Engineering

Abstract: Battery electric vehicles (BEVs) relocate emissions from the tailpipe to the power station and the battery supply chain, so their climate benefit depends on the electricity that charges them. This study presents a transparent, ISO 14040/14044-conformant cradle-to-grave life cycle assessment comparing a representative mid-size BEV with a comparable gasoline internal combustion engine vehicle (ICEV), using a process-sum inventory in which every input is traceable to a published source. The use phase is evaluated under five electricity scenarios: the 2024 Saudi Arabian grid (692 g CO2/kWh), the 2024 world and European Union averages, a prospective 2030 Saudi grid meeting the Kingdom's 50% renewable-electricity target, and the French grid. Over 225,000 km, the ICEV emits 50.6 t CO2e (225 g CO2e/km); the BEV emits 37.8 t (168 g CO2e/km) on the current Saudi grid — a 25% reduction — falling to 23.4 t under the 2030 scenario and 17.4 t on the EU grid. BEV production emissions are 1.9 times the ICEV's, dominated by the traction battery; emission break-even occurs at 76,000 km on the Saudi grid. Monte Carlo analysis shows the BEV superior in 99.6% of 10,000 runs. Electrification therefore delivers a robust climate benefit even on a fossil-dominated grid, and the benefit roughly doubles with the announced power-sector transition.

Abstract: To address the difficulty of single-modal images in simultaneously representing structural defects and thermal abnormal defects of equipment in complex transmission-line inspection scenarios, this paper proposes a transmission equipment defect detection method based on dual-stream thermal-aware infrared–visible image fusion. The proposed method takes spatially registered visible images and infrared thermal images as inputs. First, a dual-stream feature extraction structure is constructed to separately extract structural texture features from visible images and thermal radiation features from infrared images. Subsequently, a shared–specific feature transfer mechanism is designed to decompose and interactively model visible-specific structural information, infrared-specific thermal information, and cross-modal shared semantic information, thereby enhancing the complementary information representation capability between the two modalities. On this basis, a thermal-aware gated enhancement module is introduced to adaptively strengthen abnormal thermal response regions in infrared images, enabling the fused image to preserve equipment edge contours and texture details while highlighting potential thermal fault features. To further constrain the fusion results, a joint optimization function composed of thermal radiation priority loss, edge preservation loss, and contrast enhancement loss is constructed to improve the collaborative preservation capability of structural information and thermal abnormal information in the fused image. Experiments are conducted on an aligned infrared–visible image dataset collected from transmission-line inspection scenarios. The dataset includes normal insulator strings, normal strain clamps, damaged insulator strings, thermal faults in insulator strings, and thermal faults in strain clamps. The experimental results show that, compared with typical fusion methods such as EgeFusion, DANT-GAN, and ITFuse, the proposed method achieves superior performance in terms of average structural similarity, average mutual information, thermal radiation preservation rate, and edge preservation rate. Furthermore, images generated by different fusion methods are fed into a YOLO detection model for downstream defect detection validation. The proposed method achieves Precision, Recall, and mAP@0.5 values of 0.936, 0.921, and 0.943, respectively, outperforming the comparison methods. The results demonstrate that the proposed method can effectively fuse visible structural texture information and infrared thermal radiation information, enhance the discriminative representation of defect regions in transmission equipment, and provide an effective approach for intelligent perception and defect detection of transmission equipment in complex inspection scenarios.

Abstract: The transition from rigid-body to compliant mechanisms offers well-recognized advantages in mechanical design, including reduced part count, improved energy efficiency, and expanded functional integration. However, existing catalogue-based and artificial intelligence–assisted design approaches often rely on abstract functional archetypes, limiting practical applicability and hindering identification of integrated, application-specific compliant solutions. This study presents a methodological framework for extracting compliant mechanism solutions from patent literature based on a structured, multi-agent pipeline employing Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) architecture. The proposed approach integrates product decomposition, lexical and technological expansion, patent retrieval, compatibility assessment, and taxonomy-grounded classification using compliant mechanism archetypes. The framework is demonstrated through a case study involving 40 patents related to pedal-actuated waste bin mechanisms, enabling qualitative evaluation against expert judgment. The results indicate consistent identification of compliant alternatives to conventional rigid mechanisms, ranging from localized compliant substitutions (e.g., living hinges and leaf springs) to more integrated compliant transmission architectures. The patent-derived solutions provide application-specific structural embodiments, including geometric details and implementation information suitable for design reuse. Rather than proposing a finalized design tool, the study clarifies the architectural and methodological requirements for leveraging patent data in compliant mechanism design. By grounding design exploration in validated prior art and taxonomy-based reasoning, the proposed framework supports systematic discovery of contextually relevant compliant mechanisms while reducing abstraction and cognitive load in early-stage design.

