Hard turning of AISI D2 steel with cubic boron nitride cutting inserts

Kumar, Satish; Tamilselvan, P.; Feroskhan, M.; Doss, Arockia Selvakumar Arockia; Sasikumar, M.; Elango, M.; Sivarajan, S.

doi:10.1016/j.matpr.2022.07.338

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…More so, it was recorded that feed rate has a larger effect (90.2%) on surface roughness than nose radius, cutting insert, depth of cut, and cutting speed. Kumar et al [29] investigated process parameters on cutting force and surface roughness on the hard turning of AISI D2 using CBN inserts. Results show that the feed rate has a greater effect (56%) on cutting force than cutting speed while cutting speed has a greater effect (72%) on surface roughness than feed rate.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the contrary views of results, on which of process parameters (cutting speed, depth of cut, and feed rate) influences surface roughness most; as reported by [28,32,33], the feed rate is most significant compared to the depth of cut and cutting speed. Furthermore, [29] reported that cutting speed was the most significant factor, while [2] and [45] suggested depth of cut as most significant compared to feed rate and cutting speed. Therefore, it is needful to investigate and make contributions to these findings.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Preprint

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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 Ra 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Preprint

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Investigation on tool wear mechanisms and machining tribology of hardened DC53 steel through modified CBN tooling geometry in hard turning

Hassan

Khan

Naveed

et al. 2023

Int J Adv Manuf Technol

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DC 53 steel has emerged as a possible replacement of AISI D2 steel possessing competitive hardness and better toughness. In the current work, turning of DC 53 steel was conducted via Xcel modified inserts by varying workpiece hardness levels (40 and 60 HRC), cutting speed (130 and 160 m/min), feed rate (0.07 and 0.112 mm/rev), and depth of cut (0.07 and 0.17 mm). A two-level 4-factor full factorial design was employed entailing 16 runs. An analysis of variance (ANOVA) was conducted to statistically analyze the effect and contributions of input parameters on response variables namely tool life, surface roughness, volume of material removed, power consumption, and machining zone temperature. Results show that the tool life, surface roughness, volume of material removed, and machining zone temperature are primarily affected by the hardness of DC53 with PCRs of ~ 96%, ~ 25%, ~ 62%, and ~ 25%, respectively. At a 40 HRC hardness value, true crater wear was observed due to continuous chips sliding at the rake face while for the workpiece having a 60 HRC, discontinuous chip formation produced less prominent crater wear. SEM images revealed complete delamination of the coating from the tool surface with adhesion and attrition wear identified as the main wear mechanisms. The formation of a groove pattern was also noticed on the flank face. The minimum surface roughness was 0.90 µm-Ra for the workpiece having a 40 HRC hardness level, and the same value was obtained for 60 HRC as well. The threshold value of the feed rate for the excellent performance of these inserts was less than 0.20 mm/rev. Additionally, the turning process proved to be productive for this material along with a lower surface roughness value in comparison to the wire EDM process.

show abstract

Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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 all machine learning models gave excellent predictive accuracy with an average MAPE value of 7.38%. The validation tests were also statistically significant, with ANFIS and ANN having MAPE values of 9.98% and 3.43%, respectively. Additional validation tests for the models with new experimental data indicate average R, RMSE, and MAPE values of 0.78, 0.19, and 36.17%, respectively, which are satisfactory. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Rɑ, among the process parameters, with 92% influence, and optimal cutting conditions were 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.

show abstract

Hard turning of AISI D2 steel with cubic boron nitride cutting inserts

Cited by 5 publications

References 17 publications

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Investigation on tool wear mechanisms and machining tribology of hardened DC53 steel through modified CBN tooling geometry in hard turning

Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool

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