A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic

Abstract

This study focused on the Grey-Based Fuzzy Logic Algorithm for the prediction and optimization of multiple performance characteristics of oblique turning process. Experiments have been constructed according to Taguchi's L16 orthogonal array design matrix. Cutting speed, rate of feed and depth of cut were selected as input parameters, whereas material removal rate, cutting force and surface roughness were selected as output responses. Using grey relation analysis (GRA), grey relational coefficient (GRC) and grey relation grade (GRG) were obtained. Then, Grey based fuzzy algorithm was applied to obtain grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the application feasibility of the grey based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.

Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms

“…These hazards may result in circumstances that directly endanger the lives of many people, result in many deaths, or have a significant impact on the lives of those impacted. Both on the military and civilian levels, an isolated patient transportation capsule is an essential life-saving tool [27][28].…”