Bilal Akbar Chuddher

This study addresses the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP) by investigating the simultaneous effects of workforce skill proficiency (depth) and skill versatility (breadth) on project makespan reduction. Two state-of-the-art techniques, the ACO algorithm integrated with the parallel scheduling approach (PSS) and LINGO, were implemented to solve a known dataset. The results indicate that LINGO’s performance improves compared to the ACO algorithm with increasing resource levels and provides higher solution quality at higher resource availability. On the other hand, with limited resource availability, the ACO algorithm performs better at lower SP levels (0.1 and 0.3). However, as the SP level increases, LINGO outperforms the ACO algorithm, especially for SP values of 0.5, 0.7, and 0.9. Furthermore, it is also observed that there is a positive correlation between resource level and optimality percentage. The results indicate that ACO may be more competitive for scenarios with limited SP and few resources, while LINGO is more effective in instances with greater resource amounts and larger SP values.

Miniaturized systems demand micro-components exhibiting high dimensional fidelity, superior surface quality, and complex geometries for aerospace, biomedical, and microelectronic applications. Micro-milling offers flexibility and precision, but consistent performance remains challenging by rapid tool degradation, burr generation, and size-effect-dominated cutting mechanism, particularly in high strength alloys. This study investigates machinability of Inconel 718 in low-speed micro-milling using uncoated and three coated micro-end mills, with feed rates defined relative to cutting-edge radius. Taguchi L16 orthogonal array methodically assesses the impact of cutting speed, feed rate, depth of cut, and tool coating across four distinct levels each, on surface roughness, tool wear, and burr formation. The experimental outcomes were evaluated statistically using Analysis of Variance (ANOVA), while Grey Relational Analysis (GRA) was applied to identify optimal machining conditions. Results reveal that TiAlN coatings improve surface finish, nACo suppresses burr formation, and uncoated tools enhance wear resistance. The optimal condition from GRA is 10.5 m/min cutting speed, 1.5 μm/tooth feed, and 120 μm depth of cut with uncoated tool. Response Surface Methodology (RSM) yielded average reductions of 23.81%, 11.49%, and 18.11% in surface roughness, tool wear, and burr formation, respectively. This integrated Taguchi-ANOVA-GRA-RSM Optimization (TANGRO) framework provides robust insights for optimal machining performance.

Dissimilar welding of titanium alloys to stainless steels offers the potential to combine lightweight, corrosion-resistant titanium with the toughness and cost-effectiveness of steel, yet the formation of brittle Ti–Fe intermetallic compounds (IMCs) at the interface has long prevented its reliable applications. This study examines the role of tantalum interlayer thickness in controlling intermetallic phases evolution and enhancing the mechanical response in pulsed gas tungsten arc welded (P-GTAW) Ti–6Al–4V/SS304 joints. Composite interlayer consisting of Tantalum (Ta) foils of 0.1, 0.3, and 0.5 mm thickness combined with Cu filler was introduced at the interface, and the resulting welds were evaluated through ultimate tensile strength (UTS), microhardness, and detailed microstructural characterization using scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) and X-ray diffraction (XRD) supported by fractography analysis. The results demonstrate that increasing interlayer thickness progressively suppressed the formation of Ti–Fe IMCs, refined the fusion zone grain structure, and promoted the development of Cu and Ta based phases. The joints with thickest Ta foil (0.5 mm) depicted high maximum strength (297 MPa), improving by 75% compared to the thinnest interlayer and microhardness profiles becoming smoother across the fusion boundary. These findings confirm that interlayering is an effective method to mitigate brittle phase formation in dissimilar Ti–SS welds. The improvements demonstrated underscore tantalum’s potential for aerospace and structural applications are required, where both high strength and reliability are required.

Biomimetic design for engineering applications may suggest the optimal performance of engineering devices. In this work the passive/pure pitching characteristics of a hydrofoil are investigated experimentally with and without a pair of biomimetic fin strips placed symmetrically on the two sides of the foil leading edge. The work is performed in a recirculating water channel at low Reynolds numbers (Re) with a range of 1300 ≤ Re ≤ 3200. Using high-speed videography and Particle Image Velocimetry (PIV), the pitching characteristics and wakes are visualized. Passive pitching characteristics, i.e., the pitching amplitude and pitching frequency of the hydrofoils, are investigated based on their trailing edge movement. Significant improvement in both pitching frequency and amplitudes are observed for the foil with fin strips compared to the baseline simple foil. Comparing the pitching characteristics of the two foils, it is observed that the hydrofoil with biomimetic fin strips exhibits 25% and 21% higher pitching amplitude and pitching frequency, respectively, compared to that of the baseline at comparable Reynolds numbers. The initiation of pitching for the finned foil is also observed at comparatively low Reynolds numbers. The wake is also studied using time mean and fluctuating velocity profiles obtained using PIV.

The decline in battery voltage during drone (UAV) flights poses a critical challenge, impacting flight safety, operational range, and efficiency. As battery voltage drops, motor performance diminishes, increasing the risk of crashes or failure to return to the launch point. This issue requires innovative solutions due to its implications for safety, performance, cost reduction, and sustainability. This research proposed the development of an intelligent system using live data and the machine learning algorithms to predict the state of drone’s battery. The model uses historical flight data, incorporating key variables such as battery voltage, wind speed, flight time, and environmental conditions to predict battery discharge during the operation. The proposed research is consisted of multiple phases, starting with problem definition and exploratory data analysis (EDA), followed by correlation analysis to understand the relationships between variables. The final phase will involve developing an interactive intelligent system that integrates the predictive model for real-time monitoring, proactive maintenance, and improved power management strategies. The current study will reduce operational costs, extend battery lifespan, and enhance safety across various drone applications.

Efficient selection of trucks represents a significant challenge within logistics operations, directly influencing both cost efficiency and delivery effectiveness. This study introduces a comprehensive four-stage data modeling framework that employs machine learning techniques, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), to forecast load capacities and appropriately assign trucks for deliveries. The analysis utilized historical delivery data, which was meticulously processed to ensure accurate predictions regarding truck selection based on load capacity. Findings indicate that the Random Forest model excels in precisely estimating pallet requirements, while the Decision Tree method demonstrates superior performance in optimizing truck assignments. This research provides logistics managers with a viable strategy to enhance operational efficiency, reduce expenses, and improve resource allocation.