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In today’s world slurry handling equipments are facing lot of engineering problems which is namely erosion. Slurry erosion plays a very important role in the design and operation of slurry transportation systems. In the work an attempt is made to reduce slurry erosion by cobalt based clad developed through microwave energy. The microwave clads are developed on AISI 420 substrate with a microwave exposure of 07 min. The developed clad shown great improvement in the erosion resistance than substrate. Slurry erosive wear studies were carried out with focus of three important parameters such as speed, impact angle and time of slurry exposure. ANOVA and control factors analysis studies were carried out with 95% of confidence level. Impact angle shown most dominating parameter on wear rate. Mixed mode of erosion was noticed on the target surface.

The WC–12Co-based clad powder was deposited on the AISI 420 substrate using the microwave energy. The dry sliding action of the WC–12Co-based microwave coating on the substrate AISI 420 was studied using a pin-on-disk testing equipment. The dry sliding wear study was carried out as per Taguchi OA L9. Speed, sliding distance, and load parameters were considered at three various levels. The more wear rate was observed on the unclad surface than the clad surface. Enhanced sliding resistance of the microwave cladding is due to the uniform distribution of carbides. Unclad surface experiences the removal of binder and degradation of carbide grain, while clad surface suffers plastic deformation and undergoes microscale wear.

This paper presents wavelet based recognition of the machined surfaces namely turned, ground and shaped surfaces from the images acquired using Computer Vision System. Selection of mother wavelet has been done based on the peak signal to noise ratio (PSNR) value using Discrete wavelet transform (DWT) which has been used for feature extraction. Artificial neural network has been used to recognize the machined surfaces.