Assessment of CVD- and PVD-Coated Carbides and PVD-Coated Cermet Inserts in the Optimization of Surface Roughness in Turning of AISI 1045 Steel

Paese, Evandro; Geier, Martin; Rodrigues, Fabiano R.; Mikołajczyk, Tadeusz; Mia, Mozammel

doi:10.3390/ma13225231

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…The use of trial-and-error is one of the options available to optimize the associated parameters, but this approach is not suitable for current industries using complex manufacturing processes, as it is time-consuming and costly [ 3 ]. Therefore, applying optimisation methods such as the Response Surface Methodology (RSM), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Taguchi is a good option to optimise the setting of processing parameters to mould parts with better quality [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials

et al. 2021

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Achieving good quality of products from plastic injection moulding processes is very challenging, since the process comprises many affecting parameters. Common defects such as warpage are hard to avoid, and the defective parts will eventually go to waste, leading to unnecessary costs to the manufacturer. The use of recycled material from postindustrial waste has been studied by a few researchers. However, the application of an optimisation method by which to optimise processing parameters to mould parts using recycled materials remains lacking. In this study, Response Surface Methodology (RSM) and Particle Swarm Optimisation (PSO) methods were conducted on thick plate parts moulded using virgin and recycled low-density polyethylene (LDPE) materials (100:0, 70:30, 60:40 and 50:50; virgin to recycle material ratios) to find the optimal input parameters for each of the material ratios. Shrinkage in the x and y directions increased in correlation with the recycled ratio, compared to virgin material. Meanwhile, the tensile strength of the thick plate part continued to decrease when the recycled ratio increased. R30 (70:30) had the optimum shrinkage in the x direction with respect to R0 (100:0) material where the shrinkage increased by 24.49% (RSM) and 33.20% (PSO). On the other hand, the shrinkage in the y direction for R30 material increased by 4.48% (RSM) and decreased by 2.67% (PSO), while the tensile strength of R30 (70:30) material decreased by 0.51% (RSM) and 2.68% (PSO) as compared to R0 (100:0) material. Validation tests indicated that the optimal setting of processing parameter suggested by PSO and RSM for R0 (100:0), R30 (70:30), R40 (60:40) and R50 (50:50) was less than 10%.

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Section: Introductionmentioning

confidence: 99%

“…RSM is among the optimisation methods that have been used by many researchers [ 6 , 7 , 8 , 9 , 10 ]. Sudsawat and Sriseubsai [ 10 ] conducted research using the RSM optimisation approach to create optimal process settings to minimise warpage and volume shrinkage.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials

et al. 2021

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“…The results showed that increased feed results in increased surface roughness, while increasing the cutting speed slightly improves the surface quality. Paese et al [23] performed an experimental investigation based on ANOVA and RSM techniques to determine how the factors such as cutting speed, feed, depth of cut, corner radius, substrate and coating method influence surface quality. The PVD-coated inserts in combination with lower feed and higher corner radius generated the lowest surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Surface Roughness Based on Turning Parameters and Insert Geometry

Vukelić¹,

Prica²,

Ivanov³

et al. 2022

Int. j. simul. model.

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This study is focused on dry longitudinal turning of AISI steel using CVD coated cutting inserts. The machining was conducted at different levels of cutting speed, feed, depth of cut, corner radius, rake, inclination and approach angles. Surface roughness was measured after each experiment, and statistical analysis was used to derive an empirical, regression model for arithmetical mean surface roughness. The regression model was used to theoretically minimize surface roughness, followed by additional verification experiments. The 95 % confidence interval constructed using ten additional batteries of experiments, contained the theoretically predicted minimum roughness of Ra = 0.238 μm. The mean absolute prediction error of the optimal roughness equals 0.006 μm. The results reveal practical applicability of the developed model.

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“…Paese et al [8] evaluated the performance of CVD, PVD, and cermet inserts after turning the AISI 1045 carbons steel shaft by using ANOVA analysis and response surface methodology. The cermet and PVD coated inserts generated minimum surface roughness compared with the CVD insert.…”

Section: Introductionmentioning

confidence: 99%

Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions

et al. 2022

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Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were divided randomly into three subsets for training, validation, and testing the developed hybrid intelligent model. ANOVA results revealed that feed rate is highly affected by the surface roughness followed by the depth of cut. One-way ANOVA, including a Post-Hoc test, was used to evaluate the performance of three developed models. The hybrid model of Artificial Neural Network-Gravitational Search Algorithm (ANN−GSA) has outperformed Artificial Neural Network (ANN) and Artificial Neural Network-Genetic Algorithm (ANN−GA) models. ANN−GSA achieved minimum testing mean square error of 7.41 × 10−13 and a maximum R-value of 1. Further, its convergence speed was faster than ANN−GA. GSA proved its ability to improve the performance of BPNN, which suffers from local minima problems.

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Assessment of CVD- and PVD-Coated Carbides and PVD-Coated Cermet Inserts in the Optimization of Surface Roughness in Turning of AISI 1045 Steel

Cited by 8 publications

References 31 publications

Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials

Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials

Optimization of Surface Roughness Based on Turning Parameters and Insert Geometry

Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions

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