Multi-objective analysis and optimization of energy aspects during dry and MQL turning of unreinforced polypropylene (PP): an approach based on ANOVA, ANN, MOWCA, and MOALO

Hamdi, Amine; Yapan, Yusuf Furkan; Uysal, Alper; Abderazek, Hammoudi

doi:10.1007/s00170-023-12205-3

Cited by 10 publications

(2 citation statements)

References 52 publications

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“…In the realm of manufacturing and machining, achieving optimal performance in machining processes is paramount for ensuring product quality, energy efficiency, and cost-effectiveness [54]. The quest for excellence in machining operations necessitates a meticulous analysis of process parameters to attain the finest balance between multiple conflicting objectives such as minimizing cutting force and cutting temperature, maximizing material removal rates, and ensuring surface finish quality.…”

Section: Multi-criteria Decision Making With Topsismentioning

confidence: 99%

Experimental investigation and optimization of hybrid turning of Ti6Al7Nb alloy under nanofluid based MQL by TOPSIS method

DUMAN

2023

J Adv Manuf Eng

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The present work aims to decide on machining parameters and enhance machinability of the biomedical Ti6Al7Nb alloy using nanofluid MQL with nanoparticles of graphene (NMQL) and ultrasonic vibration assisted (UVA) machining methods were applied both separately and in a hybrid manner. Consequently, for the chosen cutting parameters, when compared to the conventional turning (CT) with vegetable cutting oil-based MQL, the UVA-NMQL hybrid method has achieved a reduction in cutting forces ranging from approximately 11% to 23%, a decrease in cutting temperatures by around 9% to 17%, and an enhancement in average surface roughness by roughly 15% to 53% across all the analyzed results compare to vegetable oil based conventional MQL turning conditions. Additionally, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, the optimum cutting parameters were determined as UVA-NMQL cutting condition, 130 m/min cutting speed, and 0.1 mm feed value.

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Section: Multi-criteria Decision Making With Topsismentioning

confidence: 99%

Experimental investigation and optimization of hybrid turning of Ti6Al7Nb alloy under nanofluid based MQL by TOPSIS method

DUMAN

2023

J Adv Manuf Eng

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“…Additionally, the depth of cut was found to significantly influence cutting temperature. In a recent study, Hamdi et al [25] demonstrated that feed (𝑓) and the use of minimal quantity lubrication (MQL) are two independent factors that influence specific cutting energy (SCE) consumption during CNC turning of unreinforced polypropylene (PP).…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

2024

jjmie

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The paper has a dual purpose: firstly, to examine the influence of various cutting conditions (cutting speed 𝑉 𝑐 , feed 𝑓, depth of cut 𝑎𝑝, tool nose radius 𝑟 ɛ , and cutting edge angle 𝑋 𝑟 ) on the quality of machined parts (𝑅𝑎), tangential force (𝐹 𝑍 ) and cutting power (𝑃 𝑐 ) during the turning process of polyoxymethylene POM-C. Two carbide inserts, SPMR 120304 and SPMR 120308, were used for the three-dimensional cutting operations. Secondly, the goal is to identify optimal cutting conditions that maximize material removal rate (𝑀𝑅𝑅) while minimizing three output parameters (𝑅𝑎, 𝐹 𝑍 , and 𝑃 𝑐 ). The study employed analysis of variance (ANOVA) to assess the significance of the input parameters on the desired outcomes and utilized an artificial neural network (ANN) to create mathematical models. The K-fold Cross-Validation approach was deemed suitable due to its efficiency in requiring fewer experiments. To optimize the cutting conditions, a new metaheuristic optimization algorithm called Multi-Objective Artificial Hummingbird Algorithm (MOAHA) was selected. ANOVA analysis reveals that factors 𝑓 and 𝑟 ɛ contribute 58.05% and 32.25%, respectively, to the response 𝑅𝑎. Classical parameters (𝑉 𝑐 ,𝑓, and 𝑎𝑝) also impact mechanical cutting actions (𝐹 𝑍 and 𝑃 𝑐 ).The MOAHA algorithm, coupled with four ANN models, optimized the five cutting conditions, resulting in optimal values 𝑉 𝑐 = 250 𝑚/𝑚𝑖𝑛, 𝑓 = 0.08 𝑚𝑚/𝑟𝑒𝑣, 𝑎𝑝 = 1.3 𝑚𝑚, 𝑟 ɛ = 0.8 𝑚𝑚, and 𝑋 𝑟 = 75°. Under these conditions, responses are: 𝑅𝑎 = 0.6 µ𝑚, 𝐹 𝑍 = 21.51 𝑁,𝑃 𝑐 = 60.24 𝑊, and 𝑀𝑅𝑅 = 26.38 𝑐𝑚 3 /𝑚𝑖𝑛. The ANN-MOAHA coupling provides an excellent, simple, and fast computer tool for multi-objective optimization.

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Investigation of MQL and CNC turning parameters on the machinability of unreinforced polypropylene: study of surface roughness, temperature, and specific cutting energy

Hamdi,

Yapan,

Uysal

et al. 2023

Int J Adv Manuf Technol

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Multi-objective analysis and optimization of energy aspects during dry and MQL turning of unreinforced polypropylene (PP): an approach based on ANOVA, ANN, MOWCA, and MOALO

Cited by 10 publications

References 52 publications

Experimental investigation and optimization of hybrid turning of Ti6Al7Nb alloy under nanofluid based MQL by TOPSIS method

Experimental investigation and optimization of hybrid turning of Ti6Al7Nb alloy under nanofluid based MQL by TOPSIS method

Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

Investigation of MQL and CNC turning parameters on the machinability of unreinforced polypropylene: study of surface roughness, temperature, and specific cutting energy

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