Abstract
Due to its high precision, productivity, and surface quality, computer numerical control turning (CNC) is a desirable processing tool in the traditional processing area. CNC machining procedures have a huge number of process parameters, making it challenging to find the best combination of parameters for increased accuracy. In this research work, the Taguchi method and ANOVA were used to study the effects of CNC machining parameters in EN8 steel turning: Surface roughness (Ra) value of component affected due to cutting speed, depth of cut and feed rate. Three-level three-parameter experimental design, using Minitab 17 software using L9 orthogonal array, using coated carbide insert cutting tools, using signal-to-noise ratio (S/N) to study the performance characteristics of EN8 steel turning. In this study, statistical approaches such as the signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) were used to explore the effects of cutting speed, depth of cut, and feed rate on surface roughness. Nature-inspired algorithms play a vital role in solving real life. In this study, the bat algorithm can be used to predict the optimal surface value (Ra) and process parameters. Verify the results by conducting confirmation experiments. The current research shows that the feed rate is the most important factor affecting the surface roughness (Ra) of EN8 steel turning.
Highlights
Modern precision manufacturing requires extremely high dimensional accuracy and surface finish
The current work involves determining the optimal settings of single parameter optimization and multi-response optimization process parameters for machining on computer numerical control (CNC) machine tools of EN8 steel based on the Taguchi method and Bat-inspired algorithm
The advantage of Taguchi method in simplifying experiments is effectively used in the design and analysis of surface quality in this study
Summary
Modern precision manufacturing requires extremely high dimensional accuracy and surface finish. Even if it is not a skilled operator, if not impossible, it is difficult to achieve such a performance manually. The development of computer numerical control (CNC) machines has made it possible to automate the machining process, and can flexibly handle the production of small and medium batches of parts. This led to the development of computer-based automatic machine tool controls, known as numerical control (NC) systems. In this research paper a modern and effective optimization method which is a bio-inspired method is used to optimize the parameters
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