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

Abstract

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.

Evaluation of the Effect of Machining Technologies on the Surface Texture Analysis of Ertacetal C Polymer

“…The authors of Tabacaru et al (2020) proposed a neural model that is able to predict surface roughness not only with regard to cutting parameters, but also to the type of material when drilling polymeric materials in dry conditions -high-density polyethylene (class HDPE 1000), polyamide (class PA6) and polyacetal (class POM -C). The final conclusion from this study can be stated that POM-C has the best machinability of all materials studied, the second-best being HDPE 1000 and PA6 [18].…”

Comparative study of turning two engineering plastics (POM-C and PA-6) and optimisation using GA, SA, GRA and COPRAS with and without weighting (Entropie, critic, Swara, ROC)

“…On the other hand, the increase in f and apse translates into an increase in the section of the chip; consequently, the volume of the chip increases, which contributes directly to the rise in temperature in the cutting zone. As polymer materials in general and our two materials studied in particular have very low thermal diffusion coefficients, the heat released by the cutting process is concentrated in the cutting zone and in turn contributes to the degradation of the surface condition obtained [6]. The graph of the main effects is presented in figure 2.…”

“…However, for the evolution of the specific cutting effort (Ks), (Vc) is more dominant. Tabacaru et al [6] conducted a study on predicting roughness during dry drilling of polymeric materials such as high-density polyethylene (HDPE 1000 grade), polyamide (PA-6 grade), and polyacetal (POM-C grade). Alateyah et al [7] performed an experimental and analytical study investigation on the influence of cutting parameters on the turning of two different polymers types: high-density polyethylene (HDPE) and un-reinforced polyamide (PA-6).…”

Dust Control Technology in Dry Directional Drilling in Soft and Broken Coal Seams

“…Pre-draining coal seam gas with long boreholes is one of the effective measures to control coal seam gas in China [1][2][3][4]. At present, wet drilling and dry drilling are two of the methods mainly adopted for long boreholes in coal seams [5][6][7]. Wet drilling generates less dust powder but has problems with drilling jams, hole collapse, and sewage disposal [8,9].…”