In the present study, an additive manufacturing process of copper using extrusion 3D printing, solvent and thermal debinding, and sintering was explored. Extrusion 3D printing of metal injection moulding (MIM) feedstock was used to fabricate green body samples. The printing process was performed with optimized parameters to achieve high green density and low surface roughness. To remove water-soluble polymer, the green body was immersed in water for solvent debinding. The interconnected voids formed during solvent debinding were favorable for removing the backbone polymer from the brown body during thermal debinding. Thermal debinding was performed up to 500 °C, and ~ 6.5% total weight loss of the green sample was estimated. Finally, sintering of the thermally debinded samples was performed at 950, 1000, 1030, and 1050°C. The highest sintering temperature provided the highest relative density (94.5%) and isotropic shrinkage. Micro-computed tomography (μCT) examination was performed on green samples and sintered samples, and qualitative and quantitative analysis of the porosity confirmed the benefits of optimized printing conditions for the final microstructure. This work opens up the opportunity for 3D printing and sintering to produce pure copper components with complicated shapes and high density, utilizing raw MIM feedstock as the starting material.

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.