Fuzzy rule-based modeling of machining parameters for surface roughness in turning carbon particle-reinforced polyamide

Self Cite

Machining processes face two major challenges: sustainability and cleaner production. However, the effective utilization of tool and methods of lubrication system in sustainable machining have been dealt in depth in earlier investigations. This work aims to optimize sustainable machining parameters in drilling process for recyclable carbon fiber-reinforced polypropylene (CFR-PP) composites. This work is focused on modeling and optimization of drilling parameters for sustainable machining with respect to thrust force and torque for CFR-PP composites. The response surface method based on D-optimal design of experiments is used for modeling and optimization with variables such as drill spindle speed and drill feed rate as numerical factors, which includes different drill material as the categorical factor. The influences of tool materials on the sustainable machining are also discussed in detail. Further, the sensitivity analysis is applied to compare the relative impact of control parameters (spindle speed, feed rate, and drill materials) on thrust force and torque. The scanning electron microscope images are used for analyzing the morphologies of drilled surfaces.

Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Optimization and sensitivity analysis of drilling parameters for sustainable machining of carbon fiber–reinforced polypropylene composites

Mudhukrishnan

Hariharan

Palanikumar³

et al. 2018

Self Cite

“…Fuzzy logic is one of the important mathematical modeling-based artificial intelligence techniques that can be used to solve complex problems using linguistic terms. 17,[29][30][31] In fuzzy system, model is in the form of "IF-THEN" rules instead of a mathematical equation. The fuzzy logic decision-making unit is composed of five functional blocks 32,33 and is illustrated in Figure 7.…”

Section: Fuzzy Rule-based Modeling Of Cutting Parametersmentioning

confidence: 99%

“…A fuzzy system is composed of a fuzzifier, which is used to generate fuzzy sets by converting the crisp inputs through selected MFs; an inference engine, which is used to generate fuzzy values based on defined rules; and a defuzzifier, which is used to convert fuzzy values into crisp outputs. 17,31 In this study, cutting speed, feed rate, and depth of cut were considered as input parameters, while ESC, Ra, and MRR were considered as the output parameters (Figure 8). In the fuzzy logic, the ranges of parameters are firstly identified.…”

Section: Fuzzy Rule-based Modeling Of Cutting Parametersmentioning

confidence: 99%

Fuzzy logic-based modeling and analysis of surface roughness, electrostatic charge, and material removal rate in ultrahigh precision diamond turning of rigid contact lens polymer

Liman

Abou-El-Hossein

Abdulkadir

2019

Due to increasing demand for high accuracy and high-quality surface finish in optical industry, contact lens manufacturing requires reliable models for predicting surface roughness (Ra) which plays a very important role in the optical manufacturing industry. In this study, a Nanoform 250 ultra-grind turning machine was used for machining, while cutting speed, feed rate, and the depth of cut (with values selected to cover a wide range based on the literature) were considered as the machining parameters for a diamond turned rigid polymethylmethacrylate (PMMA) contact lens polymer. Turning experiments were designed and conducted according to Box–Behnken design which is a response surface methodology technique. Fuzzy logic-based artificial intelligence method was employed to develop an electrostatic charge (ESC), Ra, and material removal rate (MRR) prediction models. The accuracy and predictive ability of the fuzzy logic model was then judged by considering an average percentage error between experimental values and fuzzy logic predictions. Further, a comparative evaluation of experiments and fuzzy logic approach showed that the average errors of ESC, Ra, and MRR using fuzzy logic system were in tandem with experimental results. Hence, the developed fuzzy logic rules can be effectively utilized to predict the ESC, Ra, and MRR of a rigid PMMA contact lens polymers in automated optical manufacturing environments for high accuracy and computational cost.

“…The best result was obtained by PCD tools; it reduced turning forces, and better R a was obtained compared to the rivals. Some studies 13 –16 were carried out to enhance the surface quality using different methods such as design expert, fuzzy logic, 17 analysis of variance, 16 and ANNs, 8 to model by interlocking the cutting conditions to the R a . For example, Bozdemir studied the cutting parameter (six factors) effects on the R a of PA6G materials.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling for prediction of cutting forces in turning unreinforced and reinforced polyamide

Özden

Mata

Öteyaka

2019

Cutting force measurement in manufacturing is very important to optimize the machining process. The parameters, such as the type of material, feed rate, cutting speed, and cutting tool, affect the cutting forces in the turning operation. In this study, an artificial neural network (ANN) model is used to predict the cutting forces during the turning operation of unreinforced and reinforced polyamide (PA) with 30 v/v% carbon fibers using the cutting tools K15 and polycrystalline diamond (PCD). The cutting speed (50–200 m/min), feed rate (0.05–0.2 mm/rev), type of material, and cutting tools (K15 and PCD) are defined as input parameters of the system. The predicted values obtained from ANN model and experimental results are compared in terms of the coefficient of determination ( R2) and the mean absolute percentage error. The results of the model are in good agreement with the experimental data and show the effectiveness of the ANN method in predicting cutting forces in the turning operation of PA.