Composite is an artificial multiphase material that constituent phases which are chemically dissimilar and separated by a distinct interface. As a result of such anisotropic and non-homogeneous formation machining of composites is a strenuous task. Therefore, the need arose for formulating a stable predictive model. The comprehensive intention of this study is to develop a highly robust and stable predictive soft-computing model for forecasting the machining performance of Al-5083 alloy reinforced with B 4 C particles (Al5083/B 4 C). The study mostly emphasize on selecting the best machining parameters among the conducted experiments and evaluating the optimal machining parameters for Al5083/B 4 C composite in wireelectro discharge machin. The paper is an integration of equally weighted experimental as well as computational study. In the experimental part of the study, 5 different specimen of Al5083/B 4 C is prepared by the ex situ technique through stir casting process. The experimental part includes design of experiment by Taguchi's method. The computational part of the study comprised of three different stages. The first stage involves the mathematical modelling of the performance measures and statistical scrutiny of the models. In the second stage, the best machining parameters are selected based on the fuzzy IFthen rules. The final stage of the manuscript is the trade-off analysis conducted to obtain the optimal machining parameters. In order to test the robustness of the formulated model an experimental validation is carried out at the optimal machining combination. The error calculated from the comparison is within the range of 2-5% which justifies the objective of the study.
Additive manufacturing (AM) of metals attracts attention because it can produce complex structures in a single step without part-specific tooling. Wire arc additive manufacturing (WAAM), a welding-based method that deposits metal layer by layer, is gaining popularity due to its low cost of operation, feasibility for large-scale part fabrication, and ease of operation. This article presents the fabrication of cylindricalshaped mild steel (ER70S-6) samples with a gas metal arc (MIG)—based hybrid WAAM system. A mechanism for actively cooling the substrate is implemented. Deposition parameters are held constant to evaluate the impact of active cooling on deposition quality, inter-pass cooling time, and internal defects. Surface and volume defects can be seen on the cylindrical sample fabricated without an active cooling setup. Defect quantification and phase analysis are performed. The primary phase formed was α-iron in all samples. Actively cooled deposition cross section showed a 99% decrease of incomplete fusion or porosity, with temperature measured 60 s after deposition averaging 235°C less than non-cooled. Microstructural analysis revealed uniformity along the build direction for actively cooled deposition but non-uniform microstructures without cooling. Hardness decreased by approximately 22HV from the first layer to the final layer in all cases. Property variation can be attributed to the respective processing strategies. The current study has demonstrated that active cooling can reduce production time and porosity while maintaining uniform microstructure along the build direction. Such an approach is expected to enhance the reliability of WAAM-processed parts in the coming days.
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