Abstract
The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multi-criteria decision making respectively. This integrated approach, to best of the authors’ knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design.
Highlights
Owing to the rapidly accelerating global demand for high-quality and sustainable products, scientific and manufacturing communities are focusing on sustainable machining evaluation and design [1], [2], with particular focus on coming up with innovation and creative solutions in metal cutting technology
Different solutions have been proposed for several sophisticated requirements
Depth of cut and feed of 196.8 m/min, 0.93 mm 0.14 mm/rev, respectively, it compromised between quality, productivity, and process economics, leading to surface roughness of 0.419 μm, material removal rate of 26131.6 mm3/min and machining power of 4.04 kW
Summary
Owing to the rapidly accelerating global demand for high-quality and sustainable products, scientific and manufacturing communities are focusing on sustainable machining evaluation and design [1], [2], with particular focus on coming up with innovation and creative solutions in metal cutting technology. Gray Relation Analysis GRA with equal weights were used to optimize Ra, cutting forces, and hardness while turning AISI 316L and 304L by Basmaci and Ay [22] and Ay [23] respectively They compared conventional and wiper tools, as well as MQL and flood coolant under different feed rates and cutting speeds. It is imperative to integrate adaptive process design approaches, such as the proposed one in this study, with advanced machining technologies, like the innovative wiper insert design, to promote sustainability in the era of smart manufacturing. Following this introduction, the remaining of this article is outlined as follows. An intensive discussion will be provided to show the superiority and trades-off between the different proposed designs
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