Modelling of machining force components during turning of PEEK CF30 by TiN coated cutting tools using artificial intelligence

The cutting force has a significant effect on the geometry and tolerances (e.g. circularity, deviation of circular shape or cylindricity, ovality, conical surfaces) of the workpiece (especially in the case of thin and slim parts), which are key criteria for the products. Moreover, they also affect the tool wear process. Due to the development of technology, the new tool (coatings) and workpiece materials require the exact knowledge of the cutting forces, their investigation during turning is an important field of cutting studies. In the case of turning, the kinetic conditions of which can be seen in Fig. 1, a spatial force system is generated.A new frictional model was constructed by Rusinek et al.[1] in case of orthogonal cutting that took the forces acting on the tool face as well as on the tool flank into consideration. Furthermore, the influence of the tool flank forces on system dynamics was studied.The mathematical modelling of cutting processes can be based on two methods: creating mechanical or phenomenological models. In recent years, several researchers have investigated the machinability of different types of materials, dealt with optimizing, measuring, and calculating cutting forces. The investigation of the machinability of different types of steel is still a basic field of cutting studies.Kulkarni et al.[2] carried out cutting tests on AISI304 austenite stainless steel by using AlTiCrN coated inserts. In their experimental runs, the depth of cut was held at a constant value (a p = 1 mm), the limits of the other cutting parameters were v c = 140 m/min to 320 m/min; f = 0.08 mm to 0.26 mm. The generated forces and the cutting temperature were studied. They said that regarding the force components, the effect of the feed was the most significant; however, the influence of cutting speed was negligible. In contrast, • Novel geometric parameters of the chip cross-section were introduced. Application of a Force Model Adapted for the Precise Turning of Various Metallic Materials• The main values of specific cutting forces characterizing fine turning technology were theoretically calculated.• The applicability of the equations for each material used was investigated by residuals.

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“…Mata et al [21] were studying the cutting ability of polyether ether ketone (PEEK) reinforced with 30 % carbon fibre.…”

Section: Application Of a Force Model Adapted For The Precise Turningmentioning

confidence: 99%

Application of a Force Model Adapted for the Precise Turning of Various Metallic Materials

Horváth

Lukacs

2017

SV-JME

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“…Hence, it is crucial to have a well-designed set of experiments that can substantially condense the number of experiments as compared to classical experimental design. In the current study, the ranges of process parameters were selected based on the previous investigations on PEEK materials [9,11]. In the present work, three levels are defined for each of the cutting conditions i.e.…”

Section: Experimental Designmentioning

confidence: 99%

“…The carbon and glass fibers are the common reinforcements in PEEK material because of low expansion rate and high flexural modulus and hence find several applications in resistant or structural components, mainly at temperatures above 150ºC. The reinforced poly ether ether ketone with 30% of carbon fiber (PEEK-CF30) constitutes cost-effective alternative to stainless steel and other metallic materials in strongly corrosive industrial applications [11].…”

Section: Introductionmentioning

confidence: 99%

“…The optimal feed rate is found to be 0.05-0.075 mm/rev ((low range) for minimizing the surface roughness in PEEK-CF30 material using TiN cutting tools. Petropoulos et al [9] and Mata et al [11] also found that feed rate exerts the strongest influence on surface roughness, whilst cutting speed has a secondary effect during turning of PEEK-CF30 material using PCD and carbide tools. Davim and Mata [25] observed that feed has significant influence on R a and R z during turning of PEEK-CF30 material using three different tools, namely, chemical vapor deposition (CVD), PCD and cemented carbide (K10) cutting tools.…”

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confidence: 96%

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Surface Roughness Minimization in Turning PEEK-CF30 Composites with TiN Cutting Tools Using Particle Swarm Optimization

Cabrera¹,

Garrido

Tejero

et al. 2013

MSF

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Poly ether ether ketone (PEEK) composite belongs to a group of high performance thermoplastic polymers and the addition of carbon fibers increases the resistance and rigidity of PEEK. Due to favorable mechanical and thermal properties and potential applications in various manufacturing industries, it is essential to analyze the machining performance of reinforced PEEK composites. The surface roughness is a widely used measure of product quality that could influence the performance of mechanical products and production costs. The present study establishes the relationships between the cutting conditions (cutting speed and feed rate) and three surface roughness parameters (centerline average surface roughness, maximum peak to valley height and root mean square roughness) by developing second order mathematical models based on response surface methodology (RSM). The reinforced PEEK with 30% of carbon fiber (PEEK-CF30) workpieces were machined using TiN-cutting tools. The experiments have been planned as per full factorial design of experiments (FFD). The developed surface roughness models were then employed with particle swarm optimization (PSO) to optimize the cutting conditions. PSO program gives the minimum values of surface roughness parameters and the corresponding optimal cutting conditions.

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“…A fuzzy rule-based model is developed to predict the machining force components in turning of carbon fibrereinforced polymer (CFRP) [15]. Good agreement is observed between the predictive model results and the experimental values.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks back propagation algorithm for cutting force components predictions

Hanafi¹,

Cabrera²,

Khamlichi

et al. 2013

Mechanics & Industry

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Reinforced Poly Ether Ether Ketone with 30% of Carbon Fiber (PEEK CF30) offer several thermo-mechanical advantages over standard materials and alloys which make them better candidates in different applications. However, the hard and abrasive nature of the reinforcement fiber is responsible for rapid tool wear and high machining costs. It is very important to find highly effective ways to machine that material. Accordingly, it is important to predict forces when machining fiber matrix composites because this will help to choose perfect tools for machining and ultimately save both money and time. In this study, Artificial Neural Network (ANN) was applied to predict the cutting force components in turning operations of PEEK CF30 using TiN coated cutting tools under dry conditions where the machining parameters are cutting speed ranges, feed rate, and depth of cut. For this study, the experiments have been conducted using full factorial design experiments (DOEs) on CNC turning machine. The results indicated that the well-trained (ANN) model could be able to predict the cutting force components in turning of Carbon Fiber Reinforcement Polymer (CFRP) composites. Complementary results that were not used during derivation of the ANN model have enabled one to assess the validity of the obtained predictions.

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Modelling of machining force components during turning of PEEK CF30 by TiN coated cutting tools using artificial intelligence

Cited by 8 publications

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Application of a Force Model Adapted for the Precise Turning of Various Metallic Materials

Application of a Force Model Adapted for the Precise Turning of Various Metallic Materials

Surface Roughness Minimization in Turning PEEK-CF30 Composites with TiN Cutting Tools Using Particle Swarm Optimization

Artificial neural networks back propagation algorithm for cutting force components predictions

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