Optimization of Process Parameters during Pressure Die Casting of A380: a Silicon-Based Aluminium Alloy Using GA &amp; Fuzzy Methodology

Electrical discharge machining is an essential process in the domain of micromachining. However, many issues need to be solved to implement it in the industrial field. Increasing the machining rate still remains a challenging task in case of micro electrical discharge machining. It becomes impossible to machine a microfeature at a larger depth. Numerous investigators have investigated the positive effect of assistance such as magnetic field and ultrasonic vibration. This paper the discusses machining performance by simultaneously applying the ultrasonic vibration and magnetic field to the machining zone in micro-electrical discharge machining. The process performance is analyzed by measuring the performance characteristics such as material removal rate and taper of the microfeature. The results confirmed that the cumulative effect of each assistance ends in a better material removal rate and low taper of the microfeature.

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“…The UV is at two levels while the remaining factors are at three levels. 32,33 The input parameters with different levels are shown in Table 3.…”

Section: Methodsmentioning

confidence: 99%

Micro-EDMing of SKD-5 die steel stimulated with magnetic field and ultrasonic vibration

Singh

Sharma

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Finally, in a number of previous papers on metal manufacturing, several optimization techniques, such as the genetic algorithm, [131][132][133][134] particle swarm optimization, 46,135 Bayesian optimization, [136][137][138] and even statistical approaches like response surface methodology, 17,139,140 Taguchi's design of experiment, and analysis of variance (ANOVA), [141][142][143] have been used. The main difference between these methods and the RL is that the former do not ''learn from experience.''…”

Section: Process Optimizations For Manufacturing Mmcs Using Reinforcement Learningmentioning

confidence: 99%

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

Kordijazi

Zhao

Zhang

et al. 2021

JOM

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In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC). We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.

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“…These methods have been found to be more realistic than statistical methods and are based on a learning algorithm. Gupta et al (2021) used a learning algorithm-based AI strategy to find the best combination of input parameters and compared it to the Taguchi statistical method. When compared to the Taguchi methodology, genetic algorithm (GA) showed a 58.28% reduction in pressure die casting failures.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers use these techniques all the time, and they are even used in real-world production scenarios. AI approaches are playing a critical role in real-time optimization of manufacturing processes during mass production in the Industry 4.0 (Gupta et al , 2021) idea.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid approach GREG-fuzzy-GA for minimizing work piece temperature during 2.5D milling of Inconel625 super alloy

Kumar

Gupta

Kumar

et al. 2023

WJE

Self Cite

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Purpose Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls. Design/methodology/approach Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization. Findings SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized. Originality/value The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

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Optimization of Process Parameters during Pressure Die Casting of A380: a Silicon-Based Aluminium Alloy Using GA & Fuzzy Methodology

Cited by 9 publications

References 26 publications

Micro-EDMing of SKD-5 die steel stimulated with magnetic field and ultrasonic vibration

Micro-EDMing of SKD-5 die steel stimulated with magnetic field and ultrasonic vibration

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

A novel hybrid approach GREG-fuzzy-GA for minimizing work piece temperature during 2.5D milling of Inconel625 super alloy

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