Abstract: Welding of dissimilar metals of aluminum and copper foils is important for electrical batteries and electrical vehicles, and usually results in intermetallic-compound joints with fragile mechanical properties. Continuous-wave lasers are typically used in industry, and the heating effect could result in thermal stresses that break the intermetallic compounds. Here this paper shows pulsed laser shock oscillation welding with balanced mechanical shock wave effect and thermal effect, could generate joints with better mechanical properties and with less energy consumption. The joints achieved by pulsed laser shock welding and pulsed laser shock oscillation welding are compared to reveal the advantages of the proposed method, revealing its great potential for energy storage sectors applications.

Abstract: Despite growing adoption of home gardening and demonstrated economic and environmental advantages of distributed manufacturing, no previous study has systematically evaluated their combination. To address this gap, this study evaluates technical and economic feasibility of manufacturing essential gardening products using open-source distributed 3D printing. Twenty-five common open-source printable gardening designs were selected from Thingiverse, and organized into categories: hand tools, planting and seeding, water management, planters and vertical gardening, and storage and post-harvest products. For each product, the material and energy costs of manufacturing using a RepRap-class 3D printer were compared against the retail cost of commercially available equivalents in the Canadian market. The results found that open-source distributed manufacturing with 3D printing can provide substantial eco-nomic benefits for home gardening applications. All gardening products were less ex-pensive to print than to purchase as commercial equivalents. Average savings were 78.2%, which is sufficient to recover the cost of an entry-level 3D printer for single gardening kit. The repository-level analysis showed 3D printing gardeners had already saved >$2.5 million. Beyond direct cost reduction, 3D printing offers additional ad-vantages for gardeners, including local production, repairability, and customization for specific garden layouts or user needs all of which reduce waste and bolster sustainability.

Abstract: The Split Delivery Vehicle Routing Problem (SDVRP) necessitates the coordination of route sequencing and order-splitting decisions, whose heterogeneous structure may constrain the efficacy of metaheuristics that update all decision variables under uniform search dynamics. This study introduces a Block-Scaled Grey Wolf Optimizer (BS-GWO) for a parameterized variant of the SDVRP, wherein order allocation and cycle time are determined in an initial stage, while the primary optimization process concentrates on operational vehicle-level splitting and route sequencing. Each candidate solution is represented by a continuous vector divided into two blocks: one associated with the visiting sequence and the other with the allocation of product units among vehicles. In contrast to the classical Grey Wolf Optimizer (GWO), the proposed BS-GWO incorporates differential block-wise scaling and maximum displacement control per block while maintaining the original α, β, and δ leadership structure. The results show that BS-GWO achieved more consistent solution quality than the classical GWO and the best overall performance among the compared metaheuristics. These findings suggest that adapting the search dynamics to the internal structure of the solution vector enhances the ability to solve heterogeneous SDVRP representations within the considered formulation.

Abstract: Ultrasonic welding is a process for joining plastic components with localized heating, very short welding times and a high process efficiency. It is significantly influenced by the process parameters and the tolerances of the parts to be joined. At the same time, welding theories and material models are reaching their limits with ultrasonic welding. This makes it hard to predict weld quality for an individual part. The weld seam quality is typically assessed by subsequent destructive testing of the weld seam. In the present study, we are showing that acoustic monitoring of the ultrasonic welding process allows predictions to be made about the weld seam quality during the process. For that approach, the acoustic monitoring of the mechanical vibration effects occurring in the process is examined using piezo elements, a laser microphone and a laser vibrometer. Besides sound energy density and the non-linear vibration behavior occurring in the process, the weld seam quality is characterized using conventional tensile tests. In the results, both the level of the acoustic energy density and, in particular, the course of the non-linearity during the process show a correlation with the resulting weld seam quality, thus allowing for a non-destructive quality monitoring in real-time.

