Surface roughness and heat-affected zone (HAZ) are the important features which influence the performance of the laser-drilled products. Understanding the influence of laser process parameters on these responses and identifying the cutting conditions for simultaneous optimization of these responses are a primary requirement in order to improve the laser drilling performance. Nevertheless, no such contribution has been made in the literature during laser drilling of AISI 303 material. The aim of the present work is to optimize the surface roughness (Ra) and HAZ in fibre laser drilling of AISI 303 material using Taguchi-based grey relational analysis (GRA). From the GRA methodology, the recommended optimum combination of process parameters is flushing pressure at 30 Pa, laser power at 2000 W and pulse frequency at 1500 Hz for simultaneous optimization of Ra and HAZ, respectively. From analysis of variance, the pulse frequency is identified as the most influenced process parameters on laser drilling process performance.

Fused deposition modeling (FDM) is one of the additive manufacturing (AM) methods widely used in many divisions, especially medical implants and aerospace, due to capabilities to build complex 3D objects and geometries. However, quality and dimensional accuracy of the FDM parts are significantly influenced by the various FDM process parameters including filament wire material. In the present work, new filament wire material Thermoplastic Polyurethane (TPU) was utilized to produce FDM parts. Hence, deciding the optimum process parameters is very critical to produce the FDM parts with good surface quality (Ra) and dimensional accuracy (Δd) concurrently using TPU material. In this paper, the author has contributed to determine the optimum 3D printing process parameters to improve the quality and accuracy for the new filament wire material Thermoplastic Polyurethane (TPU) using multi-attribute decision making (MADM) methods namely Gray Relational Analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS). Further, the results of GRA and TOPSIS techniques were compared and concluded that TOPSIS method substantially reduced the surface roughness to a value of 12% contrast to the GRA method whereas the dimensional deviation accuracy increased to 6.25% over the GRA method.

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In the present work, two sustainable machining techniques namely Minimum Quantity Lubrication (MQL) and dry machining were investigated during turning of AISI D2 steel material using tungsten carbide tools. Cutting velocity, feed rate and depth of cut were considered as turning process variables whereas cutting temperature, tool rake wear, tool flank wear and surface roughness were taken as turning process performance characteristics for investigation purpose. Based on the obtained results it was found that MQL machining technique significantly controlled the cutting temperature, tool rake wear, tool flank wear and surface roughness values to a maximum of 46%, 22%, 23% and 35% when compared to dry machining condition. It was noticed that MQL cooling technique uses tiny quantity of coolant and contributes for sustainable requirements in present industry. Further, it was observed that more chip entanglement marks as major surface defectives and edge chipping as major tool wear mechanism in dry machining.

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