Modelling and prediction of thrust force and torque in drilling operations of AI7075 using ANN and RSM methodologies
Resumen: Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually earned out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an AI7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM- and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.
Idioma: Inglés
DOI: 10.5545/sv-jme.2017.5188
Año: 2018
Publicado en: STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 64, 6 (2018), 351-361
ISSN: 0039-2480

Factor impacto JCR: 1.139 (2018)
Categ. JCR: ENGINEERING, MECHANICAL rank: 94 / 129 = 0.729 (2018) - Q3 - T3
Factor impacto SCIMAGO: 0.401 - Mechanics of Materials (Q2) - Mechanical Engineering (Q2)

Tipo y forma: Artículo (PostPrint)
Área (Departamento): Área Expresión Gráfica en Ing. (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Ing. Procesos Fabricación (Dpto. Ingeniería Diseño Fabri.)


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