Analysis and optimization of surface quality while machining high strength aluminium alloy

Patel, G C Manjunath; Lokare, Deepak; Chate, Ganesh R.; Parappagoudar, Mahesh B.; Nikhil, R.; Gupta, Kapil

doi:10.1016/j.measurement.2019.107337

Cited by 24 publications

(6 citation statements)

References 30 publications

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“…The control variables and operating range are finalized with the help of Ishikawa diagram and literature survey respectively [31]. Full factorial design, Central composite design and Box-Behnken designs are the popular response surface methodologies used to develop process models.…”

Section: Methodsmentioning

confidence: 99%

“…outputs), if lies at extreme levels of inputs may not be estimated properly with BBD. Therefore, regression models for machining of Al 7075 alloy in turning process are developed by utilizing experimental matrix based on CCD [31]. The input-output variables for both forward and reverse modelling of artificial neural networks are presented schematically in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Table 1 provides information on variables and their operating range used in conducting experiments. The regression equations developed, tested statistically and validated through test cases are used to generate training data [31]. Supervised learning ANNs, such as BPNN, GA-NN, ABC-NN, and RNN are trained with one thousand set of input-output data.…”

Section: Data Collection For Network Training and Testingmentioning

confidence: 99%

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Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

et al. 2020

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Intelligent manufacturing requires significant technological interventions to interface manufacturing processes with computational tools in order to dynamically mold the systems. In this era of the 4th industrial revolution, Artificial neural network (ANNs) is a modern tool equipped with a better learning capability (based on the past experience or history data) and assists in intelligent manufacturing. This research paper reports on ANNs based intelligent modelling of a turning process. The central composite design is used as a data-driven modelling tool and huge input-output is generated to train the neural networks. ANNs are trained with the data collected from the physics-based models by using back-propagation algorithm (BP), genetic algorithm (GA), artificial bee colony (ABC), and BP algorithm trained with selffeedback loop. The ANNs are trained and developed as both forward and reverse mapping models. Forward modelling aims at predicting a set of machining quality characteristics (i.e. surface roughness, cylindricity error, circularity error, and material removal rate) for the known combinations of cutting parameters (i.e. cutting speed, feed rate, depth of cut, and nose radius). Reverse modelling aims at predicting the cutting parameters for the desired machining quality characteristics. The parametric study has been conducted for all the developed neural networks (BPNN, GA-NN, RNN, ABC-NN) to optimize neural network parameters. The performance of neural network models has been tested with the help of ten test cases. The network predicted results are found in-line with the experimental values for both forward and reverse models. The neural network models namely, RNN and ABC-NN have shown better performance in forward and reverse modelling. The forward modelling results could help any novice user for off-line monitoring, that could predict the output without conducting the actual experiments. Reverse modelling prediction would help to dynamically adjust the cutting parameters in CNC machine to obtain the desired machining quality characteristics.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Collection For Network Training and Testingmentioning

confidence: 99%

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Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

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“…In product assembly, cylindrical features (dimension properties) are important quality characteristics that ensure the proper functioning of parts [ 38 , 39 ]. Cylindricity errors are treated as areas between two co-axial cylinders, among which all points of the part surface must lie [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

“…Cylindricity errors are treated as areas between two co-axial cylinders, among which all points of the part surface must lie [ 40 ]. JAYA, PSO, GA, and TLBO have been applied to simultaneously optimize the dimensional errors (surface roughness, cylindricity error, circularity error) in turning and drilling processes [ 38 , 39 ]. GA was applied to conduct a global search to minimize the deviation of building parts compared with STL facets of 3D geometry [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle Search Algorithms

et al. 2021

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High impact polystyrene (HIPS) material is widely used for low-strength structural applications. To ensure proper function, dimensional accuracy and porosity are at the forefront of industrial relevance. The dimensional accuracy cylindricity error (CE) and porosity of printed parts are influenced mainly by the control variables (layer thickness, shell thickness, infill density, print speed of the fused deposition modeling (FDM) process). In this study, a central composite design (CCD) matrix was used to perform experiments and analyze the complete insight information of the process (control variables influence on CE and porosity of FDM parts). Shell thickness for CE and infill density for porosity were identified as the most significant factors. Layer thickness interaction with shell thickness, infill density (except for CE), and print speed were found to be significant for both outputs. The interaction factors, i.e., shell thickness and infill density, were insignificant (negligible effect) for both outputs. The models developed produced a better fit for regression with an R2 equal to 94.56% for CE, and 99.10% for porosity, respectively. Four algorithms (bald eagle search optimization (BES), particle swarm optimization (PSO), RAO-3, and JAYA) were applied to determine optimal FDM conditions while examining six case studies (sets of weights assigned for porosity and CE) focused on minimizing both CE and porosity. BES and RAO-3 algorithms determined optimal conditions (layer thickness: 0.22 mm; shell thickness: 2 mm; infill density: 100%; print speed: 30 mm/s) at a reduced computation time equal to 0.007 s, differing from JAYA and PSO, which resulted in an experimental CE of 0.1215 mm and 2.5% of porosity in printed parts. Consequently, BES and RAO-3 algorithms are efficient tools for the optimization of FDM parts.

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Optimization of Machining Parameters for Material Removal Rate and Machining Time While Cutting Inconel 600 with Tungsten Carbide Textured Tools

Khan

Gupta

2020

Intelligent Manufacturing

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Analysis and optimization of surface quality while machining high strength aluminium alloy

Cited by 24 publications

References 30 publications

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle Search Algorithms

Optimization of Machining Parameters for Material Removal Rate and Machining Time While Cutting Inconel 600 with Tungsten Carbide Textured Tools

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