Publication2026
Machine Learning-Based Prediction Modelling of EDM Application on Ti6Al4V Alloy Using 3D-Printed SS316L Electrodes
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- 2026 1st International Conference on Artificial Intelligence and Machine Learning in Communication and Power Systems Aimlcps 2026
- Published
- 1 January 2026
- DOI
- 10.1109/aimlcps68702.2026.11542286
- Scopus ID
- 105042442034
- Authors
- 4
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Machine learning prediction models for EDM using 3D-printed electrodes demonstrate manufacturing innovation integrating additive manufacturing and AI-driven optimization
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.2 Industry and manufacturing
3D-printed electrode manufacturing with ML-based process optimization advances precision machining capability and manufacturing efficiency for industrial production
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Machine Learning-Based Prediction Modelling of EDM Application on Ti6Al4V Alloy Using 3D-Printed SS316L Electrodes”. Kortex.