http://dspace.dtu.ac.in:8080/jspui/handle/123456789/109 2026-04-28T04:03:29Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22694 Title: STUDY OF THERMAL AND METALLURGICAL ASPECTS OF FRICTION STIR WELDED JOINTS OF DISSIMILAR METALS Authors: GAURAV, SHWETANSHU; Zunaid, Mohammad (SUPERVISOR); Mishra, Radhey Shyam (CO-SUPERVISOR) Abstract: Multi-material Dissimilar friction stir welding (FSW) of magnesium AZ91D and aluminium AA6061-T6 poses significant challenges due to metallurgical incompatibility. Despite their potential in lightweight applications, comprehensive optimization of process parameters in SiC-reinforced AZ91D and AA6061-T6 FSW joints remains limited. The present investigation comprehensively examines the combined effects of process parameters and nanoparticle reinforcement on the mechanical and metallurgical behavior of dissimilar AZ91D/AA6061-T6 joints. SiC particles were introduced into pre-configured grooves, and experiments were carried out by varying volume fraction (Vf) (5%,10%,15%) of SiC, tool rotational speed (TRS) (600,700,800 rpm), and traverse speed (TS) (20,30,40 mm/min). This study investigates the influence of Tool Rotational Speed (TRS), Traverse Speed (TS), and SiC Volume fraction (Vf) on the mechanical and metallurgical properties of these joints using Central Composite Design (CCD) of Response Surface Methodology (RSM) to mathematically model FSW input parameters with key mechanical responses such as ultimate tensile strength (UTS), strain, and microhardness. The analysis of variance (ANOVA) approach identified critical parameters. It validated the model’s prediction with a 95% confidence interval (CI), yielding R² values of 0.9973 for Ultimate Tensile Strength (UTS), 0.9319 for % strain, and 0.9951 for microhardness, indicating excellent predictive capability. Optimization revealed that the optimal microhardness, strain, and UTS in the stir zone (SZ) were 88.44 HV0.1, 6.45%, and 114.56 MPa, respectively. Microstructural analysis revealed that the SiC nanoparticles significantly refined the grains in the stir zone (SZ). This refinement was primarily due to the pinning effect of nano-sized SiC particles, which restricted grain growth and facilitated dynamic recrystallization (DRX) during FSW, ultimately leading to a substantial reduction in grain size. Increasing SiC Vf from 5% to 15% enhanced friction stir-welded (FSWed) joints and exhibited improved mechanical characteristics. Among systematically designed experiments, the condition with 700 rpm, TRS 30 mm/min, and Volume fraction 15% achieved the highest UTS (114.98 MPa) and strain (6.67%) along with microhardness of 88.9 HV0.1, closely matching the model’s predicted optimum and vi confirming its accuracy and robustness across the design space. In contrast, the lowest UTS of 71.46 MPa, 32.63% joint efficiency, and 67.87 HV0.1 microhardness was recorded at 600 rpm, 40 mm/min, and 5% Vf SiC, mainly due to inadequate mixing and particle clustering, confirming the strengthening role of SiC. These findings illustrate the potential of SiC nanoparticle reinforcement in enhancing the mechanical properties of FSWed joints and demonstrate that the weld quality in AZ91D and AA6061-T6 joints strongly depends on parameter selection to ensure uniform particle distribution and defect-free welds. Complementing the experimental investigation, a three-dimensional transient thermal simulation was performed using ANSYS, considering heat generation from both frictional sliding and plastic deformation at the tool-workpiece interface. The model was validated using thermocouple measurements placed on both the advancing and retreating sides of the joint, showing a deviation of less than 5% between the simulated and experimental temperature histories. The simulation results indicated that heat input increased with tool rotational speed and decreased with traverse speed, directly correlating with the observed material flow, grain refinement, and IMC morphology. The optimal condition (700 rpm, 30 mm/min) yielded a balanced heat input, which is sufficient for full plasticization without melting, thereby explaining the superior mechanical performance and defect-free weld morphology. Overall, this integrated experimental-numerical-statistical framework establishes a comprehensive process-structure-property relationship for dissimilar AZ91D/AA6061- T6 FSW joints reinforced with SiC nanoparticles. The study not only validates the predictive capability of CCD-RSM for optimizing FSW parameters but also highlights the vital role of thermal input and nanoparticle dispersion in achieving defect-free, AZ91D/AA6061-T6 dissimilar joints. The outcomes provide a reliable foundation for extending nanoparticle-assisted FSW toward advanced lightweight hybrid structures in automotive and aerospace applications. 