Browsing by Author "Hamdi, Amine"

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    Enhancing sustainability in CNC turning of POM-C polymer using MQL with vegetable-based lubricant: machine learning and metaheuristic optimization approaches
    (Springer Science and Business Media, 2025) Hakmi, Tallal; Abderazek, Hammoudi; Yapan, Yusuf Furkan; Hamdi, Amine; Uysal, Alper
    Sustainable machining of polymer parts, which is still less advanced than metal machining, aims not only to improve machinability but also to address environmental and economic challenges. Therefore, this study analyzes the sustainability of polyoxymethylene copolymer (POM-C) turning by incorporating minimum quantity lubrication (MQL) parameters (Q: flow rate, θ: nozzle angle, and d: nozzle distance) and conventional cutting parameters (Vc: cutting speed, f: feed, and ap: depth of cut), while replacing conventional oil with a biodegradable and environmentally friendly lubricant derived from Eraoil KT/2000. Additionally, the methodology relies on sustainability indicators such as surface roughness (Ra), total energy consumption (Etotal), total carbon emissions (CEtotal), and overall cost (Ctotal). To achieve this, several approaches are employed, including analysis of variance (ANOVA), artificial neural networks (ANN), k-fold cross-validation (k-fold CV), and two multi-objective metaheuristic optimization algorithms, namely SHAMODE (success history-based multi-objective adaptive differential evolution) and RPBILDE (real-code population-based incremental learning and differential evolution), are used to identify significant factors, establish mathematical models, and determine optimal conditions. The multi-objective optimization highlights trade-offs between the four sustainability criteria. Thus, a low feed value and a low MQL flow rate, combined with significant angle and distance, as well as moderate cutting speed and depth of cut, provide minimal surface roughness (Ra = 1008 µm), low energy consumption (Etotal = 0.0947 MJ), low carbon emissions (CEtotal = 0.0583 kgCO₂) but with a slightly higher cost (Ctotal = 1701 $). These results confirm a Pareto front where the improvement of one criterion negatively impacts another, guiding industrial decisions based on priorities
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    Enhancing sustainability in CNC turning of POM-C polymer using MQL with vegetable-based lubricant: machine learning and metaheuristic optimization approaches
    (Springer Science and Business Media, 2025) Hakmi, Tallal; Abderazek, Hammoudi; Yapan, Yusuf Furkan; Hamdi, Amine; Uysal, Alper
    Sustainable machining of polymer parts, which is still less advanced than metal machining, aims not only to improve machinability but also to address environmental and economic challenges. Therefore, this study analyzes the sustainability of polyoxymethylene copolymer (POM-C) turning by incorporating minimum quantity lubrication (MQL) parameters (Q: flow rate, θ: nozzle angle, and d: nozzle distance) and conventional cutting parameters (Vc: cutting speed, f: feed, and ap: depth of cut), while replacing conventional oil with a biodegradable and environmentally friendly lubricant derived from Eraoil KT/2000. Additionally, the methodology relies on sustainability indicators such as surface roughness (Ra), total energy consumption (Etotal), total carbon emissions (CEtotal), and overall cost (Ctotal). To achieve this, several approaches are employed, including analysis of variance (ANOVA), artificial neural networks (ANN), k-fold cross-validation (k-fold CV), and two multi-objective metaheuristic optimization algorithms, namely SHAMODE (success history-based multi-objective adaptive differential evolution) and RPBILDE (real-code population-based incremental learning and differential evolution), are used to identify significant factors, establish mathematical models, and determine optimal conditions. The multi-objective optimization highlights trade-offs between the four sustainability criteria. Thus, a low feed value and a low MQL flow rate, combined with significant angle and distance, as well as moderate cutting speed and depth of cut, provide minimal surface roughness (Ra = 1008 µm), low energy consumption (Etotal = 0.0947 MJ), low carbon emissions (CEtotal = 0.0583 kgCO₂) but with a slightly higher cost (Ctotal = 1701 $). These results confirm a Pareto front where the improvement of one criterion negatively impacts another, guiding industrial decisions based on priorities
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    Sustainability and machinability in hard turning under MQL conditions using nanoparticle-enriched vegetable-based fluid: an integrated assessment
    (Springer Science and Business Media, 2025) Hamdi, Amine; Abderazek, Hammoudi; Yapan, Yusuf Furkan; Uysal, Alper; Merghache, Sidi Mohammed
    This study introduces an integrated approach for assessing both sustainability and machinability in the CNC turning of hardened AISI 1045 steel (52 HRC). The machining process employs minimum quantity lubrication (MQL) supplemented with biodegradable, vegetable oil–based nanofluids. Two distinct nanoparticle formulations are investigated: one based on multiwalled carbon nanotubes (MWCNT) and the other on hexagonal boron nitride (hBN). The study is conducted according to a Taguchi L18 experimental design. Four critical performance indicators are assessed: surface roughness (Ra), cutting temperature (Tc), total carbon emissions (CEtotal), and overall machining cost (MCtotal). The comprehensive analysis integrated Pareto charts, multiple linear regression modeling, and experimental validation. Furthermore, multicriteria decision making using the technique for order preference by similarity to ideal solution (TOPSIS) is employed, with equal weighting (25%) assigned to each criterion. The findings reveal that the hBN-based nanofluid achieved the highest overall performance, closely followed by the MWCNT-enhanced formulation, which demonstrated notable effectiveness in thermal control and environmental impact reduction. In contrast, traditional MQL without nanoparticles proved less efficient. Moreover, the cross-analysis of Pareto diagrams revealed that the cutting condition (CC) is the only factor with a statistically significant effect on all measured responses