Optimization of End Milling Process for Al2024-T4 Aluminum by Combined Taguchi and Artificial Neural Network Process

Sahare, Shilpa B.; Untawale, Sachin; Chaudhari, Sharad S.; Shrivastava, Rajeev; Kamble, Prashant D.

doi:10.1007/978-981-10-5699-4_49

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…http: //www.ijmp.jor.br v. 11, n. 1, January-February 2020ISSN: 2236 In this paper, focus is on the selection and optimization of suitable components for remanufacturing. Optimization may be defined as the minimization of unnecessary traits and maximization of necessary ones, to find the most effective and highest attainable performance (SAHARE et al, 2018). In this study, different parameters were considered which affects the viability in remanufacturing as well as impacts the recovery of a product during remanufacturing.…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Feasibility analysis of design for remanufacturing in bearing using hybrid fuzzy-topsis and taguchi optimization

Fegade

Shrivastava

Kale

et al. 2020

Ind. Jour. Manag. & Prod.

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The tremendous advancement in technology, productivity and improved standard of living has come at the cost of environmental deterioration, increased energy and raw material consumption. In this regard, remanufacturing is viable option to reduce energy usage, carbon footprint and raw material usage. In this manuscript, using computational intelligence techniques we try to determine the feasibility of remanufacturing in case of roller bearings. We collected used N308 bearings from 5 different Indian cities. Using Fuzzy-TOPSIS, we found that the roundness, surface roughness and weight play a vital role in design for remanufacturing of roller bearings. Change in diameter, change in thickness and change in width showed minimal influence. We also used Taguchi analysis to reassess the problem. The roundness of inner and outer race was found to be the most influential parameters in deciding the selection of bearing for remanufacturing. The results suggest the bearing designer to design the bearing in such a way that roundness of both races will be taken cared while manufacturing a bearing. However, using Taguchi the weight of the rollers was found to be of least influence. Overall, the predictions of Taguchi analysis were found to be similar to Fuzzy-TOPSIS analysis.

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Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Feasibility analysis of design for remanufacturing in bearing using hybrid fuzzy-topsis and taguchi optimization

Fegade

Shrivastava

Kale

et al. 2020

Ind. Jour. Manag. & Prod.

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“…The response parameters were material removal rates, surface roughness, and cutting force. The findings demonstrated that ANN combined with Taguchi's method was appropriate for amending (Sahare et. al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of emission parameters of diesel engine fueled with various plastic pyrolysis oils

Modi,

Patel

2023

Int. J. Eng. Sci. Tech

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Plastic has various advantages and is becoming more ubiquitous by the year, yet recycling and disposal remain challenges for the world. We conduct tests in this study to evaluate the appropriateness of various Plastic Pyrolysis Oils (PPOs) made from Low Density Polyethylene (LDPE), High Density Polyethylene (HDPE), and Polypropylene (PP) for diesel engine fuel. Determine which type of plastic pyrolysis oil would function best as an alternative fuel in an experimental investigation. The elemental composition of pyrolysis oil generated from LDPE, HDPE, and PP to diesel was assessed, tabulated, and compared in this study. This study examines emission characteristics (Nitrogen Oxide (NOX), Hydro Carbon (HC), and Carbon Monoxide (CO)) for Diesel, LDPE, HDPE and PP in CI engines using different injection pressure, compression ratio, and load in a specific configuration. In the result of experimental work the CO emission range for diesel and LDPE is exceeded only during overload situations or when the engine load is 100%. The average NOX emission is lowest when PP is used in comparison to other fuels, and the highest NOX emission occurs when LDPE is utilised as a fuel. Increased fumigation rates may be to blame for the rising hydrocarbon content of plastic oil. Unburned hydrocarbon levels are lower at lighter loads due to increased oxygen availability. The use of this whole pyrolysis oil results in a significant decrease in Nitrogen Oxide (NOX), Hydro Carbon (HC), and Carbon Monoxide (CO) exhaust emissions. With the help of this current study. one can solve plastic waste disposal problem and also find any unknown fuel (PPOs) that can be used as an alternative fuel for CI engine or not.

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“…Kilickap et al [18] investigated the relationship between cutting parameters such as cutting speeds, feed rate, and depth of cut using optimization techniques such as RSM and ANN. Sahare et al [19] optimized the end milling process for Al2024-T4 workpiece material by using the ANN combined with Taguchi method. Yeganefar et al [20] predicted and optimized the surface roughness and cutting forces in milling of Al7075-T6 by using regression analysis, support vector regression, artificial neural network, and multiobjective genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

Eser

Ayyıldız

et al. 2021

Advances in Materials Science and Engineering

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This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

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Optimization of End Milling Process for Al2024-T4 Aluminum by Combined Taguchi and Artificial Neural Network Process

Cited by 8 publications

References 5 publications

Feasibility analysis of design for remanufacturing in bearing using hybrid fuzzy-topsis and taguchi optimization

Feasibility analysis of design for remanufacturing in bearing using hybrid fuzzy-topsis and taguchi optimization

Experimental investigation of emission parameters of diesel engine fueled with various plastic pyrolysis oils

Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

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