The role of stepover ratio in prediction of surface roughness in flat end milling |
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| Authors: | Eyüp Sabri Topal |
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| Affiliation: | 1. Key Laboratory of Low-grade Energy Utilization Technologies and Systems (Chongqing University), Ministry of Education, Chongqing 400030, China;2. Institute of Engineering Thermophysics, Chongqing University, Chongqing 400030, China;1. College of Marine Life Science, Ocean University of China, Qingdao 266003, China;2. Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China;3. Department of Life Sciences, Natural History Museum, London SW7 5BD, UK;1. Institute of Research and Development, Duy Tan University, 03 Quang Trung, Da Nang 550000, Viet Nam;2. Faculty of Mechanical Engineering, Le Quy Don Technical University, 236 Hoang Quoc Viet, Ha Noi 100000, Viet Nam;1. Department of Surgery, Loma Linda University Medical Center, Loma Linda, California;2. Department of Surgery, University of California, Riverside School of Medicine, Riverside, California;1. University of Strathclyde, Department of Mechanical and Aerospace Engineering, Glasgow, UK;2. University of Bath, Department of Mechanical Engineering, Bath, UK |
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| Abstract: | Surface roughness prediction studies in end milling operations are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The stepover ratio is usually neglected without investigating it. The aim of this study is to discover the role of the stepover ratio in surface roughness prediction studies in flat end milling operations. In realising this, machining experiments are performed under various cutting conditions by using sample specimens. The surface roughnesses of these specimens are measured. Two ANN structures were constructed. First of them was arranged with considering, and the second without considering the stepover ratio. ANN structures were trained and tested by using the measured data for predicting the surface roughness. Average RMS error of the ANN model considering stepover ratio is 0.04 and without considering stepover ratio is 0.26. The first model proved capable of prediction of average surface roughness (Ra) with a good accuracy and the second model revealed remarkable deviations from the experimental values. |
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