths. Generally, increasing the width of cut, DoC, SS, and FR increase the CP and SR. Zhu et al [50] studied the optimisation of energy efficiency in machining of wood-plastic composites. They suggested high milling depth, low feed per tooth, and large spiral angle, or high milling depth and feed per tooth, and small spiral angle for optimal milling conditions.…”