Matches in SemOpenAlex for { <https://semopenalex.org/work/W4378983232> ?p ?o ?g. }
- W4378983232 abstract "Abstract High-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for Ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, Machine learning models and algorithms were developed with Support vector machine (SVM), Gaussian process relation (GPR), Adaptive-neuro fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that ANFIS gave the best predictive accuracy of average R, RMSE, and MAPE values of 0.98, 0.06, and 9.98%, respectively, and that of additional validation tests were 0.81, 0.17 and 32.34%, respectively, which are found reasonably accurate. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness R a among the process parameters, with 92% influence, and optimal cutting conditions was found to be cutting speed = 100 m/min, feed = 0.025 mm/rev and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry." @default.
- W4378983232 created "2023-06-02" @default.
- W4378983232 creator A5029041141 @default.
- W4378983232 creator A5045874646 @default.
- W4378983232 creator A5052738467 @default.
- W4378983232 creator A5087774354 @default.
- W4378983232 creator A5092065207 @default.
- W4378983232 date "2023-06-01" @default.
- W4378983232 modified "2023-10-18" @default.
- W4378983232 title "Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool" @default.
- W4378983232 cites W1982445933 @default.
- W4378983232 cites W2141057577 @default.
- W4378983232 cites W2460216658 @default.
- W4378983232 cites W2560197674 @default.
- W4378983232 cites W2612761251 @default.
- W4378983232 cites W2621187875 @default.
- W4378983232 cites W2772717124 @default.
- W4378983232 cites W2791808621 @default.
- W4378983232 cites W2793471602 @default.
- W4378983232 cites W2802499187 @default.
- W4378983232 cites W2809817009 @default.
- W4378983232 cites W2889160856 @default.
- W4378983232 cites W2901618712 @default.
- W4378983232 cites W2906589870 @default.
- W4378983232 cites W2915045717 @default.
- W4378983232 cites W2921732421 @default.
- W4378983232 cites W2922097266 @default.
- W4378983232 cites W2940721039 @default.
- W4378983232 cites W2946396870 @default.
- W4378983232 cites W2946787920 @default.
- W4378983232 cites W2947906476 @default.
- W4378983232 cites W2948394201 @default.
- W4378983232 cites W2962993994 @default.
- W4378983232 cites W2968213231 @default.
- W4378983232 cites W2976098773 @default.
- W4378983232 cites W2990217975 @default.
- W4378983232 cites W2991315710 @default.
- W4378983232 cites W2992302336 @default.
- W4378983232 cites W3003674510 @default.
- W4378983232 cites W3009927626 @default.
- W4378983232 cites W3013155981 @default.
- W4378983232 cites W3031990181 @default.
- W4378983232 cites W3036448124 @default.
- W4378983232 cites W3041632065 @default.
- W4378983232 cites W3043290180 @default.
- W4378983232 cites W3069402781 @default.
- W4378983232 cites W3084706547 @default.
- W4378983232 cites W3084774929 @default.
- W4378983232 cites W3092218908 @default.
- W4378983232 cites W3094984866 @default.
- W4378983232 cites W3095870326 @default.
- W4378983232 cites W3114757001 @default.
- W4378983232 cites W3129819192 @default.
- W4378983232 cites W3133743901 @default.
- W4378983232 cites W3133777875 @default.
- W4378983232 cites W3134905843 @default.
- W4378983232 cites W3158858900 @default.
- W4378983232 cites W3170928928 @default.
- W4378983232 cites W3180055976 @default.
- W4378983232 cites W3182706339 @default.
- W4378983232 cites W3189164715 @default.
- W4378983232 cites W3200685635 @default.
- W4378983232 cites W3202854056 @default.
- W4378983232 cites W3210689982 @default.
- W4378983232 cites W4200358663 @default.
- W4378983232 cites W4213193121 @default.
- W4378983232 cites W4224277380 @default.
- W4378983232 cites W4289313210 @default.
- W4378983232 cites W4289938567 @default.
- W4378983232 cites W4296365683 @default.
- W4378983232 cites W4296488344 @default.
- W4378983232 cites W4308444552 @default.
- W4378983232 cites W4313595964 @default.
- W4378983232 cites W4384625560 @default.
- W4378983232 doi "https://doi.org/10.21203/rs.3.rs-2981004/v1" @default.
- W4378983232 hasPublicationYear "2023" @default.
- W4378983232 type Work @default.
- W4378983232 citedByCount "0" @default.
- W4378983232 crossrefType "posted-content" @default.
- W4378983232 hasAuthorship W4378983232A5029041141 @default.
- W4378983232 hasAuthorship W4378983232A5045874646 @default.
- W4378983232 hasAuthorship W4378983232A5052738467 @default.
- W4378983232 hasAuthorship W4378983232A5087774354 @default.
- W4378983232 hasAuthorship W4378983232A5092065207 @default.
- W4378983232 hasBestOaLocation W43789832321 @default.
- W4378983232 hasConcept C107365816 @default.
- W4378983232 hasConcept C111919701 @default.
- W4378983232 hasConcept C11413529 @default.
- W4378983232 hasConcept C119857082 @default.
- W4378983232 hasConcept C12267149 @default.
- W4378983232 hasConcept C127413603 @default.
- W4378983232 hasConcept C150217764 @default.
- W4378983232 hasConcept C154945302 @default.
- W4378983232 hasConcept C159985019 @default.
- W4378983232 hasConcept C186108316 @default.
- W4378983232 hasConcept C192562407 @default.
- W4378983232 hasConcept C195975749 @default.
- W4378983232 hasConcept C41008148 @default.
- W4378983232 hasConcept C50644808 @default.
- W4378983232 hasConcept C523214423 @default.