2026-03-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22693 Title: INVESTIGATION OF EROSIVE WEAR IN METALLIC PIPE BENDS Authors: YADAV, BHARAT SINGH Abstract: For the transportation of powders from one place to another, pneumatic Conveying is widely used in industries. Pharmaceutical Industries, chemical industries, Ash Handling work, etc., had applications of Pneumatic Conveying. During conveying, due to the bombardment of particles on the bend inner surface for changing the direction of particles, bend erosion occurs, and bend puncture is a major problem in this system. To reduce the bending erosion, researchers are working on various factors that affect the bending erosion rate, like the hardness of particles, impingement angle, bend geometry, bend material, etc, but direct bombardment of particles at 30° angle is a severe bend erosion condition. Researchers are focused on inventing a device to change the mechanism to induce swirl flow before the bend so that bombardment of particles can be reduced, leading to a reduction in erosive wear. In this research, motor-operated Swirling devices and Auto swirl devices are used to reduce erosive wear in pipe bends. CFD numerical prediction and experiments are performed to find the efficiency of the auto swirl and motor-operated swirling device. A range of 20% to 50 % reduction in erosive wear with different parameters, like the mean effective particle size and rpm of the swirling device. The motor- operated swirling device was found to be more effective than the auto swirling device. With fine powders swirling, the device was found significantly effective with a 50% reduction in erosive wear. Experiment results validate the CFD numerical prediction with 10% more erosive wear in experimentation. A significant amount of reduction in bend erosion rate is the outcome of this research, but enhancement of design and experimentation can make these devices more effective. Auto Swirl Device can be more effective with different fan blade angle and this device is very energy efficient but, in this research, found less effective due to experimentation limitations. 2026-03-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22687 Title: OPTIMIZATION OF SCHEDULING TECHNIQUES IN FMS IN THE CONTEXT OF INDIAN INDUSTRY Authors: KAUR, GAGANPREET; Mishra, R.S. (SUPERVISOR); Madan, A.K. (CO-SUPERVISOR) Abstract: In today’s rapid and highly demanding production environment, manufacturing enterprises are increasingly adopting Flexible Manufacturing Systems (FMS) to enhance productivity, responsiveness, and operational efficiency. An FMS represents an integrated and automated manufacturing environment that combines programmable machine tools, automated material handling systems, automated storage and retrieval units, and centralized computer control to efficiently manufacture a wide variety of components. As a cornerstone of Industry 4.0, FMS plays a critical role in enabling intelligent, data-driven, and adaptive manufacturing systems capable of responding to dynamic market demands. A fundamental challenge in FMS operation is scheduling optimization, which involves determining the optimal sequence of jobs while allocating limited and interdependent resources such as machines, tools, and Automated Guided Vehicles (AGVs). Effective scheduling significantly enhances system performance by reducing make span, minimizing tardiness, improving resource utilization, lowering operational costs, and increasing throughput. In the context of Industry 4.0, scheduling optimization becomes even more crucial, as it directly supports the realization of smart, autonomous, and energy-efficient manufacturing environments. Traditional optimization approaches such as linear programming, dynamic programming, and exact mathematical models have been widely used for manufacturing scheduling. However, due to the combinatorial complexity, non-linearity, multi-objective nature, and large search spaces inherent in FMS, these methods often become computationally infeasible for real-world applications. In contrast, metaheuristic algorithms, inspired by natural and evolutionary processes, provide robust and scalable alternatives. Their ability to balance exploration and exploitation, handle multiple conflicting objectives, and adapt to complex constraints makes them particularly suitable for FMS scheduling problems. With recent advances in computational power and intelligent optimization strategies, metaheuristics have emerged as powerful tools for addressing large-scale industrial scheduling challenges. In this research, three novel hybrid metaheuristic methodologies are proposed to address simultaneous scheduling problems in FMS under Industry 4.0 paradigms: 1. Dynamic-Particle Multi-Swarm