Abstract: Photovoltaic (PV) backsheets are subjected to continuous environmental stressors, resulting in gradual tensile strength degradation and reduced long-term module reliability. This paper presents a six-stage statistical modeling framework to quantify how cumulative ultraviolet (UV) exposure, temperature, relative humidity, and wind speed each contribute to tensile strength loss in a multilayer PPE-based PV backsheet (polyethylene terephthalate/polyethylene terephthalate/ethylene vinyl acetate, PET/PET/EVA). Pearson correlation using a field dataset of 511 sequential observations of cumulative UV doses (0.84 MJ/m² to 271.6 MJ/m²) established UV irradiance as the most important degradation driver (r = -0.960, R² = 0.921). Since the observations constitute one degradation time series, the serial autocorrelation was checked with the Durbin-Watson statistic and confirmed, necessitating Newey-West heteroscedasticity and autocorrelation-consistent (HAC) standard errors in all the inferential tests. Following a HAC correction, cumulative UV is the strongest predictor. The rate of tensile strength degradation with respect to cumulative UV exposure was estimated at 0.21–0.22 MPa per MJ/m², based on ordinary least squares (OLS), multiple regression, and weighted least squares (WLS) models applied following the Breusch–Pagan test for heteroscedasticity. The second-order regression model gave a better predictive performance with R² = 0.9686 and RMSE = 3.033 MPa; six of the nine higher-order terms were found significant after HAC correction, whereas the UV Temperature interaction became insignificant, which demonstrates the practical effect of the autocorrelation correction. Weibull distribution fitting was the best performer with shape parameter β = 15.87 and B10 tensile strength threshold of 193.14 MPa at approximately 15.4 years of field exposure. Cost benefit analysis showed that premium fluoropolymer backsheets had a net present value benefit of about $7.55 per module in comparison to 25-year design life. These results provide a model of PV backsheet degradation, which can be easily reproduced, and which can be directly applied to reliability-based design and cost-effective lifecycle models.

Abstract: Seamless steel tubes are widely used in various fields such as energy and chemical industry, machinery manufacturing, construction engineering, transportation, military and aerospace. Rolling is one of the main processes for producing seamless steel pipes, featuring high efficiency, low cost, and good quality. The Fine Quality Mill (FQM) is the core equipment of an advanced seamless steel pipe and tube production line. Rolling force is a critical process parameter in the FQM seamless steel pipe production line. During the rolling process, seamless steel pipes undergo complex deformation affected by multiple influencing factors. The rolling force fluctuates significantly in the first pass, which directly determines the dimensional accuracy of product profiles and the uniformity. Clarifying the weight contributions of key process factors to rolling force is essential for improving the modeling accuracy of rolling processes. Combining rolling deformation theory with field-measured production data, Slab Method (SM) Analysis, Grey Relational Analysis (GRA) and Finite Element Method (FEM) were adopted to quantify the influence weights of parameters on rolling force, including dynamic yield stress of materials, roll speed, mandrel speed, and friction coefficient. Consistent results obtained from the three integrated methods demonstrate that the dynamic yield stress of materials acts as the dominant factor affecting rolling force, and rolling force exhibits far higher sensitivity to dynamic yield stress than to other process parameters. Furthermore, this paper analyzes the intrinsic factors governing the dynamic yield stress of materials during the continuous rolling of seamless steel pipes. The research findings provided solid theoretical support and data references for selecting key input data and enhancing the prediction accuracy of data-driven rolling force models in engineering applications.

Abstract: In the aviation sector, spare parts management plays a critical role in mitigating aircraft downtime caused by stockouts while supporting the efficient and sustainable use of resources in maintenance operations. It involves a range of inventory control methodologies combined with spare parts demand forecasting, which is particularly challenging due to the stochastic nature of component failures. Beyond ensuring operational readiness, effective spare parts management can contribute to sustainability by reducing excess inventory, minimizing waste from obsolete components and optimizing storage and transportation requirements within Maintenance, Repair and Overhaul (MRO) systems. The integration of resource-efficient inventory strategies and accurate demand forecasting has the potential to enhance both supply chain resilience and environmental performance. This study provides a systematic review of recent approaches to spare parts management and evaluates their contribution to sustainability in the aviation industry. A structured literature review was conducted, including the identification, screening and analysis of studies published between 2010 and 2025. The review of 16 selected studies highlights the growing relevance of aligning demand forecasting with spare parts management, enabling more efficient inventory decisions that improve aircraft availability while supporting resource efficiency and sustainability objectives in MRO operations. This study contributes by consolidating current knowledge on sustainable spare parts management practices and identifying key areas for future research in aviation supply chains.

