Using RSM Technique for Modeling and Optimization the Influence of Cutting Parameters on Tool Wear and Cutting Forces in Turning Operation

  • Shaker S. Hassan Department of Mechanical Engineering, University of Technology, Baghdad, Iraq
  • Samir A. Amin Department of Mechanical Engineering, University of Technology, Baghdad, Iraq
  • Asaad. A. Dabish Department of Mechanical Engineering, University of Technology, Baghdad-Iraq

Abstract

This study is an attempt to investigate the effect of cutting parameters on the cutting force and tool wear during turning of AISI 304 steel using tungsten carbide tool (WC). The first aim of present work was to employ the Response Surface Methodology (RSM) technique to obtain the influence of input machining parameters, such as cutting feed, cutting speed and cutting depth on the cutting force and wear of tool. Experiments were carried out in a 20 runs experimental matrix by a CNC machine according to the design matrices established by Design of Experiment (DOE) software 'version 8' with RSM technique. Cutting force was measured using a lathe dynamometer and tool wear with the help of an optical microscope. The relationships between parameters of machining and the responses (cutting tool wear and cutting force) were modeled and analyzed by RSM technique. ANOVA analysis was applied to study the impact of machining parameters on the outputs (responses) and to establish empirical equations for these responses in terms of input machining parameters. Significant quadratic models were developed with a probability (p-value ≤ 0.05) for both tool wear and cutting force. Results showed that the depth of cut is the most significant factor affecting the cutting force, closely followed by feed and cutting speed, whereas only the important parameter influencing the tool wear was appeared to be the cutting depth. Also, the results manifested that the optimum value for minimum tool wear and minimum cutting force was found at (80 m/min) cutting speed, (0.2 mm/rev) feed and (0.4 mm) cutting depth. A good agreement was found between the experimental and predicted results with a maximum error of 8%.
Published
2019-04-02
How to Cite
Hassan, S. S., Amin, S. A., & Dabish, A. A. (2019). Using RSM Technique for Modeling and Optimization the Influence of Cutting Parameters on Tool Wear and Cutting Forces in Turning Operation. Engineering and Technology Journal, 36(3 A). Retrieved from http://engtechjournal.org/index.php/et/article/view/151