Journal of Scientific & Industrial Research Vol. 78, March 2019, pp. 166-172 Optimization and Prediction of Cutting Parameters in the End Milling Process for Cast Aluminium B 4 C Based Composite R Singh 1 *, M Shadab 2 and R N Rai 3 1, 2,3 Department of Production Engineering, National Institute of Technology, Agartala, India Received 16 June 2018; revised 22 December 2018; accepted 24 January 2019 End milling process is a very common and important machining process not only due to its ease of machining but also due to the availability of various cutter profiles and curved surfaces. This research work investigates the effect of various process parameters, such as rotational speed of the cutting tool, feed rate, depth of cut on the machined surface of the composite, experimentally. The composite material is synthesized by using the stir casting process with reinforcement of B 4 C particulate into Al5083 aluminium alloy. The Taguchi design of experiments is used to calculate the optimum process parameters for machining with minimum variability. In this study, RSM (Response surface methodology) based equation is applied to Teaching-Learning-Based Optimization (TLBO) algorithm to optimize the process parameters. The mathematical model is developed with a confidence level of 95% with a prediction error of less than ±5%. The efficiency and effectiveness of the TLBO algorithm has been observed with the help of convergence graph of the value outcome from experiment. The optimized results obtained from TLBO are almost nearer to the average results of 10 runs. Keywords: End Milling , TLBO , RSM , Al5083, Taguchi Introduction Aluminium alloy, Al5083 (Al-Mg) is a well-known non-heat treatable alloy. The Al-Mg alloy is achieving its strength by the use of solid-solution strengthening technique, in the presence of Magnesium. It is best suited for marine and automobile industry due to its better design efficiency, good welding properties, good resistance against corrosion, high strength to weight ratio, and low cost. In any machining operation, the performance of machining operation is evaluated in terms of its power consumed, tool wear, cutting force (CF), and surface finish, of which the quality of surface finish plays an important role in any machined component. Develop a mathematical model for surface quality by using parameters, such as rotational speed of the tool, feed rate, and depth of cut, and then applying the TLBO optimization technique to analyse the cutting parameters. In addition, analysis of variance (ANOVA) is applied to determine the effects of other diverse factors on the cutting parameters. Materials There has been an increase in demand by the present-day manufacturing industry for parts with high accuracy. To full fill this demand for high accuracy parts, it is necessary for manufacturers to make high quality components, such that those parts can better achieve the functional as a well as the operational requirements of the components 1 . Masmiati et al. investigated the influence of machined surface inclination, depth of cut rotational speed of tool and feed rate in inclined end milling process and found that machined surface inclination angle had high impact on micro hardness and residual stress along the feed direction 2 . Saharea et al. examined the optimum coating condition for the quality of surface finish and metal removal rate by varying cutting speed, feed rate, and depth of cut 3 . Gopal & Prakash examined the effect of material and machining parameters on the surface roughness and CF 4 . Suresh et al. studied the hybrid metal matrix composite (Al-SiC-Gr) where the reinforced graphite particle is mainly to improve the machinability and tribological properties of the composite. In their results they found that the Al-10% (SiC-Gr) composite shows better properties when compared with Al-5% (SiC-Gr) and Al-7% (SiC-Gr) 5 . Maher et al. studied the adaptive neuro-fuzzy inference system (ANFIS) which is used as prediction tool to predict the surface roughness of end milling process in CNC milling and the predictor variables are rotational speed of tool, feed rate and —————— *Author for Correspondence E-mail: ramsingh650@gmail.com