Optimization (Dy-PSO) 2. Novel Variant of Particle Swarm Optimization (NvPSO) and Walrus Optimization Algorithm (WaOA) 3. Novel Genetic and Adaptive Artificial Bee Colony Algorithm (NG-AABCA) vii The first study introduces Dynamic-Particle Multi-Swarm Optimization (Dy-PSO), a novel scheduling framework primarily focused on makespan minimization in FMS. Dy-PSO employs multiple interacting swarms with adaptive parameter control to prevent premature convergence and stagnation. A spatial exclusion strategy is incorporated to avoid redundant exploration of previously visited regions of the solution space. A significant methodological contribution of this study is the integration of machine learning, where a Random Forest Regressor, enhanced through Genetic Algorithm–based learning, is used to predict and guide scheduling decisions. This hybridization of evolutionary optimization and predictive learning establishes a data-driven scheduling framework that enhances solution quality and convergence speed. Dy-PSO is evaluated under three realistic scheduling scenarios: (i) simultaneous scheduling of jobs, tools, and AGVs, (ii) scheduling without tool constraints, and (iii) scheduling integrated with machine learning assistance. Comparative analysis with conventional PSO demonstrates substantial reductions in makespan and superior robustness across all scenarios. The second study focuses on energy-aware scheduling through a Novel Variant of PSO (NvPSO) and the Walrus Optimization Algorithm (WaOA). This study addresses the growing need for sustainable manufacturing by simultaneously minimizing makespan, tardiness, and total energy consumption. NvPSO incorporates logistic map–based parameter tuning, enhancing diversity and search efficiency. Experimental results across 13 diverse job sets show that NvPSO achieves up to 9% reduction in energy consumption while completely eliminating tardiness penalties, outperforming conventional algorithms such as the Artificial Immune System (AIS) and Modified Genetic Tabu Algorithm (MGTA). While WaOA demonstrates faster convergence and lower computational complexity, NvPSO consistently delivers superior solution quality for larger and more complex scheduling instances, highlighting its suitability for energy-efficient Industry 4.0 manufacturing systems. The third study proposes the Novel Genetic and Adaptive Artificial Bee Colony Algorithm (NG- AABCA) for minimizing makespan, penalty costs, and total tardiness. NG-AABCA introduces cognitive (ε₁) and social (ε₂) learning mechanisms, which are generally underutilized in classical ABC algorithms, to exploit global knowledge and enhance convergence behavior. The algorithm further integrates Genetic Algorithm elitism and Random-Restart Hill-Climbing, effectively balancing solution diversity and intensification. Computational results demonstrate that NG- AABCA achieves a 5.3% reduction in makespan and an 8.7% reduction in tardiness compared to conventional metaheuristics, resulting in improved productivity and more efficient utilization of manufacturing resources. viii Extensive computational experiments were conducted using MATLAB R2019a on an Intel Core™ i7 platform, and the proposed algorithms were validated across benchmark datasets as well as realistic FMS configurations. The results confirm that the suggested hybrid metaheuristic approaches consistently outperform traditional optimization methods in terms of solution quality, convergence speed, robustness, and scalability. In several cases, the algorithms identified new best-known makespan values, demonstrating their effectiveness in exploring high-quality solution spaces. Overall, this research makes significant contributions by developing adaptive, hybrid, and energy-aware scheduling frameworks that address the complexities of simultaneous resource scheduling in FMS. The integration of metaheuristics, machine learning, and sustainability objectives positions the proposed approaches as powerful decision-support tools for Industry 4.0–enabled smart manufacturing, offering both theoretical advancements and strong potential for industrial applicability. 