Effect of Cutting Parameters on the Hole Quality in Dry Drilling of Some Thermoplastic Polymers

Susac, Florin; Tabacaru, V.; Teodor, Virgil; Baroiu, Nicușor

doi:10.37358/mp.19.1.5160

Cited by 7 publications

(9 citation statements)

References 10 publications

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“…The experimental set-up used for surface roughness measurement of drilled holes is presented in Figure 2. The Ra surface roughness was measured as the arithmetic average of the absolute values of effective profile ordinates with respect to median line that is considered as origin [5]. For Ra, the roughness measurement range of the profilometer is from -200 to +160 microns [24].…”

Section: Measurement Of Surface Quality Parametersmentioning

confidence: 99%

“…The circularity error at the hole entry was evaluated as the maximum distance between the real hole profile (D) and adjacent circle (D0), as shown in Figure 4. The adjacent circle is the theoretic or programmed drilled hole [5]. For cylindricity error, the measurement trajectory was a spiral from the upper to the lower part of the hole, as shown in Figure 3b.…”

Section: Figure 3 Experimental Set-up For Circularity and Cylindricimentioning

confidence: 99%

“…Machining of thermoplastic polymers (turning, milling, drilling or others) is a complex process affected by various machining factors such as cutting parameters, geometry and material of the cutting tool, and thermo-mechanical properties of the machined polymers [1][2][3][4][5][6][7][8][9][10][11][12]. Thus, designers and engineers have to deal with great challenges when machining thermoplastic materials because the cutting parameters have a significant influence on the machinability attributes like surface integrity and finish [3,4,7,8,13], cutting forces [1,11,14,15] tool wear and life [14] and dimensional and geometric accuracy [2,9,10,15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Optimization of the cutting parameters is an important step in machining of polymers and polymerbased composites and, therefore, many researchers have employed different methods, e.g. Taguchi method, analysis of variance [17], artificial neural networks, response surface methodology or combination of these methods, to identify the process parameters that have a significant effect on the surface roughness [4][5][6][7][10][11][12][13][14] and derive the predictive models for various output parameters such as surface roughness [2][3][4][5]7,8,18], cutting forces [1,11,14,15] or delamination factor [1,10,11,14].…”

Section: Introductionmentioning

confidence: 99%

“…Among various machining operations, drilling is the most commonly employed machining operation for polymers, such as PMMA, due to the need for mechanical joining [1]. Apart from surface roughness, additional characteristics, of particular importance, such as circularity and cylindricity errors must be considered when optimizing the drilling of polymers [2,5,12,15].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of PMMA Using ANN and ANOVA

Susac¹,

Stan²

2020

Mater. Plast.

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In this paper, experimental investigation, modeling and optimization of the drilling of PMMA are performed using the Taguchi Design of Experiments (DOE), analysis of variance (ANOVA) and artificial neural networks (ANN) methods. Drilling experiments were conducted on PMMA to assess the impact of process parameters (drill diameter, spindle speed, and feed rate) on the hole-quality characteristics (surface roughness, circularity error, and cylindricity error). ANOVA was performed to identify the drilling parameters that have a statistically significant influence on the hole-quality characteristics. A predictive model for the hole-quality characteristics was derived using a four-layer ANN with a backpropagation algorithm and a sigmoidal transfer function at the hidden layers. The ANN model was able to accurately predict the hole-quality parameters with the absolute mean relative errors of the testing data in the limits of 3 to 7%. Based on the experimental results and analytical modeling, it was found that drilling of PMMA requires lower spindle speed and high feed rate when the integrity of the drill hole is the main quality criterion.

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Section: Measurement Of Surface Quality Parametersmentioning

confidence: 99%

Section: Figure 3 Experimental Set-up For Circularity and Cylindricimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of PMMA Using ANN and ANOVA

Susac¹,

Stan²

2020

Mater. Plast.

Self Cite

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Artificial neural networks applied to prediction of surface roughness in dry drilling of some polymers

Tabacaru

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Polymers become more and more attractive for automotive and aerospace industries due to their remarkable mechanical, thermal and electrical properties that make these materials suitable for many industrial applications. Machining of polymers is of a great interest among researchers and engineers due to the possibility of replacing expensive materials with plastics that have similar mechanical characteristics, but of a lower cost. Drilling is the most common mechanical machining operation in manufacturing of parts. Within this context, it is essential to analyze this cutting process and find the best solution for controlling the output parameters of the process, such as surface roughness and cutting forces. The present work concerns the developing of an artificial neural model for the prediction of surface roughness in dry drilling of some polymeric materials: high density polyethylene (grade HDPE 1000), polyamide (grade PA6) and polyacetale (grade POM-C). The neural model was built based on trial-and-error method, by modifying the number of hidden layers and the number of hidden neurons on each layer. The experimental plan was designed to be suitable for artificial neural network prediction. The aim of experimental work was to study the effect of cutting parameters (spindle speed, feed rate and drill diameter) on the quality of machined surface (surface roughness). This paper proposes a neural model that is able to predict the surface roughness considering not only the cutting parameters, but the type of material. Moreover, a study concerning the accuracy of the neural model depending on the number of hidden layers and the number of hidden neurons on each hidden layer was carried out. The predicted values of roughness were compared with experimental data in order to determine the precision of the neural model.

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Effect of Drilling Parameters on Machining Performance in Drilling Polytetrafluoroethylene

Zeng

Al-Furjan

et al. 2022

Materials

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Polytetrafluoroethylene (PTFE) plays an important role in semiconductor manufacturing. It is an important processing material for the key sealing components in the field of immersion lithography. The lack of research related to the mechanical processing of PTFE leads to many challenges in producing complex parts. This paper conducted a drilling experiment on PTFE. The effect of cutting parameters on the drilling performance was investigated. Thrust, torque, surface roughness, and drilling temperature were used to evaluate the influence of cutting parameters on drilling performance. In addition, the empirical mathematical models of thrust and torque were developed using analysis of variance (ANOVA). The results indicated that the spindle speed had the most important effect on the thrust and the feed rate had the most significant effect on the torque. The lowest values of thrust and torque were, respectively, 22.64 N and 0.12 Nm, achieved in the case of spindle speed of 5000 rev/min, and feed rate of 50 mm/min. The surface quality is also best at this cutting parameter. Studies have shown that higher spindle speeds with lower feed rates are ideal parameters for improving the drilling performance and machining quality of PTFE. In addition, it was found that the temperature differences due to different drilling depths were related to chip accumulation. Surface roughness inconsistencies at various locations in the inner wall of the hole were influenced by chip adhesion during machining. This paper provides a suggestion for optimizing cutting parameters and hole quality.

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Effect of Cutting Parameters on the Hole Quality in Dry Drilling of Some Thermoplastic Polymers

Cited by 7 publications

References 10 publications

Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of PMMA Using ANN and ANOVA

Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of PMMA Using ANN and ANOVA

Artificial neural networks applied to prediction of surface roughness in dry drilling of some polymers

Effect of Drilling Parameters on Machining Performance in Drilling Polytetrafluoroethylene

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