Optimisation and influence of process parameters for machining oil hardened non-shrinking steel (OHNS) in wire-EDM

Kuruvila, Nixon; Ravindra, H.V.

doi:10.1504/ijmmm.2012.050435

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…III. LITERATURE REVIEW N. Kuruvila and H. Ravindra in [3] evaluated Non-convectional Machining (NCM) processes and classified them with respect on the conditions of energy incorporated in machining. These classifications include mechanical machining procedure, which is a process where mechanical energy is applied in the erosion of unwanted materials based on the application high-speed water jet and abrasives.…”

Section: Background Analysismentioning

confidence: 99%

Critical Analysis of the Selection of Non-Conventional Machining Processes

2021

JMC

View full text Add to dashboard Cite

Non-Convectional Machining (NCM) processes are used widely to produce accurate and intricate material shape such as titanium, stainless steels and resistant alloy that are of high strength, fiber-based composite, refractories and ceramics. The production of more complex shapes of materials using convectional machining processes is considered to be challenging. This research paper focusses on the section of the most effective NCM process. The ‘choice’ of the most considerable NCM process for a particular application could be seen as a multi-technique for making proper choices for diverse or conflicting approaches. To aid the process of choosing, various NCM techniques have been proposed in this research. This contribution focuses on the usage of unexplored NCM and Multi-Feature Decision-Making (MFDM) ‘choice’ issues.

show abstract

Section: Background Analysismentioning

confidence: 99%

Critical Analysis of the Selection of Non-Conventional Machining Processes

2021

JMC

View full text Add to dashboard Cite

show abstract

“…Moreover, a good amount of studies reported the optimization of WEDM process performance by regulating the process parameters. 4–10 For instance, Puri and Bhattacharyya 11 reported the influence of parametric variations associated with the large set of input parameters on three important process outcomes namely, cutting speed, geometrical inaccuracy, and surface features. Similarly, Tosun and Pihtili 12 investigated the impact of pulse duration, wire-speed, dielectric flushing pressure, and open-circuit voltage on wire crater size.…”

Section: Introductionmentioning

confidence: 99%

Data-driven probabilistic performance of Wire EDM: A machine learning based approach

Saha

Gupta

Maity

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

The wire electric discharge machining (WEDM) is a potential alternative over the conventional machining methods, in terms of accuracy and ease in producing intricate shapes. However, the WEDM process parameters are exposed to unavoidable and unknown sources of uncertainties, following their inevitable influence over the process performance features. Thus, in the present work, we quantified the role of parametric uncertainty on the performance of the WEDM process. To this end, we used the practically relevant noisy experimental dataset to construct the four different machine learning (ML) models (linear regression, regression trees, support vector machines, and Gaussian process regression) and compared their goodness of fit based on the corresponding R2 and RMSE values. We further validated the prediction capability of the tested models by performing the error analysis. The model with the highest computational efficiency among the tested models is then used to perform data-driven uncertainty quantification and sensitivity analysis. The findings of the present article suggest that the pulse on time ( Ton) and peak current (IP) are the most sensitive parameters that influence the performance measures of the WEDM process. In this way, the current study achieves two goals: first, it proposes a predictive framework for determining the performance features of WEDM for unknown design points, and second, it reports data-driven uncertainty analysis in the light of parametric perturbations. The observations reported in the present article provide comprehensive computational insights into the performance characteristics of the WEDM process.

show abstract