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MECHANICAL DESIGN ON SURFACE ROUGHNESS MUHAMAD FADHLI BIN OTHMAN (MA13016) ABSTRACT The increase of consumer needs for quality metal cutting related products (more precise tolerances and better product surface roughness) has driven the metal cutting industry to continuously improve quality control of metal cutting processes. Surface roughness is a measure of the technological quality of a product and a factor that greatly influences manufacturing cost. Roughness is performance of a mechanical component, since irregularities in the surface may form nucleation soften a good prediction for cracks or corrosion. Although roughness is usually undesirable, it is difficult and expensive to control in manufacturing. INTRODUCTION Nowadays, there is a need for investigating the machining of various types of aluminium and their surface roughness, which in turn can be useful in developing more cost effective personalised products[1]. It was found that the surface roughness is significantly affected by the tip distance followed by the power requirement, cutting speed and material thickness[2]. Surface finish influences not only the dimensional accuracy of machined part, but also the mechanical property of the part, especially the fatigue strength.[13]. Surface effects are caused by differences in surface roughness, microstructure, chemical composition, and residual stress [19,20]. We can use Radian Basis Function Network (RBFN) to predict surface roughness which are more accurate compared by using Response Surface Method (RSM)[3]. In the current work, the response surface methodology has been proven to be a successful technique to perform the trend analysis of surface roughness with respect to various combinations of three design variables. By using the least square method, the first- and second-order models have been developed based on the test conditions in accordance with the Box– Behnken design method[5]. Besides, potential support vector machine (PSVM) is used to develop surface roughness predicted model[6]. There is some theory used to improve and measure the surface roughness which are based on Response Surface Method (RSM) and Radian Basis Function Network (RBFN) where it is very useful to reduce cost and time for machining mould [3]. Besides, we can use Statistical Method especially in Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining [4]. RSM is a combination of experimental and regression analysis and statistical inferences[14]. Response surface method (RSM) is a collection of statistical and mathematical methods that are useful for the modelling and optimization of the engineering problems[15]. A part from that, we can use finite element model (FEM) and analysis to carry out utilizing the finite element analysis [11]. The design work use to improve surface roughness is optimum surface roughness by using milling mould aluminium alloys (AA6061-T6) with Response Ant Colony Optimization (RACO) [1]. Besides, the artificial intelligent model using partial swarm optimization (PSO) to predict the optimum surface roughness when cutting acrylic sheets with laser beam cutting (LBC). Response surface method (RSM) was used to minimize the number of experiments [2]. Box-Behnken design based on response surface method was used to predict the effect of laser cutting parameters including the power requirement, cutting speed and tip distance on surface roughness during the machining [5]. The present study uses average roughness (Ra) for the characterization of surface roughness, due to the fact that it is widely adopted in the industry for specifying the surface roughness. The first linear and quadratic equation used to predict the surface roughness, which is expressed as Eq. (1). Ra = - 0.7059 + 0.0124Pr - 0.0000265Cspeed + 0.016GD where Ra is surface roughness, Pr is the power requirement, Cspeed is cutting speed and GD is the tip distance. From this linear equation, one can easily notice that the response surface roughness is affected significantly by the power requirement, followed by tip distance and cutting speed [16,17]. Lastly, response surface method is found the successful technique to perform the trend analysis of surface roughness with respect to various combinations of design variables include the cutting speed, feed rate, axial depth and radial depth [18]. DISCUSSION The first order model indicates that the feed rate is the most significant factor affecting surface roughness [1]. Surface roughness becomes larger when using low power, tip distance and material thickness. Combination of low cutting speed, high power, tip distance and material distance produce fine surface roughness [2]. Besides, Radian Basis Function Network (RBFN) predict surface roughness more accurately compared to Response Surface Method (RSM). With the model equations obtained, a designer can subsequently select the best combination of design variables for achieving optimum surface roughness. This eventually will reduce the machining time and save the cutting tools [3,4]. In general, within the working range of the power requirement and tip distance considered, the surface roughness increases as the both variables increases. It was found that the surface roughness is significantly affected by the tip distance followed by the power requirement and cutting speed [5]. Highest flank wear results recorded with increasing of cutting depth and cutting speed[12]. Feed rate, axial depth and cutting speed play major role to generate high friction coefficient, friction angle, friction stress, and friction force. When all the variable at highest value the friction stress become larger, on the other hand reduce the feed rate and increase other variable, it cause high friction coefficient, angle and force [7]. Besides, the influence of the peak current, pulse on time and pulse off time on the EDM performance characteristics where it also attempted to formulate mathematical model for the responses such as material removal rate, surface roughness and tool wear rate and finally to detect the optimal settings of the parameters for the same Electric Discharge Machining (EDM) characteristics. As the material removal rate increases, then peak current and pulse on time will increases. The effect of pulse off time on MRR changes with peak ampere. CONCLUSION The optimum machining conditions in favor of material removal rate are verified and compared. The optimum machining conditions in favor of material removal rate are estimated and verified with proposed optimized results. It is observed that the developed model is within the limits of the agreeable error (about 4%) when compared to experimental results. This result leads to desirable material removal rate and economical industrial machining to optimize the input parameters which can effect the surface roughness [8,9]. Futhermore, the nitriding process has the combined effect of producing a higher material strength on the surface and causing the volumetric changes which produce the residual compressive stresses [10].
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