Machining Modeling: A Comparative Study: Comparison of Predictive Cutting Models Using RSM and RNA Approaches - Softcover

Bouzid, Lakhdar; Yallese, Mohamed A; Maaoui, Said

Softcover

ISBN 10: 6630113667 ISBN 13: 9786630113662

Verlag: Our Knowledge Publishing, 2026

Inhaltsangabe

During machining, the conditions for success are determined in the cutting zone. In this context, the relationship between cutting conditions (cutting speed, feed rate, depth of cut, and tool type) and surface roughness (Ra and Rt) represents a major industrial objective. This work focuses on an experimental study examining the effects of cutting parameters on surface roughness, obtained during the machining of 42Cd4 steel (AISI 4140) hardened to 60 HRC, machined via dry hard turning using ceramic and metal carbide tools. The study is divided into two parts: the first focuses on proposing mathematical models to predict surface roughness, based on two modeling methods: the response surface methodology (RSM) and artificial neural networks (ANNs). The second part involves comparing the results of the two modeling methods-RSM and ANNs-in order to select the most effective one. Based on the comparative study, the ANN models proved to be better at predicting surface roughness within the range of the experiments conducted than the SRM models.

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Über die Autorin bzw. den Autor

Born on June 7, 1986, in Mila (Algeria), Lakhdar Bouzid holds a Ph.D. in mechanical engineering, specializing in applied mechanics. He is currently a member of the Laboratory (LMS) at the University of Guelma and an associate professor in the Department of Mechanical Engineering at Larbi ben M'Hidi University in Oum el Bouaghi, Algeria.

Details zur Ausgabe

• Verlag: Our Knowledge Publishing
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 6630113667
• ISBN 13: 9786630113662
• Einband: Taschenbuch
• Anzahl der Seiten: 68
• Kontakt zum Hersteller: Verlag Unser Wissen; customerservice@vdm-vsg.de; ; Brivibas gatve 197; Riga; 1039; Lettland