Abstract: Industry 5.0 offers new opportunities to rethink operational excellence by combining advanced technologies with sustainability, resilience, and human-centered value creation. This study examines how Industry 5.0 enabling technologies contribute to seven dimensions of operational excellence: efficiency and productivity, quality and reliability, organizational agility, continuous innovation, customer satisfaction, environmental sustainability, and organizational resilience. Based on survey data collected from 120 industrial companies that have begun integrating Industry 5.0 technologies, multiple regression analyses were conducted to assess the differentiated influence of these technologies on operational excellence outcomes. The results show that Industry 5.0 technologies do not contribute uniformly across all dimensions. Big Data emerges as a transversal lever, while Artificial Intelligence, Edge Computing, Digital Twins, Energy Efficiency Technologies, IoT, and Additive Manufacturing show more specific effects depending on the performance dimension considered. Environmental sustainability presents the strongest explanatory power, mainly supported by Energy Efficiency Technologies, Big Data, and Digital Twins. The findings suggest that Industry 5.0 adoption should follow a selective and contextual strategy aligned with firms’ operational, sustainability, and resilience objectives.

Abstract: Quality failures in the flexible packaging industry increase production costs, generate material waste, and negatively affect manufacturing sustainability. This study developed an integrated Data Analytics model for quality failure management by combining descriptive, diagnostic, predictive, and prescriptive analytics. A dataset containing 1242 real production records was analyzed using operational variables including material grammage, production speed, ambient humidity, paper type, and reel type. Several ML classification algorithms were evaluated, including Logistic Regression, K-NN, SVM, Naive Bayes, Decision Tree, and Random Forest. Model performance was assessed using Accuracy, Precision, Recall, F1_Score, and AUC-ROC metrics. The results showed that nonlinear models achieved superior performance in defect detection compared with linear approaches. Decision Tree obtained the best balance between predictive performance and interpretability, achieving a Recall of 0.927 and an F1_Score of 0.962, while Random Forest achieved the highest AUC-ROC value (0.995). To assess model robustness and reduce the risk of overfitting, a 5-fold cross-validation procedure was applied, confirming the stability and generalization capability of the selected model. A prescriptive optimization model was subsequently integrated with the Decision Tree classifier to identify process configurations associated with lower defect probabilities and lower expected production costs. The proposed framework supports data-driven quality management, reduces the likelihood of defective production, and contributes to sustainable manufacturing through improved resource utilization, lower waste generation, and more efficient operational decision-making.

Abstract: In this study, a decision-support model was proposed for evaluating orders in production environments. The developed model consisted of two stages. In the first stage, orders were classified by priority level. In the second stage, a mixed-integer programming model was developed using the classification output as input. In the first stage, orders were classified into priority classes based on criteria determined according to company priorities. For this purpose, Machine Learning (ML) models were developed using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. The classification structure covered both make-to-order (MTO) and make-to-stock (MTS) order types, and separate benefit and penalty coefficients were defined for each class. In the second stage, the resulting classification was translated into production decisions through a mathematical model that maximized total weighted benefit under capacity and budget constraints. The model generated an accept or reject decision for each MTO order. For MTS orders, quantities that could not be fulfilled due to constraints were postponed to subsequent periods. In addition, recommendations for reconsideration were provided for orders found to be infeasible following the second stage.

Abstract: The present study investigates the combined influence of nano-ground quartz sand, microsilica, and a polycarboxylate-based modifier on phase evolution, microstructure development, and mechanical properties in low-clinker cement systems. The main objective is to evaluate whether finely dispersed quartz acts as an inert filler or contributes to hydration processes, and to determine the critical clinker threshold governing system performance. A hybrid modifier composed of microsilica and polycarboxylate (4:1) was introduced into a clinker–gypsum system together with varying amounts of nano-ground quartz sand. The mixtures were subjected to mechanical activation in a ball mill. Chemical composition, phase evolution, and microstructure were analyzed using XRF, XRD, and TEM, while mechanical properties were evaluated through standard tests. The results show that grinding duration and modifier dosage significantly influence particle dispersion and water demand. Optimal performance was achieved at 90 minutes of grinding and 6% modifier content. A nonlinear relationship between quartz content and compressive strength was observed, with a maximum value of 57.1 MPa obtained at 30% quartz and 55% clinker content. XRD analysis confirmed partial portlandite consumption and the formation of additional low-crystalline C–S–H gel, indicating a synergistic interaction between nano-quartz and microsilica. The results demonstrate that nano-ground quartz acts not only as a filler but also as a nucleation-active component with limited pozzolanic contribution. A further reduction in clinker content below 55% leads to insufficient formation of hydration products and a decline in mechanical performance, confirming the existence of a critical clinker threshold. These results provide a scientific basis for the design of low-clinker, low-carbon cementitious materials with enhanced performance.

