Experimental study of drilling white Calacatta–Carrara marble using artificial neural approach

Abstract

Highly accurate marble processing is increasingly needed to comply with tight parametric/geometric tolerances and surface integrity specifications encountered while structuring, sculpture, and decorating. In this study, a new approach based on the artificial neural network technique is evaluated for the prediction of process parameters in the machining of white Calacatta-Carrara marble. The rotation speed, feed speed, drill bit diameter, drill bit height, number of pecking cycles, and drilling depth were considered as input factors. Corresponding surface roughness, hole circularity, hole cylindricity, and hole-location error were sought in output. A series of experiments was carried out using a 5-axes computer numerical control vertical machining center (OMAG) to obtain the data used for the training and testing of the artificial neural network with reasonable accuracy, under varying machining conditions. A MATLAB TM interface was developed to predict surface roughness and geometric defects (circularity, cylindricity, and localization). A 6 3 4 size multilayered neural network was developed. The number of iterations was 1000 and no smoothing factor was used. The drill quality (holelocation error, hole circularity, and hole cylindricity) and the surface roughness were modeled and evaluated individually. One hidden layer used for all models, with the number of neurons for all the responses being executed separately, was 12 while the number of neurons in the hidden layer, with all the responses executed together, was 14. In conclusion, from the obtained verified experimentally optimization results, the errors are all within acceptable ranges, which, again, confirm that the artificial neural network technique is an efficient and accurate method in predicting responses in drilling.

Heavy trucks with intelligent control of active suspension based on artificial neural networks

“…The resulting road profiles are applied to an HGV vehicle model which calculates vehicle responses. Following our experience in the application of ANNs in the field of mechanical engineering, 42,43 these vehicle responses and their corresponding road profiles are processed and used to create and train the ANN. 44…”

“…The resulting road profiles are applied to an HGV vehicle model which calculates vehicle responses. Following our experience in the application of ANNs in the field of mechanical engineering, 42,43 these vehicle responses and their corresponding road profiles are processed and used to create and train the ANN. 44 The simulation results demonstrate that the proposed ANN controller can indeed provide a good ride whatever the state of the road and make an excellent compromise between passenger comfort and handling despite dynamic uncertainties.…”

Roundness and positioning deviation prediction in single point incremental forming using deep learning approaches

“…Moreover, a BPNN can be considered as a black box and consequently the planner can use it in a very simple way without a deep knowledge of its theoretical fundamentals. 19 The general neural network architecture is given in Figure 5.…”

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

“…In this work, a Multi-Layer Perception MLP network with back propagation was used due to its adaptability to many engineering problems. 7,43 A 5-NN-1 ANN architecture (5 inputs and 1 output) was considered and one model trained for each KPI (cylindricity, circularity, and localization error). The 5-NN-1 architecture is presented on Figure 11.…”