A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. (1st April 2017) - Zhang, Hua; Deng, Zhaohui; Fu, Yahui; Lv, Lishu; Yan, Can
Abstract: Responding to the current urgent need for low carbon and high efficiency manufacturing, the relationships between the processing time, power energy consumption and the carbon emissions with the milling process parameters were researched during dry milling processes. The characteristics of the power energy consumption and carbon emissions were also analyzed. The lowest energy, high efficiency (the minimum processing time) and lowest carbon emission functions were separately constructed. Next, multi-objective optimization model to achieve high efficiency, low energy consumption and low carbon emissions were constructed. The multi-objective optimization model was converted into a single goal with weight coefficients introduced. The procedure to identify the empirical model function coefficients was built by principal component analysis and regression analysis based on experimental data. Considering the processing constraints from machine equipment performance and machining quality, the operational flow chart was given to solve the optimization model using the genetic algorithm. The feasibility of the process parameter optimization method to trade off three responses (low processing time, low energy consumption and low carbon emissions) was validated using a practical example. From the results, large feed rates and large cut width can benefit to three responses if the constrains can be met with. The optimized solution and program of cutting parameters was useful to the optimum performance of the machine tool with the cutter, and benefited to reach sustainable manufacturing. The balance of the efficiency, energy consumption and carbon emissions can be helpful for the enterprises to develop the cutting parameters and to relieve the impact to the environment. Graphical abstract: Highlights: Relationships between three responses with milling process parameters were studied. An optimization model to trade off three responses was proposed. A procedure to identify the coefficients by regression analysis was proposed.