Prediction of Machining Characteristics using Artificial Neural Network in Wire EDM of Al7075 based In-situ Composite

Surya, Vishwa R.; Kumar, Kaushal; Keshavamurthy, R.; Ugrasen, G.; Ravindra, H.V.

doi:10.1016/j.matpr.2017.01.014

Cited by 30 publications

(11 citation statements)

References 16 publications

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“…The controlling factors indulged during the study were pulse-on and-off time, and current and bed speeds. The predicted parameters were found in the good agreement with the experimental ones within a range of 0.003~3.87% for material removal rate, 1.3~12.51% for surface roughness and 0.03~1.31% for dimensional accuracy [69]. Ugrasen, et al (2014) presented a study on optimization and influence of machining parameters on precision, roughness and volumetric material removal rate of manufactured parts in Wire-EDM process.…”

Section: Robotics Simulations and Machine Visionmentioning

confidence: 54%

“…In addition, ANN model has been implemented in the electric discharge machining (EDM) process. Surya, et al (2017) developed an ANN model to predict the machining parameters in case of Wire-EDM of Al7075+TiB 2 composites to attain maximum material removal rate, minimum dimensional errors and better surface roughness. The controlling factors indulged during the study were pulse-on and-off time, and current and bed speeds.…”

Section: Robotics Simulations and Machine Visionmentioning

confidence: 99%

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The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa¹,

Arif²

2021

Int J Signal Process Anal

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Machine learning based artificial neural networking (ANN) has been acknowledged to be an authentic way to accomplish intricate pattern identification and regression analysis without any obvious necessity to paradigm and resolve the primary physical models. ANN has been introduced and adopted in various fields of life based-on their key advantages including learning and adapting ability, parallel distributed computation, robustness, and many more. This review articles discussesthe working principle, classes and structure of ANN. The potential applicable areas of ANN, the challenges and their solutions that have been identified through literature survey, are discussed and summarized. KeywordsMachine learning, Artificial neural networking (ANN), Potential applications of ANN, Challenges and solutions in ANN chine learning algorithms to detect anomalies correlations while performing test searching for patterns across the various data feeds, so facilitating the manufacturing process and making it easier for people to work [1].Although, there are tremendous applications of AI in various aspects of manufacturing systems but expert systems are the most commonly used application of AI. Expert systems are commonly used in designing, scheduling, process planning,

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Section: Robotics Simulations and Machine Visionmentioning

confidence: 54%

Section: Robotics Simulations and Machine Visionmentioning

confidence: 99%

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa¹,

Arif²

2021

Int J Signal Process Anal

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“…Furthermore, the study shows that high discharge energy causes surface defects such as cracks, craters, thick recast layer, micropores, pinholes, residual stresses, and debris [18,19]. For example, in [20], the attention is focused on the prediction and comparison of machining performances during wire EDM (WEDM) of Al7075-TiB2 through an ANN model. In all these cases, the models could be considered as characterized by some limitations since they have validity only for wire configuration and it is difficult to modify to expand their applicability to drilling, sinking or milling configurations.…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling

et al. 2021

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Artificial Neural Network (ANN), together with a Particle Swarm Optimization (PSO) and Finite Element Model (FEM), was used to forecast the process performances for the Micro Electrical Discharge Machining (micro-EDM) drilling process. The integrated ANN-PSO methodology has a double direction functionality, responding to different industrial needs. It allows to optimize the process parameters as a function of the required performances and, at the same time, it allows to forecast the process performances fixing the process parameters. The functionality is strictly related to the input and/or output fixed in the model. The FEM model was based on the capacity of modeling the removal process through the mesh element deletion, simulating electrical discharges through a proper heat-flux. This paper compares these prevision models, relating the expected results with the experimental data. In general, the results show that the integrated ANN-PSO methodology is more accurate in the performance previsions. Furthermore, the ANN-PSO model is faster and easier to apply, but it requires a large amount of historical data for the ANN training. On the contrary, the FEM is more complex to set up, since many physical and thermal characteristics of the materials are necessary, and a great deal of time is required for a single simulation.

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“…Dain Thomas et al 16 , developed a second-order regression model using RSM and found that T ON and WT play a major role in the surface roughness. V. R. Surya et al 17 predicted the machining characteristics of an Al 7075-TiB2 composite using ANN for the maximum MRR, minimum Dimensional Error (DE) and better surface finish. In this study the control factors considered were T ON , T OFF , Current and Bed Speed based on Taguchi's L 27 orthogonal array.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation for the optimization of the WEDM process parameters to obtain the minimum surface roughness of the Al 7075 aluminium alloy employed with a zinc-coated wire using RSM and GA

Shanthi¹,

Raman²,

Ramasamy³

2019

Mater. Tehnol.

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Wire Electrical Discharge Machining (WEDM) is widely used for machining conductive materials of intricate, complex and challenging shapes in the field of aerospace, die and mould making, automobile industries and the medical field. Proper selection of the WEDM process parameters can give good responses. Out of the various process responses, to achieve the minimum surface roughness is very difficult, due to arcing by the electrical discharge. The present investigation has been made to optimize the process parameters of WEDM during machining the Al 7075 aluminium alloy with zinc-coated copper wire using the Response Surface Methodology (RSM). Four input process parameters of WEDM, i.e., Pulse On Time, Pulse Off Time, Wire Feed and Wire Tension, were chosen as the process variables to study the process performance and to obtain the minimum surface roughness. An analysis of variance (ANOVA) was carried out to study the effect of the process parameters on the surface roughness and a mathematical model was developed. The model has been verified and checked for adequacy. The best fit surface roughness (Ra) value predicted is 1.048 μm for a Pulse On (TON) value of 120 μsec, a Pulse Off (TOFF) value of 58 μsec, a Wire Feed (WF) of 3 m/min and a Wire Tension (WT) value of 9 gm. Furthermore, it is observed that with an increase in the pulse-on time the Surface Roughness also increases, and an increase in the pulse-off time and the wire tension, the Surface Roughness decreases. Wire Feed does not influence much on the Surface Roughness.

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Prediction of Machining Characteristics using Artificial Neural Network in Wire EDM of Al7075 based In-situ Composite

Cited by 30 publications

References 16 publications

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling

Experimental investigation for the optimization of the WEDM process parameters to obtain the minimum surface roughness of the Al 7075 aluminium alloy employed with a zinc-coated wire using RSM and GA

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