Assessment of cylindricity and roughness tolerances of holes drilled in marble using multiple regression and artificial intelligence

Abstract

The Calacatta-Carrara marble is widely used due to its excellent physico-chemical characteristics and attractive aspect. However, the sensitivity of this materiel, when performing delicate manufacturing operations, presents for the engineers a hard challenge to overcome. This issue is mainly encountered with complex shapes of parts, for which it is difficult to preserve surface integrity and avoid geometric defects. The paper aims at finding out optimal drilling parameters of cutting in the Calacatta-Carrara white marble material, in order to minimize the holes cylindricity (HC) and surface roughness (HR) using six controlled operating factors, namely, the rotation speed ( N), the feed speed ( F), the drill bit diameter (BD), the drill bit height (BH), the number of pecking cycles ( P), and the drilling depth (DD). The experimental design uses a [Formula: see text] fractional factorial plan that is replicated once for cost consideration. The optimization process, that is, minimum cylindricity and roughness tolerances, is carried out using the Gray Relational Analysis (GRA) technique. Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality. For the sake of completeness these two models were compared in terms of fitness and predictability. The models were assessed statistically using the correlation coefficient. Results showed that either solution predicts a roughness tolerance which is in good agreement with the test data (both R-sq.(adj.) and R-sq.(pred.) >94%). However, the holes cylindricity tolerance response was shown to be superior with MLP-ANN model (R-sq.(adj.) 50.64% and R-sq.(pred.) 48.67%). The GRA analysis shows that minimum cylindricity and roughness are met when N and F are set high, BD and BH low, P high and DD low.

Linear regression and artificial neural network models for predicting abrasive water jet marble drilling quality

“…ANN was used to model the non-linear input-output relationships of the drilling process. Abbassi et al 7,41 used neuronal approaches to predict surface roughness and geometric defects (circularity, cylindricity, and localization) and assess the cylindricity and roughness tolerances of holes drilled in Carrara-Calacatta marble with a diamond cutting bit. Akıncıoğlu et al 42 used an artificial neural network (ANN) for modeling surface and hole quality in the drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills.…”

An Iterative Minimum Zone Algorithm for assessing cylindricity deviation

“…A method was proposed to analyse the influence of the assumed factors on the geometrical quality of the holes. Abbassi et al [36] used multiple regression and artificial intelligence to evaluate the cylindricity and roughness tolerances of holes drilled in marble. Varatharajulu et al [37] developed the empirical model and optimised it through Desirability Function Approach for drilling magnesium AZ31.…”