With the introduction and increased use of newer and harder materials such as titanium, stainless steel, high-strength temperature-resistant (HSTR) alloys, fibre-reinforced composites, and ceramics in the aerospace, nuclear, missile, turbine, automobile, tool, and die-making industries, a different class of machining processes has emerged. Instead of employing the conventional cutting tools, these non-traditional machining (NTM) processes use energy in its direct form to remove materials from the workpiece. Selection of the most suitable NTM process for machining a shape feature on a given work material requires consideration of several factors. A combined method using the ‘technique for order preference by similarity to ideal solution’ (TOPSIS) and an analytical hierarchy process (AHP) is proposed to select the most appropriate NTM process for a specific work material and shape feature combination, while taking into account different attributes affecting the NTM process selection decision. This paper also includes the design and development of a TOPSIS-AHP-method-based expert system that can automate the decision-making process with the help of a graphical user interface and visual aids. The expert system not only segregates the acceptable NTM processes from the list of the available processes, but also ranks them in decreasing order of preference. It also helps the user as a responsible guide to select the best NTM process by incorporating all the possible error-trapping mechanisms.

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Precision sensing in the characterization of complex additive manufacturing processes such as the Automated Fibre Placement (AFP) technique is important since the process involves a significant level of uncertainty in terms of quality and integrity of the manufactured product. These uncertainties can be monitored by embedding optical fibre Bragg grating (FBGs) sensors which provide accurate and simultaneous measurement of strain and temperature during the AFP process. The embedded sensors have been shown to remain resilient in continuous health monitoring after manufacturing. The thermal history obtained from the FBG sensors demonstrates a reduction of temperature on the bottom ply by up to 25% when the plies are laid one above the other. A numerical tool is developed to identify the physical parameters which may be responsible for the rise/fall of the temperature during ply layup. The numerical findings agree well with the sensor data and is extended to capture a breadth of parametric studies through the layup simulation. The model provides a comprehensive insight to the characteristics of the laid and the laying ply from a thermo-mechanics perspective.

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