2026-02-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22685 Title: INTEGRATED CHEMICAL TREATMENT AND MAGNETO-RHEOLOGICAL FINISHING OF TITANIUM ALLOY FOR BIOMEDICAL APPLICATIONS Authors: NAJI, FADIA AHMED ABDULLAH; QASIM, Murtaza (SUPERVISOR); Niranjan, M.S. (CO-SUPERVISOR) Abstract: Nano-finishing processes have transformed industrial surface finishing by offering significant opportunities to enhance surface integrity, particularly in biomedical applications. With the rising demand for artificial implants, expectations and standards for surface roughness are continually increasing. Achieving a consistent nanoscale finish remains a critical challenge, especially for replacement implant components such as femoral, knee, elbow, and hip joints, which must comply with ISO 7206-2:2011/AMD 1:2016 standards. Ti-6Al-4V (Grade 5) alloy is widely used in biomedical implants due to its superior combination of strength, toughness, corrosion resistance, biocompatibility, and relatively low density. Its α+β microstructure further enhances adaptability for biomedical applications. However, conventional finishing methods are effective for macro- and micro-scale finishing but are insufficient for achieving nanoscale precision, often leading to surface flaws such as cracks. To overcome these limitations, advanced finishing techniques such as magneto-rheological finishing (MRF) and abrasive flow finishing (AFF) have been developed. While they improve precision, challenges persist, including low finishing rates, microcrack formation, surface degradation, and reliance on hazardous chemicals, which raise concerns about safety and the environment. Future processes must therefore be more efficient, sustainable, and capable of achieving nanoscale precision without compromising material integrity or biocompatibility. This thesis develops two advanced processes for surface modification of Ti64 alloy for biomedical implant applications by sustainable, eco-friendly chemicals, including citric acid, Hydrogen peroxide, and Sodium hydroxide. Firstly, a novel thermochemical process (THCP) was developed, followed by simulated mineralization in Hank's Balanced Salt Solution (HBSS) to enhance the mechanical properties, biocompatibility, and bioactivity of Ti64 alloy. The study investigates the effects of eco-friendly chemicals on surface characteristics by XIX evaluating their ability to modify surfaces and determining the optimal pH value. The findings demonstrated that the modification effectively enhanced the Ti64 alloy's mechanical properties, with a significant increase in average microhardness of approximately 71% and a reduction in wear rate of approximately 64.29%, compared to the untreated Ti64 alloy. The surface modification with pH (5, 7, and 9) revealed absorptive properties, as evidenced by a contact angle below 90 °, indicating a hydrophilic surface that enhances cell attachment to biomaterials. After soaking Ti64 alloy in HBSS, a uniform coating layer of approximately 20.5 μm formed, leading to increased bioactivity, as evidenced by a Ca/P ratio of 1.67, comparable to that of hydroxyapatite in human bone. The hemolysis ratio of 0.027% at pH 7 indicates minimal Red Blood Cell (RBC) lysis and increased biocompatibility. The corrosion rate was enhanced with pH (5,7 and 9) approximately (1.975 × 10−2, 1.078 × 10−2, and 1.615 × 10−2) mm/year, respectively. These findings indicate that the novel process at neutral pH (7) is optimal for surface modification, as it is the most effective at enhancing the biocompatibility and bioactivity of the Ti64 alloy, making it suitable for biomedical implants. Secondly, an advanced finishing process using a hybrid chemical process to oxidize and soften the Ti alloy surface, followed by a magnetorheological fluid (CH-MR) to achieve nanoscale roughness with reduced processing time and enhanced surface quality. To systematically evaluate and optimize the influence of CH-MR process parameters, a Central Composite Design (CCD) under Response Surface Methodology (RSM) was employed. CCD combines factorial or fractional factorial points, axial points, and center points to develop a quadratic model for predicting and optimizing process responses. In this study, CCD was used to investigate the effects of critical parameters of pH value, working gap (WG), rotational speed (RS), and current (C) on the surface roughness (Sa) of Ti64 alloy. Analysis of Variance (ANOVA) revealed pH as the most influential parameter (19.14% contribution), followed by WG (15.73%), RS (12.85%), and C (9.93%), while the remaining variation was attributed to parameter XX interactions. Optimization yielded a minimum Sa of 38.20 nm after 30 minutes of finishing under the optimal parameters of pH of 5, a WG of 0.5 mm, an RS of 150 rpm, and a C of 3.5 A. Surface morphology and integrity were assessed using Field Emission Scanning Electron Microscopy (FESEM) and Atomic Force Microscopy (AFM), confirming a significant reduction in surface roughness and improvements in quality, with fewer surface defects. X-ray Photoelectron Spectroscopy (XPS) further elucidated the finishing mechanism, linking surface oxidation states to enhanced chemical- mechanical synergy. These characterizations validated the CH-MR process's ability to improve surface roughness and confirmed the absence of contamination or subsurface damage, both of which are crucial for biomedical and aerospace applications. 2025-09-01T00:00:00Z