Modeling and optimization of machining parameters to minimize surface roughness and maximize productivity when turning polytetrafluoroethylene (PTFE)

Azzi, A.; Boulanouar, Lakhdar; Laouisi, Aissa; Mebrek, Alima; Yallese, Mohamed Athmane

doi:10.1007/s00170-022-10160-z

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…All experimental tests and survey results highlighted that the feed is the primary factor influencing the arithmetic mean roughness 𝑅𝑎, contributing 65.9% and 99.1%, respectively. In the literature, several studies [17][18][19][20][21] have explored the impact of machining conditions on engineering plastics, focusing on output parameters like surface texture, cutting temperature, material removal rate, viscous deformation, crystallinity rate, cutting forces, and cutting power. A recent study by Azzi et al [17] determined that the optimal parameters for minimizing𝑅𝑎and maximizing 𝑀𝑅𝑅 during the turning of polytetrafluoroethylene polymer (PTFE) are 𝑎𝑝 = 2 𝑚𝑚, 𝑓 = 0.126 𝑚𝑚/𝑟𝑒𝑣, and 𝑉 𝑐 = 270 𝑚/𝑚𝑖𝑛.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, several studies [17][18][19][20][21] have explored the impact of machining conditions on engineering plastics, focusing on output parameters like surface texture, cutting temperature, material removal rate, viscous deformation, crystallinity rate, cutting forces, and cutting power. A recent study by Azzi et al [17] determined that the optimal parameters for minimizing𝑅𝑎and maximizing 𝑀𝑅𝑅 during the turning of polytetrafluoroethylene polymer (PTFE) are 𝑎𝑝 = 2 𝑚𝑚, 𝑓 = 0.126 𝑚𝑚/𝑟𝑒𝑣, and 𝑉 𝑐 = 270 𝑚/𝑚𝑖𝑛. They found that the key influencer for the quantities of interest (𝑅𝑎, 𝑅𝑧, and 𝑀𝑅𝑅) is the feed, with contributions of 90.02%, 91.81%, and 49.22%, respectively.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Hence, it should be highlighted that the behavior of the polymer during machining is significantly different from that of metal alloys due to the dependence of the polymer on the temperature [ 6 ]. The problem of the influence of the machining parameters on the properties of a semi-crystalline polymer material has been considered by many authors [ 7 , 8 , 9 ]. The authors of [ 7 ] presented research aimed at assessing the machinability of typical thermoplastic and thermosetting polymers and understanding the impact of their viscosity on the surface integrity, chip formation and cutting force.…”

Section: Introductionmentioning

confidence: 99%

“…Test results indicated that polyimide had good mechanical properties and machinability. The authors of [ 9 ] studied the impact of the machining parameters on the technological parameters, surface roughness criteria and rate of material removal during polytetrafluoroethylene turning. Modeling of the output technological parameters was carried out in order to determine the most efficient process.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Impact of Changes in Thermomechanical Properties of Annealed Semi-Crystalline Plastics on the Surface Condition after the Machining Process

Gnatowski

Gołębski

Sikora³

et al. 2023

Materials

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In this paper, the authors present a comparative analysis of the thermomechanical properties of plastics intended for machining before and after the annealing process. The research included the dynamic properties, thermal analysis and a study of the surface after machining. The dynamic properties were tested using the DMTA method. The characteristics of changes in the value of the storage modulus E’ and the tangent of the mechanical loss angle tgδ depending on the temperature and vibration frequency were determined. The thermal properties were tested using the DSC method, and a comparative analysis of the roughness parameters of the tested materials obtained from the profilometer was carried out. The presented studies indicate the extent of the impact of the annealing process on the machinability of structural polymer materials, taking into account the analysis of changes in the thermomechanical properties of the tested materials.

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“…For minimal finish surface roughness issued from a turning process, the study gives 628 m/min, 0.08 mm/rev, and 1 mm as the optimized cutting parameters respectively for Vc, f and ap. For the turning process of PTFE (Polytetrafluoroethylene) polymer, Azzi et al [12] analyzed the effects of input cutting regime parameters (i.e., ap; f; Vc) on roughness criteria (Ra, Rz) and material removal rate (MRR) using an L27 Taguchi design. Through ANOVA, it is concluded that Ra, Rz and MRR are effectively influenced by f (feed rate).…”

Section: Introductionmentioning

confidence: 99%

Manufacturing of testing specimens from tough HDPE-100 pipe: Turning parameters optimization

Mammeri,

Chaoui,

Bouacha

2023

Res. Eng. Struct. Mat.

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Numerous machining operations are required in order to manufacture standard and non-standard specimens for mechanical testing of polymers. The present work focuses on the machinability of HDPE-100 pipe to prepare the utmost regular filaments with specified thickness and width. The study is set to establish mathematical correlations between surface quality (total roughness; Rt), cutting temperature (T°), filament uniformity (L) and corresponding cutting conditions. The latter include Vc (cutting speed), f (feed rate) and ap (depth of cut), combined with tool geometry (i.e., rake angle: γ and cutting-edge angle: κr). A mixed Taguchi L18 plan is adopted to organize and process the experimental runs. ANOVA and RSM (Response Surface Methodology) are employed to construct prediction models and optimize subsequent machining results. It is found that T (cutting temperature) and surface roughness criteria (Ra, Rt) are strongly affected by f and Vc. In addition, ANOVA results related to the height of filament bends (L) are likewise studied as a function of tool angles γ and κr. It is noted that cutting process is influenced by κr as it explains ~19% contributions of the total variation of parameter L, while γ, Vc and f show a little influence. It is deduced that optimum input parameters, represented by Vc, f, ap, γ and κr, are respectively 160 m/min, 0.5 mm/rev, 4 mm, (-6°) and 90° when turning tough HDPE material.

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Modeling and optimization of machining parameters to minimize surface roughness and maximize productivity when turning polytetrafluoroethylene (PTFE)

Cited by 8 publications

References 22 publications

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

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

Analysis of the Impact of Changes in Thermomechanical Properties of Annealed Semi-Crystalline Plastics on the Surface Condition after the Machining Process

Manufacturing of testing specimens from tough HDPE-100 pipe: Turning parameters optimization

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