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This paper presents methodology to identify the surface roughness value in CNC machining process using a soft computing approach. The aim of this paper is to achieve a roughness accuracy value above 95% and reduce the error rate to below 5% by using an artificial neural network. An artificial neural network method was selected to improve the time of inspection. Fourier transformation method will be used to extract the turning workpiece image, which is the squared value of the major frequency and principal component magnitude. Primary machining parameters such as feed rate, depth of cut, speed, frequency range, gray scale value, and conventional measurement value feed are used as the training input in the artificial neural network. Based on the training sample, the artificial neural network generates the vision measurement value for the testing samples that is compared to the stylus probe measurement value to predict the error rate and accuracy. The novelty of this work is to create an effective methodology using artificial neural network techniques to detect surface roughness errors of materials used in manufacturing industries.

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Today’s automotive designers and material specialists regard lighter vehicles for less fuel consumption (economy and ecology) and higher safety to passengers. Metal matrix composites have been a large area of interest. Aluminium composite is potentially applied in automotive and aerospace industries, because it has a superior strength to weight ratio and is a light weight metal with high temperature resistance. Composites containing hard oxides and ceramics (such as alumina) are preferred for high wear resistance along with increased hardness. In this work, alumina and zinc are reinforced in Al-LM25 alloy through stir casting process, where alumina is varied 6% and 12% in Al-5%Zn. Various mechanical analyses were conducted and the effect of wear with different percentage of alumina reinforcement was studied. The resulting properties are imported in a piston, modelled using solid works, and analysed in ANSYS work bench. Imparting this new material for pistons could introduce deep design and improvements in engine operation of a vehicle.

The aim of the study is to predict the surface topological characteristics of Al-B4C composite electrodes and the OHNS Die steel in the Electrical Discharge Machining (EDM) Process. The surface characteristics of Composite electrodes are evaluated by using Scanning Electron Microscopy (SEM) and EDAX Analytical Method. Surface roughness and hardness of the OHNS die steel was measured by the Stylus probe and Brinnel hardness. The composite electrodes prepared by the Aluminium 6063 and B4C materials. Both elements are mixed at molten state in the stir casting process at different compositions. The chemical composition properties of the Composite electrode is analyzed by the SEM and EDAX testing. The surface Roughness of the OHNS steel measured by the Brinell hardness tester. Based on the SEM and EDAX results, the 92% Al 8% B4C was producing the good surface roughness in OHNS die steel.

This paper proposes an efficient methodology for predicting surface roughness using different soft computing approaches. The soft computing approaches are artificial neural network, adaptive neuro-fuzzy inference system and genetic algorithm. The proposed surface roughness prediction procedure has the following stages as feature extraction from the materials, classifications using random forests, adaptive neuro-fuzzy inference system (ANFIS). In this paper, the statistical features are extracted from material images as skewness, kurtosis, mean, variance, contrast, and energy.The surface roughness accuracy value varied between ANFIS and random forest classification in every measurement sequence. This limitation can be overcome by the genetic algorithm to optimize the best results. The optimization technique can produce more accurate surface roughness results for more than 98% and reduce the error rate up to 0.5%.