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There is a continuous quest in the research community for superior and more accurate methodology for fault diagnosis and condition monitoring of diverse composite structure. This is because, these structures suffer from various nonlinear mode of failures while in service those are recognised as delamination, voids, matrix crack etc. Early detection of failures is what the most research mainly aims at. In this regard, the implementation of Artificial Intelligence (AI) techniques has been proved to be a versatile method for damage assessment. The collective inevitable use of composite materials in various high-performance engineering industries requires preliminary testing (detection, location, and quantification) for damage to these materials in order to improve their integrity and order. The present paper aims to bring out a concise review on various methodologies employed for damage/fault detection in composite materials with a special emphasis on supervised and unsupervised machine learning techniques. The major observations are outlined with an objective to put forward a broad perspective of the state of art related to laminated composite structural heath monitoring.

Owing to minimum quantity or no use of toxic coolants, the dry machining technique has been evidenced to be a versatile sustainable method. However, during dry machining of ductile alloys, the severe tool wear and metal adhesion on the rake face of the cutting tool has been a matter of great concern. In the present work, an attempt has been made to assess the improvement in the tribological conditions in dry cutting by providing surface texturing on the rake face of High-Speed Steel (HSS) cutting tool. Dimples were produced on the rake surface of the HSS tool using pulsed Nd: YAG Laser and dry turning of pure aluminium is performed using the textured tool based on Taguchi’s L9 orthogonal array (OA) experimental design. The dry cutting of pure aluminium was also performed using the conventional/un-textured tool and the obtained results are used for comparison purpose. Improved turning performance in terms of material removal rate and surface roughness is found from the conformation tests using optimum process parameter determined by the Taguchi analysis. The ANOVA results suggests the effectiveness of using the textured tools during dry machining is significantly affected by feed and speed.

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