Nowadays, aluminum alloys can be safely welded by using bobbin tool friction stir welding (BT‑FSW) having various advantages over the fusion welding process. This research work was carried out to evaluate the welding process parameters such as tool rotation speed, tool pin diameter and welding speed affecting the mechanical performances of welded AA 6063 sheets. Taguchi technique was used to analyse the influence of process parameters on the hardness of the welded joint. L9 orthogonal array and the analysis of variance was employed to investigate the importance of process parameters for their responses. Experimental results revealed that the hardness of the welded joint was improved with increasing the tool rotational speed and tool pin diameter. The hardness was decreased with increasing the welding speed. The analysed results were verified in the confirmation experiments. The optimal hardness value 92 HV was achieved for the welded joint at the 1200 rpm tool rotation speed, 5.5 mm tool pin diameter, and 50 mm/min welding speed. The FSW process was simulated using Abaqus software to compare the results. The microstructure analysis for different zones of the weld joint and scanning electron microscopy for the welded joint was also performed.
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study focuses on Cupronickel and suggests creating predictive models to anticipate performance metrics in ECM through regression analysis. The experiments are formulated based on Taguchi's principles, and a multiple regression model is utilized to deduce the mathematical equations. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. The proposed prediction technique for Cupronickel is more adaptable, efficient, and accurate in comparison to current models, providing enhanced monitoring capabilities. The updated models have been verified, demonstrating a robust link between empirical data and projected results.
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 19 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 19 provides more flexibility, efficiency, and accuracy in comparison to conventional approaches, allowing for greater monitoring and control in ECM operations. Moreover, the study investigates the potential uses of Titanium Grade 19 in the automotive industry, emphasizing its crucial function in sectors that demand resilient materials in corrosive environments. The experimental validation demonstrates a strong correlation between the projected results and the actual performance, confirming the effectiveness of the ANFIS-based strategy.
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings demonstrated that the GRA approach is an effective tool for determining optimal parameter settings to enhance printing productivity and ensure the production of high-quality components. This study provides deeper insights into the Fused Deposition Modeling (FDM) process for Polyethylene terephthalate glycol (PETG) material, offering valuable strategies for improving manufacturing processes. By leveraging the GRA approach, this work highlights a reliable method for enhancing printing efficiency and quality, thereby promoting the wider adoption of FDM technology across various industries such as prototyping, manufacturing, and healthcare.
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized the manufacturing sector by enabling the production of complex geometries using various materials. Polylactic Acid (PLA) is a biodegradable thermoplastic often used in additive manufacturing (AM) because to its eco-friendliness, cost-effectiveness, and processing simplicity. This research seeks to enhance the parameters of Fused Deposition Modeling (FDM) for PLA material with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The researchers conducted experimental trials to investigate the influence of key FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on essential outcomes such as dimensional accuracy, surface quality, and mechanical qualities. The design of experiments (DOE) technique facilitated a systematic investigation of parameters. The TOPSIS method, a decision-making tool based on several criteria, was used to assess the trial data and identify the optimal parameter values. TOPSIS offers a thorough approach for improving parameters in FDM by considering both proximity to the ideal solution and distance from the negative ideal solution. The findings revealed the effectiveness of the TOPSIS technique in identifying the optimal parameter combinations for enhancing the printing quality and efficiency of PLA components. The proposed optimization framework provides significant insights into the optimization and control of processes, hence promoting the broader use of FDM technology across many sectors. This work improves the understanding of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) and offers effective methods for improving FDM settings. Manufacturers may enhance printing productivity, quality, and sustainability via the use of the TOPSIS methodology. This will subsequently facilitate the broader use of additive manufacturing technologies across many applications.
