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Machine learning induction of a model for online parameter selection in EDM rough machining

https://doi.org/10.1007/s00170-008-1532-3
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00170-004-2356-4openalex-snapshot
Detection of the eroding surface in the EDM process based on the current signal in the gap
inferredcites →https://doi.org/10.1016/0166-3615(91)90027-7openalex-snapshot
Identifying the grinding process by means of inductive machine learning
inferredcites →https://doi.org/10.1016/0924-0136(94)01439-8openalex-snapshot
Decision support for manufacturing using artificial neural-networks
inferredcites →https://doi.org/10.1016/j.ijmachtools.2003.10.013openalex-snapshot
A model for detection of the eroding surface based on discharge parameters
inferredcites →https://doi.org/10.1016/j.ijmachtools.2004.08.016openalex-snapshot
Tool breakage detection using support vector machine learning in a milling process
inferredcites →https://doi.org/10.1016/j.jmatprotec.2003.11.059openalex-snapshot
On-line selection of rough machining parameters
inferredcites →https://doi.org/10.1016/j.jmatprotec.2004.02.008openalex-snapshot
Micro-EDM—recent developments and research issues
inferredcites →https://doi.org/10.1016/j.jmatprotec.2004.04.160openalex-snapshot
Development of machine learning strategy for acquiring on-line machining skills during turning process
inferredcites →https://doi.org/10.1016/s0007-8506(07)60181-4openalex-snapshot
Advanced Pulse Discriminating System for EDM Process Analysis and Control
inferredcites →https://doi.org/10.1016/s0007-8506(07)60795-1openalex-snapshot
WEDM-Adaptive Control with a Multiple Input Model for Indentification of Workpiece Height
inferredcites →https://doi.org/10.1016/s0007-8506(07)61195-0openalex-snapshot
Analysis and Adaptive Control of EDM Sinking Process Using the Ignition Delay Time and Fall Time as Parameter
inferredcites →https://doi.org/10.1016/s0007-8506(07)61820-4openalex-snapshot
Statistical Model for an Adaptive Control of EDM-Process
inferredcites →https://doi.org/10.1016/s0007-8506(07)62195-7openalex-snapshot
WEDM Identification and Adaptive Control for Variable-Height Components
inferredcites →https://doi.org/10.1016/s0166-3615(00)00056-7openalex-snapshot
Using inductive machine learning to support decision making in machining processes
inferredcites →https://doi.org/10.1016/s0890-6955(96)00033-8openalex-snapshot
A fuzzy pulse discriminating system for electrical discharge machining
inferredcites →https://doi.org/10.1016/s0924-0136(01)01252-3openalex-snapshot
A neural network approach for the on-line estimation of workpiece height in WEDM
inferredcites →https://doi.org/10.1063/1.343994openalex-snapshot
Theoretical models of the electrical discharge machining process. I. A simple cathode erosion model
inferredcites →https://doi.org/10.1063/1.343995openalex-snapshot
Theoretical models of the electrical discharge machining process. II. The anode erosion model
inferredcites →https://doi.org/10.1504/ijmpt.2007.013128openalex-snapshot
Machining parameters selection for varying surface in EDM
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.2947443openalex-extracted
Experimental Study of Process Parameters through Dissimilar Form of Electrodes in EDM Machining
inferred← citeshttps://doi.org/10.1007/s00170-015-7316-7openalex-snapshot
Spark location adaptive process control in meso-micro EDM
inferred← citeshttps://doi.org/10.1007/s00170-024-13129-2openalex-snapshot
Effects of machining parameters on spectral entropy of acoustic emission signals in the electro erosion
inferred← citeshttps://doi.org/10.1007/s42452-025-07385-4openalex-snapshot
A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys
inferred← citeshttps://doi.org/10.1016/j.jmapro.2025.08.031openalex-snapshot
In-situ process monitoring and control in EDM: A review
inferred← citeshttps://doi.org/10.1016/j.jmrt.2023.09.127openalex-snapshot
Exploring the intricacies of machine learning-based optimization of electric discharge machining on squeeze cast TiB2/AA6061 composites: Insights from morphological, and microstructural aspects in the surface structure analysis of recast layer formation and worn-out analysis
inferred← citeshttps://doi.org/10.1504/ijmpt.2007.013128openalex-snapshot
Machining parameters selection for varying surface in EDM
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1092290/v1openalex-snapshot
A Response Surface Methodology to Optimize Multiple Discharge Step Parameters for Sinking Electrical Discharge Machining
inferred← citeshttps://doi.org/10.5897/sre10.1116openalex-snapshot
Modeling of machining parameters of Ti-6Al-4V for electric discharge machining: A neural network approach
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