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

Florin Susac,Felicia Stan

doi:10.37358/mp.20.1.5312

Abstract

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.

Highlights

Machining of thermoplastic polymers 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 [1-12]
In this study, drilling experiments were conducted on PMMA to assess the impact of drilling parameters on the hole-quality characteristics
Analysis of variance (ANOVA) was performed for identifying the process parameters that significantly influence the hole quality characteristics, whereas the artificial neural network (ANN) method was used for predicting the hole quality characteristics

Summary

Introduction

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 [1-12]. 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]. Optimization of the cutting parameters is an important step in machining of polymers and polymerbased composites and, 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 [4-7,10-14] and derive the predictive models for various output parameters such as surface roughness [2-5,7,8,18], cutting forces [1,11,14,15] or delamination factor [1,10,11,14]. Artificial neural networks (ANN), in the last years, were employed as the main methodology for modeling and optimization of both input and output parameters of the cutting process [3,19-22]. The ANN methods are useful to model phenomena for which the relationships between the inputs and outputs are non-linear

Methods

Results

Conclusion