Synthetic White Marble-Like Material Produced from Natural Raw Materials

Binhussain, Mohammed; Hamzawy, Esmat M. A.

doi:10.1007/s13369-013-0917-2

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Due to the highly hard and abrasive nature of the marble, the wear of the tool during drilling is severe and the machining accuracy of the marble always represents a major challenge in this industry. Extensive research [4][5][6][7] has dealt with the processing theory, the machines/materials characterization, and the tools geometry. In recent years, research efforts have been devoted to the in-depth optimization of the marble block extraction, since this operation can induce geometrical defects, which in turn reduce the effective economic value of the stone considerably.…”

Section: Introductionmentioning

confidence: 99%

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

Abbassi

Akrichi

Yahia

2019

Advances in Mechanical Engineering

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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.

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Section: Introductionmentioning

confidence: 99%