Effect of cutting parameters on the dimensional accuracy and surface finish in the hard turning of MDN250 steel with cubic boron nitride tool, for developing a knowledged base expert system

Yousefi, Sasan; Zohoor, Mehdi

doi:10.1186/s40712-018-0097-7

Cited by 63 publications

(24 citation statements)

References 18 publications

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“…The minimum was consistently at a depth of cut of 0.2 mm. The increasing surface roughness at lower depths of cut is mainly due to the large nose radius of the cutting insert used in these experiments because, at shallower depths of cut, the material ploughed rather than forming chips resulting in poor surface roughness as mentioned in [ 68 ]. However, in the case of larger depth of cut, there are benefits of having a large nose radius for the cutting insert such as improved surface roughness [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

Precision Hard Turning of Ti6Al4V Using Polycrystalline Diamond Inserts: Surface Quality, Cutting Temperature and Productivity in Conventional and High-Speed Machining

et al. 2020

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This article presents the results of an experimental investigation into the machinability of Ti6Al4V alloy during hard turning, including both conventional and high-speed machining, using polycrystalline diamond (PCD) inserts. A central composite design of experiment procedure was followed to examine the effects of variable process parameters; feed rate, cutting speed and depth of cut (each at five levels) and their interaction effects on surface roughness and cutting temperature as process responses. The results revealed that cutting temperature increased with increasing cutting speed and decreasing feed rate in both conventional and high-speed machining. It was found that high-speed machining showed an average increase in cutting temperature of 65% compared with conventional machining. Nevertheless, high-speed machining showed better performance in terms of lower surface roughness despite using higher feed rates compared to conventional machining. High-speed machining of Ti6Al4V showed an improvement in surface roughness of 11% compared with conventional machining, with a 207% increase in metal removal rate (MRR) which offered the opportunity to increase productivity. Finally, an inverse relationship was verified between generated cutting temperature and surface roughness. This was attributed mainly to the high cutting temperature generated, softening, and decreasing strength of the material in the vicinity of the cutting zone which in turn enabled smoother machining and reduced surface roughness.

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Section: Resultsmentioning

confidence: 99%

Precision Hard Turning of Ti6Al4V Using Polycrystalline Diamond Inserts: Surface Quality, Cutting Temperature and Productivity in Conventional and High-Speed Machining

et al. 2020

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“…It not only gives the best outcome (maximum value) but also the worst outcome (minimum value). Closeness coefficient is derived to find out the best and worst-performing result [51][52][53]. TOPSIS is considered simple and believed to be more expressible than other multi-response optimisation methods.…”

Section: Analysis Of Chip Morphologymentioning

confidence: 99%

“…The CCo-rank is estimated according to their results data. Highest CCo indicates the best combination of input setting of parameters among the others [51][52]. Also, mean CCo is estimated as shown in Table 9 and from this higher mean value of each individual term represents their optimum level.…”

Section: Ar Xrk1 Xrk2 …Xrykmentioning

confidence: 99%

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

Pandey

Kumar

Sahoo

et al. 2020

IJAME

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The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

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“…With the continuous development of data mining technology, artificial intelligence methods such as neural network [23], fuzzy control [24], and expert system [25] have become more and more popular. Among them, support vector machine (SVM) is an efficient learning machine based on statistical learning theory and structural risk minimization principle proposed by Vapnik.…”

Section: Introductionmentioning

confidence: 99%

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Yan

2021

Metals

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Traditional mechanical properties prediction models are mostly based on experience and mechanism, which neglect the linear and nonlinear relationships between process parameters. Aiming at the high-dimensional data collected in the complex industrial process of steel production, a new prediction model is proposed. The multidimensional support vector regression (MSVR)-based model is combined with the feature selection method, which involves maximum information coefficient (MIC) correlation characterization and complex network clustering. Firstly, MIC is used to measure the correlation between process parameters and mechanical properties, based on which a complex network is constructed and hierarchical clustering is performed. Secondly, we evaluate all parameters and select a representative one for each partition as the input of the subsequent model based on the centrality and influence indicators. Finally, an actual steel production case is used to train the MSVR prediction model. The prediction results show that our proposed framework can capture effective features from the full parameters in terms of higher prediction accuracy and is less time-consuming compared with the Pearson-based subset, full-parameter subset, and empirical subset input. The feature selection method based on MIC can dig out some nonlinear relationships which cannot be found by Pearson coefficient.

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Effect of cutting parameters on the dimensional accuracy and surface finish in the hard turning of MDN250 steel with cubic boron nitride tool, for developing a knowledged base expert system

Cited by 63 publications

References 18 publications

Precision Hard Turning of Ti6Al4V Using Polycrystalline Diamond Inserts: Surface Quality, Cutting Temperature and Productivity in Conventional and High-Speed Machining

Precision Hard Turning of Ti6Al4V Using Polycrystalline Diamond Inserts: Surface Quality, Cutting Temperature and Productivity in Conventional and High-Speed Machining

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

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