ArticleHeliyon2021
Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance.
Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- ANN-ANFIS model for optimising methylic composite biodiesel from neem and castor oil and predicting emissions of the biodiesel blend.Scientific reports · 2025Article
- Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis.Scientific reports · 2025Article
- An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.Scientific reports · 2025Article
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- Complex artificial intelligence models for energy sustainability in educational buildings.Scientific reports · 2024Article
- Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches.Scientific reports · 2024Article
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- Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics.Frontiers in aging neuroscience · 2023Article
- Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches.Materials (Basel, Switzerland) · 2022Article
- Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence.Materials (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
In the development of an accurate modeling technique for the design of an efficient machining process, manufacturers must be able to identify the most suitable technique capable of producing a fast and accurate performance. This study evaluates the performance of the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting the machining responses (metal removal rate and tool wear) in an AIS steel turning operation. With data generated from carefully designed machining experimentation, the adequacies of the ANN and ANFIS techniques in modeling and predicting the responses were carefully analyzed and compared. Both techniques displayed excellent abilities in predicting the responses of the machining process. However, a comparison of both techniques indicates that ANN is relatively superior to the ANFIS techniques, considering the accuracy of its results in terms of the prediction errors obtained for the ANN and ANFIS of 6.1% and 11.5% for the MRR and 4.1% and 7.2% for the Tool wear respectively. The coefficient of correlation (R
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