ArticleHeliyon2024
Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of nicorandil in preventing contrast-induced nephropathy in patients undergoing cardiac catheterization procedures: an updated systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
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- A multi-barrier defence strategy using plant-derived antifungals against mycotoxigenic fungi in livestock feed.Archives of microbiology · 2026Review
- Gene regulatory and biomolecular networks and their multifaceted biotechnological applications.World journal of microbiology & biotechnology · 2026Review
- Bioconversion of chicken feather waste: optimizing keratinase production by Pseudomonas aeruginosa PA1045 and Alcaligenes faecalis IHB B 6507, characterization and application as organic fertilizers.International microbiology : the official journal of the Spanish Society for Microbiology · 2025Article
- Bentonite clay in aflatoxin mitigation: advances, efficiency and perspectives.Archives of microbiology · 2025Review
- The molecular mechanisms of quinolone resistance in Salmonella: a review.Archives of microbiology · 2025Review
- Comparative analysis of physical, morphological, tensile and thermal stability characteristics of raw and alkali treated novel Tinospora cordifolia natural fiber.Scientific reports · 2025Article
- Analysis of machining performance in turning with trihybrid nanofluids and minimum quantity lubrication.Scientific reports · 2025Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes. For manufacturing a single product, predicting energy demand can be decisive in determining parametric control and other factors. Previously analytical models have been largely used to model machining requirements and energy demand. However, these models largely depend on parameterization and do not facilitate the integration of external sub-systems. Therefore, in this paper, an artificial intelligence-based power reduction strategy is developed and implemented on single material (Inconel 718), four control parameters (cutting speed, feed rate, depth of cut and flow rate) and two sub-systems (minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL)). The paper employs four machine learning algorithms,' K-Nearest Neighbor', 'Gaussian Regression', 'Decision Tree', and 'Logistic Regression', to evaluate their functionality in predicting power consumption (Pc) of CNC machining systems using a real experimental data set. As per evaluation based on five performance metrics (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.