Optimization of turning process parameters using a new hybrid evolutionary algorithm

Abderazek, Hammoudi; Laouissi, Aissa; Nouioua, Mourad; Atanasovska, Ivana

doi:10.1177/09544062231195472

Cited by 4 publications

(2 citation statements)

References 47 publications

(53 reference statements)

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“…Advanced mathematical optimization methods are analyzed and researched by solving a series of multiparametric optimization for real engineering problems. [5][6][7] In Abderazek et al, 5 a new hybrid optimization algorithm (improved differential evolution and Nelder-Mead -IDE-NM) is introduced for optimizing the multi-objective machining process during the turning operation under three modes of lubrication conditions. Five mixed design variables are considered in the optimization procedure, including the cutting speed, feed rate, depth of cut, mode of lubrication, and type of cutting material.…”

Section: Special Issue: Advanced Mathematical Modeling In Mechanical ...mentioning

confidence: 99%

Special issue: Advanced mathematical modeling in mechanical engineering

Atanasovska,

Patil,

Casanova

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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Section: Special Issue: Advanced Mathematical Modeling In Mechanical ...mentioning

confidence: 99%

Special issue: Advanced mathematical modeling in mechanical engineering

Atanasovska,

Patil,

Casanova

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The findings indicated that the highest material removal rate of 4550 mm 3 /min was achieved with a cutting speed of 100 m/min, a feed rate of 0.091 mm/rev, and a depth of cut of 0.4mm. Abderazek et al (2024) introduced an innovative multi-objective optimization algorithm termed Improved Differential Evolution and Nelder-Mead (IDE-NM). Their study incorporated three lubrication conditions and five key process parameters: cutting speed, feed rate, depth of cut, lubrication mode, and cutting material type.…”

Section: Ntukidem B I; Achebo J I; Ozigagun A; Uwoghiren F O; Obahiag...mentioning

confidence: 99%

Optimization of Material Removal Rate in Computer Numerical Control Lathe Machine in Turning AISI 1040 Steel

Ntukidem,

Achebo,

Ozigagun

et al. 2024

jasem

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The Computer Numerical Control (CNC) of a Lathe Machine helps in shaping hard materials like metal and wood, while rotating on two axes and the amount of materials removed per unit time during the production process provides the material removal rate (MRR). Hence, the objective of this paper was to optimize the Material Removal Rate in a Computer Numerical Control Lathe Machine in Turning AISI 1040 Steel while focusing on cutting parameters such as depth of cut, cutting speed, and feed rate. Employing a central composite design with twenty experimental runs, ANOVA analysis revealed cutting speed (F-value: 80.40) as the most influential parameter on MRR. Initially, Artificial Neural Networks (ANN) predicted MRR, yielding optimal parameters: depth of cut (0.25 mm), cutting speed (250 m/min), feed rate (0.20 mm/rev), resulting in MRR of 27.88 mm3/min. Genetic Algorithm (GA) optimization surpassed ANN, yielding higher MRR (29.07442 mm3/min) with optimal parameters: depth of cut (0.65 mm), cutting speed (221.09 m/min), and feed rate (0.21 mm/rev). Confirmatory tests validated predictions. This study provides insights into enhancing CNC turning efficiency and productivity.

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Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network

Vargas,

Mosquera,

Fuertes

et al. 2024

Processes

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This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.

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Optimization of turning process parameters using a new hybrid evolutionary algorithm

Cited by 4 publications

References 47 publications

Special issue: Advanced mathematical modeling in mechanical engineering

Special issue: Advanced mathematical modeling in mechanical engineering

Optimization of Material Removal Rate in Computer Numerical Control Lathe Machine in Turning AISI 1040 Steel

Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network

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