Environmental and human-friendly welding is the need of the hour. In this context, this study explores the application of the regulated metal deposition (RMD) technique for ASTM A387-Gr.11-Cl.2 steel plates. To examine the effect of metal-cored filler wire (MCFW), MEGAFIL 237 M was employed during regulated metal deposition (RMD) welding of 6 mm thick ASTM A387-Gr.11-Cl.2 steel plates. The welding was carried out at an optimized current (A) of 100 A, voltage (V) of 13 V, and gas flow rate (GFR) of 21 L/min. Thereafter, the as-welded plates were examined for morphological changes using optical microscopy. Additionally, the micro-hardness of the as-welded plates was measured to make corroboration with the obtained surface morphologies. In addition to this, the as-welded plates were subjected to heat treatment followed by surface morphology and micro-hardness examination. A comparison was made between the as-welded and heat-treated plates for their obtained surface morphologies and microhardness values. During this, it was observed that the weld zone of as-welded plates has a dendritic surface morphology which is very common in fusion-based welding. Similarly, the weld zone of heat-treated plates has a finer and erratic arrangement of martensite. Moreover, the obtained surface morphologies in the weld zone of as-welded and heat-treated plates have been justified by their respective hardness values of 1588.6 HV and 227.3 HV.
In metal-cutting operations, the surface roughness of the end product plays a significant role. It not only affects the aesthetic appearance of the end product but also signifies the product’s performance in the long run. Products with a high surface finish have higher endurance limits with negligible local stresses. On the other hand, products with rough surfaces are subjected to high stresses when they are engaged in various mechanical operations with varying loads. Surface roughness depends on various machining factors such as feed rate, depth of cut, cutting speed, or spindle speed. Therefore, it is required to predict surface roughness for the given machining parameters to reduce the cost and increase the life of the end product. In this work, an attempt has been made to evaluate the surface roughness of AZ91 alloy during the end milling operation. In this regard, various state-of-the-art ensemble learning models have been adopted and compared with the proposed hybrid ensemble model. The proposed hybrid ensemble model is the integration of random forest, gradient boosting, and a deep multi-layered neural network. In order to evaluate the performance of the proposed model, comparative analyses have been made in terms of mean square error, mean average error, and [Formula: see text] score. The result shows that the proposed hybrid model gives minimum error for surface roughness.
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