Abstract: Traditional attribute control charts for defect counts are commonly developed under the assumption that count data follow a homogeneous Poisson distribution. However, this assumption is often violated in practical applications. To overcome this limitation, a two-parameter Poisson distribution, the Conway-Maxwell-Poisson (CMP or COM-Poisson) distribution, has been widely used to construct control charts capable of effectively monitoring count data exhibiting over- or under-dispersion. Furthermore, the multiple dependent state (MDS) sampling scheme evaluates the current process status not only based on the present sample but also by incorporating information from previous samples, thereby achieving higher detection efficiency than single sampling schemes. This study integrates the COM-Poisson distribution with the MDS sampling strategy to develop an attribute control chart based on the extended exponentially weighted moving average statistic. The average run length is obtained under various shift magnitudes using probability-based computations. The simulation results demonstrate that the proposed chart substantially outperforms existing approaches in the prompt detection of out-of-control conditions. A real-world air quality index (AQI) monitoring study showed that the proposed chart effectively detected increases in weekly AQI counts and provided earlier warnings of potential air quality deterioration.

Abstract: This work demonstrates that the RAPDtT model—a descriptive architecture with structural and ontological scope, composed of Reception (R), Storage/Buffer (A), Processing (P), Dispatch (D), and transport vectors (t, T)—constitutes a minimal and sufficient operational homomorphism, under the defined assumptions, to formalize the internal structure of the classical black box. Based on the protocolized record of states (inputs/outputs), the operational equivalence Φ(Input) ~== -t-> R - t-> A- t-> P- t-> D - t-> (input), is proposed, understood as a structural explicitness of the system’s transfer function rather than a strict functional identity in the mathematical sense. This correspondence enables formalization of the internal architecture under conditions of defined boundaries, traceability, and operational closure, facilitating homeostatic control mechanisms through negative feedback. Methodologically, the minimality of the architecture is examined using the principle of parsimony (Ockham’s Razor), demonstrating that the removal of any component invalidates the representation under the established structural requirements. The integration of Mario Bunge’s systemic ontology and W. Ross Ashby’s formal definition of dynamic systems allows the conclusion that RAPDtT functions as a provisional standard architecture for the analysis, design, and auditing of physical–informational processes in transformative systems with bounded flow.

Abstract: Traditional cultural heritage digitization frequently isolates historical motifs in static archives, limiting their functional reuse in contemporary manufacturing and product design. To address this limitation, this study proposes a three-layer framework that transforms physical cultural motifs into manufacturing-ready, semantically enriched digital assets. The digitization layer balances human-guided point-cloud modeling with script-based rendering to bypass conventional digital reconstruction bottlenecks, directly generating versatile two-dimensional vector paths and three-dimensional solid geometries. To prevent data fragmentation, a custom web ontology language-based schema preserves non-linear dependencies by explicitly connecting the generated point clouds and digital abstractions with their corresponding structural taxonomies and cultural semantics. Validated through a case study of thirteen Ainu motifs, the framework diffuses these relational assets via an open-access web repository and an autonomous generative design assistant powered by the model context protocol. The findings demonstrate that motifs from diverse cultural origins can effectively be digitized and meaningfully interconnected using the extensible schema, illustrating a highly practical approach for consuming cultural digital abstractions in downstream applications. Ultimately, this framework transitions motif preservation from retrospective archiving to active industrial utility, establishing a reproducible paradigm for integrating indigenous design history into both contemporary engineering workflows and interdisciplinary STEM education.

