Experimental Investigation on Recast Layer and Surface Roughness on Aluminum 6061 Alloy During Magnetic Field Assisted Powder Mixed Electrical Discharge Machining
Source abstract
Abstract
Surface integrity aspects have been recognized as key research areas for manufacturing industries for determining the quality, longevity and reliability of machined engineering components. Magnetic field assisted powder mixed electrical discharge machining (MFAPM-EDM) is technological advancement in EDM process where the performance and stability of process increases by adding the suitable conductive powder in dielectric and using magnetic field. Aluminum 6061 alloy as workpiece was selected due to growing use in aviation, automotive, naval industries. In the present work, the study was conducted on EDM machined aluminum 6061 alloy with aluminum powder added in EDM oil and using magnetic field to analyze the effect of machining parameters on surface roughness, recast layer thickness and surface morphology. The input machining parameters, namely peak current, pulse on duration, pulse off duration, magnetic field and concentration of powder have been varied during the MFAPM-EDM process. Box–Behnken design approach of response surface methodology (RSM) was employed for experimental design to carry out the experiments. Quadratic model for predicting the surface roughness and recast layer thickness was developed using analysis of variance and regression analysis. Improvement in surface roughness and recast layer thickness were observed on machining with MFAPM-EDM process. Surface roughness and recast layer thickness were mostly influence by peak current and pulse on duration followed by concentration of powder and magnetic field. The surface morphology witness lesser cracks, voids, crater and molten debris particles present on the surface of machined aluminum 6061 alloy.
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Research context
OpenAlex topics: Advanced Machining and Optimization Techniques · Advanced Surface Polishing Techniques · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
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