Speaker
Description
The greenhouse effect has been a critical issue for the sustainable develop- ment of mankind, to address this issue, the concept of carbon taxes has been in- troduced. The introduction of carbon taxes forced the manufacturing industry to transform into green manufacturing to reduce carbon emissions and most im- portantly reduce cost. In this study, the carbon emission generated by the ma- chine tool will first be analyzed. This process includes energy consumption and the machining process. A second-order equation model will be fitted using least squares method for the energy consumption analysis to estimate the carbon emission. An experiment is conducted with the actual cutting process, for esti- mating the carbon emission during the manufacturing process. A mathematical model is then proposed to describe the relationship between the manufacturing process and carbon emissions. Multi-objective optimization algorithms are in- troduced to find the optimal parameters in the model for the most accurate pre- diction. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), is compared to see the model optimization performance. The final results showed that GWO has the lowest fitness value of 24.97. After obtaining the best manufacturing parameters, an experiment is conducted again, proving that the carbon emissions are lower after optimiza- tion.