Abstract: This perspective originates from an MSc thesis in Food Quality Management completed at Kwame Nkrumah University of Science and Technology (KNUST), Ghana, in 2017 (Oppong Kyekyeku 2017), which tested the performance of a closed-vessel storage system for dry cocoa beans against ISO 9001 requirements, establishing regression-based performance benchmarks across 201 retention samples over a 121-day storage period with 408 days of environmental monitoring. The present argument asks what those validated outputs imply beyond their original compliance purpose, and advances three connected claims. First, the parametric outputs of that performance qualification — regression benchmarks with R² ≥ 96.0% and a 408-day Xbar-S environmental baseline — constitute the Conversion-level model component that any digital twin requires as its performance baseline, resolving the primary modelling barrier to Cyber-level deployment. Second, the 5C cyber-physical systems architecture of Lee et al. (2015) is the generative architecture from which any digital twin in a regulated quality system must logically be derived: it is a formal description of what every Quality Management System requires in its intelligence and actuation dimensions, expressed in the vocabulary of control engineering rather than quality management. Third, this structural identity between QMS validation logic and the 5C CPS architecture holds uniformly across food, pharmaceutical, biologics, medical device, and cosmetics regulatory contexts, making the methods of performance qualification a transferable analytical competency across regulated industries. The perspective additionally reports new analysis of the motivating case infestation dataset (Mann–Whitney U = 5,575.5, p < 0.000001), which demonstrates that pre-storage biological state is a statistically significant predictor of post-storage defect outcome, with hidden biological state accounting for a minimum of 48.2% of unexplained variance in the All Other Defect variable. This finding is advanced as a concrete illustration of a class of observability problem — consequential latent variables undetectable by standard measurement protocols — that appears in structurally equivalent forms across all regulated sectors. Counterarguments to the generative claim are identified and addressed: the 5C framework was developed for industrial manufacturing health management rather than regulated quality assurance, and not all validation outputs meet the statistical criteria for Conversion-level sufficiency. These limitations delimit rather than invalidate the central argument.

Abstract: The increasing complexity of global manufacturing environments demands seamless vertical integration of information systems across all enterprise levels. Manufacturing Execution Systems (MES) occupy a critical intermediate tier between shop-floor automation and Enterprise Resource Planning (ERP); however, their systematic integration with Intelligent Manufacturing Systems (IMS) remains insufficiently formalised. This paper proposes a comprehensive four-stage MES–IMS integration methodology grounded in the ISA-95 enterprise-control framework and the MESA collaborative MES model. The methodology encompasses: (i) identification of MES and IMS baseline characteristics; (ii) definition of collaborative activities; (iii) design of a hierarchical communication model; and (iv) specification of bidirectional data-exchange requirements including Key Performance Indicators (KPIs). The proposed framework was experimentally verified on the FESTO FMS 500 within the Žilina Intelligent Manufacturing System (ZIMS) concept. An original hierarchical MES–IMS model was derived, articulating vertical and horizontal communication flows across three enterprise tiers. A structured KPI taxonomy—covering equipment effectiveness, process throughput, quality, and workforce metrics—was formulated and validated against FMS 500 station data (23 indicators). MES can act as the primary intelligence-supporting layer within IMS, providing the real-time data substrate required for adaptive autonomous manufacturing behaviour. The proposed framework offers a replicable integration pathway aligned with Industry 4.0 paradigms.

Abstract: This paper analyzes the use of Omani limestone, collected from the mountain ranges of the Sultanate of Oman, a major global exporter of limestone, as a natural reinforcement to improve the mechanical properties of recycled aluminum alloys obtained from automotive engine cylinder scraps. The recycled aluminum was melted and mixed with limestone powder at volume fractions of 2.5%, 5%, and 7.5% using the stir-casting technique. Mechanical test results showed progressive, statistically significant improvements (one-way ANOVA, p < 0.05) with increasing reinforcement content. At the optimal 5.0 vol.% fraction, tensile strength increased by 16.9%, surface hardness improved by 19.7%, and impact resistance increased by 28.6% relative to the unreinforced alloy. Scanning Electron Microscopy (SEM) confirmed uniform particle distribution and microstructural densification at 5.0 vol.%, but severe porosity, particle agglomeration, and microcrack initiation at 7.5 vol.%. X-ray diffraction (XRD) analysis confirmed the stable presence of CaCO₃ compounds and positive interaction with the aluminum matrix. The 5.0 vol.% fraction is identified as optimal, delivering the best balance of mechanical enhancement and microstructural soundness. These results suggest promising prospects for Oman's limestone waste in advanced engineering applications with significant environmental and economic benefits.