Mechanical and tribological properties of PVD-coated cemented carbide as evaluated by a new multi-pass scratch testing method

A new test method based on multipass scratch testing has been developed for evaluating the mechanical and tribological properties of thin, hard coatings. The proposed test method uses a pin-on-disc tribometer and during testing a Rockwell C diamond stylus is used as the “pin” and loaded against the rotating coated sample. The influence of normal load on the number of cycles to coating damage is investigated and the resulting coating damage mechanisms are evaluated by posttest scanning electron microscopy. The present study presents the test method by evaluating the performance of Ti0.86Si0.14N, Ti0.34Al0.66N, and (Al0.7Cr0.3)2O3 coatings deposited by cathodic arc evaporation on cemented carbide inserts. The results show that the test method is quick, simple, and reproducible and can preferably be used to obtain relevant data concerning the fatigue, wear, chipping, and spalling characteristics of different coating-substrate composites. The test method can be used as a virtually nondestructive test and, for example, be used to evaluate the fatigue and wear resistance as well as the cohesive and adhesive interfacial strength of coated cemented carbide inserts prior to cutting tests.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.