RESUMO
Ultra-precision turning is a crucial process in the manufacturing industry as it helps to produce parts with high dimensional accuracy, surface finish, and tolerance. The process is similar to traditional turning but is carried out under special circumstances to achieve greater precision and surface finish. The process can be applied to conventional structural materials, but the demand for machining hardened steels is increasing. The optimization of ultra-precision turning of AISI D2 using cubic boron nitride (CBN) tools is a crucial aspect in the field of high-quality machining. This study aims to evaluate the performance of the process and identify the optimal parameters that result in the best quality components while using a CBN tool's ultra-precision turning of AISI D2. Ultra-precision turning process factors such as cutting speed, feed, and depth of cut were experimentally investigated to enhance the response output, such as surface roughness and cutting force components. The full factorial experimental design was used for determining the process characteristics under different conditions, and experimental results were applied to search for the optimum response of machining performance. The optimization process was done by combining the hybrid genetic algorithm-response surface methodology (GA-RSM) and the Taguchi-grey relational analysis (GRA) statistical tools. These methods are useful in situations where the relationship between the input variables and the output responses is complex and non-linear. The results showed that a hybrid GA-RSM approach, combined with Taguchi-GRA statistical analysis, can effectively find optimal process parameters, leading to the best combination of surface roughness and cutting force. In hybrid Taguchi - GRA, the optimal cutting conditions were found to be a cutting speed of 175 m/min, a feed of 0.025 mm, and a depth of cut of 0.06 mm. The findings of this study provide valuable insights for the optimization of ultra-precision CBN turning operations, contribute to the development of precision manufacturing technology, and can be used as a reference for similar machining processes.
RESUMO
Additive manufacturing (AM), also known as 3D printing, is a cutting-edge industrial production technique that enables the creation of lighter, stronger components and systems. Fused deposition modeling (FDM) is a popular AM process for creating prototypes and functional components out of common engineering polymers. The mechanical characteristics of printed items are dramatically altered as a result of various process factors. As a result, it is critical to examine the impact of printing settings on the quality of the printed item. In terms of flexural strength, this study presents an experimental examination into the quality analysis of parameters on printed components utilizing FDM. By adjusting process factors such as layer height, raster width, raster angle, and orientation angle, the experiment was carried out utilizing Taguchi's L18 mixed orthogonal array approach. The UNITEK-94100 universal testing equipment was used to evaluate the flexural strength of Acrylonitrile butadiene styrene (ABS) specimens that had been conditioned as per ASTM D790 standard. The impacts of parameters on experimental results were examined and optimized using the hybrid genetic algorithm with response surface methods, response surface approach, and Taguchi method. When the optimal solutions of each technique were studied, the response surface approach and Taguchi methods were determined to be less promising than the genetic algorithm method.
