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Drilling of polymeric materials can be challenging when holes are produced in large scale due to the necessity of controlling the holes quality. This paper presents the effect of machining parameters on the hole quality, in terms of surface roughness and circularity error, in drilling of high density polyethylene, polyamide and polyacetale. The analysis of variance was performed in order to assess the significance of cutting parameter on the hole quality parameters. The experimental results indicate that different cutting conditions are to be employed in order to achieve the optimum surface roughness and circularity.

Polymeric materials are synthetic macromolecular products, of which, by mechanical or thermal processing, objects of various shapes can be obtained, with wide uses in industry and commerce. This paper deals with the roughness of surfaces obtained during drilling of three polymeric materials: polyamide - PA6, polyacetal - POM-C and high density polyamide - HDPE 1000. In the experimental research was used a EMCO MILL 55 milling machine numerical controlled and HS steel helical drills with two straight cutting edges with the diameter of �8 mm and �10 mm, respectively. Experimental determinations consisted in drilling of the polymeric materials by modifying some parameters of the cutting regime, and determining the roughness of the surface of the holes machined, using the Mitutoyo Surftest SJ-210 rough meter. The purpose of the paper is to predict the roughness of the machined surfaces as one of the important surface quality indicators by using a geometrical model and an artificial neural network (ANN) methodology.

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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