Determination of Cutting Parameters in End Milling Operation based on the Optical Surface Roughness Measurement

Taga O., KIRAL Z., Yaman K.

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, vol.17, no.5, pp.579-589, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 5
  • Publication Date: 2016
  • Doi Number: 10.1007/s12541-016-0070-4
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.579-589
  • Keywords: Artificial neural network, Cutting parameters, Optical sensor, In-line control, Surface roughness, NEURAL-NETWORKS, RECOGNITION SYSTEM, GENETIC ALGORITHM, MULTILAYER FEED, OPTIMIZATION, PREDICTION, MODEL, PARTS
  • Dokuz Eylül University Affiliated: Yes


In this study, a milling system based on the in-line surface roughness measurement during machining process is developed using Artificial Neural Network (ANN) technique. In the proposed system, optimum feed rate and cutting speed are determined by ANN so as to provide the desired surface roughness, which is an important criterion for high quality surface. For this purpose, firstly an algorithm determining the operating principle of the system is developed. According to this algorithm, the optimum cutting parameters are predicted for end milling (finishing) operation by measuring semi-finish machining surface roughness via an optical sensor and then end milling operation is performed with the cutting parameters determined by the system. In the experimental part of this study, surface quality is observed for the milling process before and after the intervention of the system and the results is compared. The experimental results show that the system can be integrated with the modern machining systems in order to obtain the desired surface quality levels.