TY - GEN
T1 - Multi-response Analysis and Optimization during the Dry Turning of Aluminium 7075
AU - Maposa, Philly Previledge
AU - Mathonsi, Thabo Nelson
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - When machining components, it is crucial to identify appropriate parameters that enhance production efficiency while upholding quality. Recent studies into the optimum parameters of turning aluminium 7075 (Al7075) have revolved around four or fewer critical responses, including surface roughness, tool wear, material removal rate, cutting force, cylindricity and circularity error. This study delved into the analysis and optimization of the turning process for Al 7075 alloys, seeking the finest combination of surface roughness, tool wear, circularity error, cylindricity error, and material removal rate. The optimization process employed the Taguchi method, with a specific emphasis on three input parameters selected for experimental design: spindle speed, feed rate, and depth of cut, each set at three evenly spaced levels. The Taguchi method analysis determined that the optimum conditions for dry turning Al 7075 are a speed of 1950 RPM, a feed rate of 0.25 mm/rev, and a depth of cut of 2 mm. Analysis of variance (ANOVA) was then utilized to determine the impact of the turning parameters on the output responses. The results highlight that the spindle speed exerts the greatest influence on the flank wear at 45.3% while feed rate significantly impacts surface roughness at 64.9%. MRR, on the other hand, was mostly affected by the depth of cut at 62.9%. Cylindricity error and circularity error were both predominantly influenced by feed rate, with contributions of 30% and 32.8% respectively. When considering the combined optimized output response which encompasses minimizing surface roughness, flank wear, circularity and cylindricity error, while maximizing MRR, spindle speed exerted the most influence at 54.5%, followed by the depth of cut at 24.3%, and the feed rate at 13.7%. The residual error of the analysis was determined to be 7.5%
AB - When machining components, it is crucial to identify appropriate parameters that enhance production efficiency while upholding quality. Recent studies into the optimum parameters of turning aluminium 7075 (Al7075) have revolved around four or fewer critical responses, including surface roughness, tool wear, material removal rate, cutting force, cylindricity and circularity error. This study delved into the analysis and optimization of the turning process for Al 7075 alloys, seeking the finest combination of surface roughness, tool wear, circularity error, cylindricity error, and material removal rate. The optimization process employed the Taguchi method, with a specific emphasis on three input parameters selected for experimental design: spindle speed, feed rate, and depth of cut, each set at three evenly spaced levels. The Taguchi method analysis determined that the optimum conditions for dry turning Al 7075 are a speed of 1950 RPM, a feed rate of 0.25 mm/rev, and a depth of cut of 2 mm. Analysis of variance (ANOVA) was then utilized to determine the impact of the turning parameters on the output responses. The results highlight that the spindle speed exerts the greatest influence on the flank wear at 45.3% while feed rate significantly impacts surface roughness at 64.9%. MRR, on the other hand, was mostly affected by the depth of cut at 62.9%. Cylindricity error and circularity error were both predominantly influenced by feed rate, with contributions of 30% and 32.8% respectively. When considering the combined optimized output response which encompasses minimizing surface roughness, flank wear, circularity and cylindricity error, while maximizing MRR, spindle speed exerted the most influence at 54.5%, followed by the depth of cut at 24.3%, and the feed rate at 13.7%. The residual error of the analysis was determined to be 7.5%
KW - aluminium 7075
KW - Analysis of variance (ANOVA)
KW - Taguchi method
KW - turning
UR - http://www.scopus.com/inward/record.url?scp=85199576657&partnerID=8YFLogxK
U2 - 10.1109/ICMIMT61937.2024.10585855
DO - 10.1109/ICMIMT61937.2024.10585855
M3 - Conference contribution
AN - SCOPUS:85199576657
T3 - 2024 15th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2024
SP - 114
EP - 123
BT - 2024 15th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2024
Y2 - 17 May 2024 through 19 May 2024
ER -