Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

Improving the surface characteristics of roller burnishing processes is one of effective approaches to decrease the machining costs and time. This paper systematically investigates the nonlinear relationships between machining parameters and surface characteristics, including surface roughness (R_a), surface hardness (H), and hardness depth (HD) of the interior roller burnishing using response surface method (RSM) model. Three process parameters considered include spindle speed S, feed rate F, and burnishing depth D. A set of physical experiments was carried out with AISI 1045 steel on a computer numerical control (CNC) milling machine using the roller burnishing tool. The target of the current complex optimization is to enhance the surface hardness and hardness depth, while the surface roughness is considered as the constraint. Finally, an evolutionary algorithm entitled archive-based micro genetic algorithm (AMGA) was used to generate a set of feasible optimal solutions and determine the best machining conditions. The results show that an appropriate trade-off solution can be drawn with regard to the low surface roughness and high the surface hardness as well as hardness depth. Furthermore, the integration of RSM model and AMGA can be considered as a powerful approach for modeling and optimizing interior roller burnishing processes.

Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This article presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed (V), depth of cut (a), feed rate (f), and nose radius (r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the Grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85% and 57.65%, respectively, and the power factor improves around 28.83%, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices.

The burnishing process is an impressive solution in order to improve the surface integrity. However, energy-efficient optimization of the burnishing process is rarely considered due to the high efforts required. This paper presented an input factor-based optimization to simultaneously enhance the power factor (PFB), the improvement of the Brinell hardness (K_BH), and the reduction of the average roughness (K_Ra), while energy consumption (ECB) aims to decrease for the burnishing process of SKD61 steel. The burnishing speed (V), the feed (f), and the depth of penetration (d) were considered as the processing factors. The trials were conducted using the matrix generated by Taguchi. The principal component analysis (PCA) was applied to calculate the weight values of responses. The optimal parameters were determined using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results showed that the optimal values of the V, f, and d are 700 RPM, 500 mm/min, and 0.13 mm, respectively. The technical outputs are primarily influenced by the feed rate and depth of penetration. The reductions of energy consumption and surface roughness are approximately 49.48% and 13.79%, while the power factor and Brinell hardness improve around 21.80% and 56.02%, respectively, as compared to the worst case.