Investment casting is one of metallurgy’s oldest processes, with roots stretching back to early weapons, jewelry, and religious art. It remains prized today for aerospace turbine blades and biomedical components because it delivers exceptional surface finish, dimensional accuracy, and the ability to reproduce complex shapes. In Nepal, the technique has been used since at least the sixth century A.D. to produce the bronze and gilded deities that fill temples and monasteries across the Himalayas. The process is intensely manual: a sculptor first carves a detailed wax master pattern, which is encased in a rubber mold from which multiple wax replicas can be made. These replicas are assembled with gating systems, dipped in a traditional slurry of cow dung and clay, reinforced with metal wires, dewaxed, preheated, and finally filled with molten metal. After cooling, each piece demands hours of filing, chiseling, and hand-applied gold plating.

That artisanal character comes at a cost. Defect rates in Nepali sculpture casting hover around thirty percent, and sculptures can take anywhere from one month to two years to complete depending on size. Because global economic shifts and advancing technology are squeezing demand for Nepali cast sculptures, improving casting design, modeling, and production efficiency has become essential for the industry’s survival. The research team, led by Zenisha Shrestha with Abhishek Pandey and Bijendra Prajapati, set out to determine whether systematic parameter optimization, never before applied to this traditional setting, could meaningfully improve dimensional accuracy in gilding metal, an alloy of ninety percent copper and ten percent zinc that is the most widely used sculpture material in Nepal.

The researchers chose a sword as their test specimen, selected for its cultural relevance in Nepali society and its relatively simple geometry, which makes dimensional analysis tractable. The design was modeled in SOLIDWORKS, with the gating system developed through the modulus method to reflect designs typical of sculpture manufacturing. From an Ishikawa cause-and-effect analysis of casting stability, the team identified three controllable parameters: the number of slurry coating layers, the mold preheat temperature, and the metal pouring temperature. Factors such as wax composition, alloy composition, slurry composition, cooling methods, and environmental conditions were treated as noise parameters that could not be easily controlled in the workshop.

The experimental design followed a Taguchi L9 orthogonal array, allowing nine carefully chosen experiments to explore the parameter space efficiently. Slurry coatings ranged from two to four layers, preheat temperatures spanned 500 to 600 degrees Celsius, and pouring temperatures covered the 1150 to 1200 degree Celsius range typical of Nepali sculpture foundries. Each coating choice involves a trade-off: thin shells save material and time but risk bulging and leaking under the metallostatic pressure of pouring, while thicker coatings prevent defects and improve heat retention at the cost of longer processing. Preheating the mold reduces thermal shock and premature freezing of the melt, improving fill of thin sections, though excessive preheat can accelerate mold-metal reactions and increase surface-connected porosity. Higher pouring temperatures superheat the metal above its melting point, preventing unfilled sections in intricate features.

Four response variables were measured for each casting: weight, length, breadth, and thickness. Before optimization, the team verified data quality through normality testing with probability plots and the Anderson-Darling test, confirming that all responses followed normal distributions. Pareto tests and analysis of variance at a 95 percent confidence level then established that all three process parameters significantly influenced every response variable. The number of coatings emerged as the dominant factor, contributing 44.42 percent of the variance in weight, 48.01 percent in length, 58.81 percent in breadth, and 41.82 percent in thickness. Pouring temperature exerted its greatest influence on weight at 39.31 percent and length at 31.38 percent, while preheat temperature most strongly affected breadth and thickness. Residual plots showed randomly distributed errors, confirming the reliability of the statistical model.

Because dimensional accuracy depends on optimizing all four responses simultaneously, the researchers turned to multi-response optimization. Grey relational analysis, a technique designed for systems with limited information, converts multiple responses into a single grey relational grade by normalizing the data and calculating correlation coefficients, using an identification coefficient of 0.5 consistent with prior studies. The innovation here was the coupling of grey relational analysis with principal component analysis, which uses eigenvectors to derive objective weights for each response rather than assuming they matter equally. The first principal component captured 81.9 percent of the data’s variance and identified length as the most significant response. Experiment 3, combining a 500 degree Celsius preheat, a 1200 degree Celsius pour, and four coating layers, ranked highest under both the standard and PCA-weighted grades, and both methods converged on the same optimal setting, strengthening confidence in the result.

The confirmatory experiment delivered striking improvements. The weighted grey relational grade rose from 0.357 under initial conditions to 0.966 under optimal conditions, closely matching the predicted value of 0.9677. Weight deficit fell from 3.160 percent to 0.682 percent, length shrinkage dropped from 4.860 percent to 0.545 percent, breadth shrinkage plummeted from 6.156 percent to 1.067 percent, and thickness shrinkage declined from 4.200 percent to 1.800 percent. In practical terms, a cast sword produced under the optimized parameters now deviates from its wax pattern by barely one percent in its principal dimensions, meaning far less manual filing and correction before gold plating. Given that finishing work is among the most labor-intensive stages of sculpture production, even modest dimensional improvements translate into significant reductions in lead time and cost.

The implications extend well beyond Nepal’s foundries. The authors note that the GRA-PCA methodology can be adapted to any complex manufacturing process where multiple conflicting objectives must be balanced, including precision casting of aerospace turbine blades, biomedical device manufacturing where dimensional control is critical, and automotive component casting where strength, weight, and tolerances compete. For Nepal, the study represents the first application of advanced multi-response optimization to traditional sculpture casting, and it arrives at a pivotal moment for an industry whose economic viability depends on competing with industrialized producers. Future work, the researchers suggest, could examine surface roughness, mechanical properties, and additional process parameters. But the central message is already clear: fourteen centuries of artisanal wisdom and twenty-first-century statistical rigor are not adversaries. When the number of coatings, the preheat temperature, and the pouring temperature are tuned together, the ancient art of Himalayan metal sculpture can achieve a precision its original masters could scarcely have imagined, preserving both a cultural heritage and the livelihoods of the craftsmen who sustain it.

Subject of Research: Multi-response optimization of investment casting parameters to improve the dimensional accuracy of gilding metal sculptures in traditional Nepali manufacturing.

Article Title: Improvement of dimensional accuracy in gilding metal sculpture manufacturing using grey relational analysis coupled with principal component analysis

Article References: Shrestha, Z., Pandey, A., & Prajapati, B. (2026). Improvement of dimensional accuracy in gilding metal sculpture manufacturing using grey relational analysis coupled with principal component analysis. Heliyon, 12(14), Article e45412. https://doi.org/10.1016/j.heliyon.2026.e45412

Image Credits: AI Generated

DOI: Not provided

Keywords: investment casting, grey relational analysis, principal component analysis, Taguchi method, dimensional accuracy, gilding metal, Nepal sculpture manufacturing, shrinkage reduction, ANOVA, multi-response optimization, Kathmandu Valley, metal casting

The choice of matrix alloy was deliberate. AA6061 is a wrought aluminium alloy prized for its excellent thermal and physical properties, its corrosion resistance, and the ease with which it can be fabricated into machine parts, structural elements and aerospace components. Yet like most commercial alloys, it has limits in strength and wear performance that can restrict its use in demanding tribological applications such as brakes, bearings and gears. Materials scientists have long explored ways to tailor aluminium by adding hard ceramic or mineral particles, including silicon carbide, alumina, fly ash, red mud and zircon silicate, and previous studies have generally found that such reinforcements boost hardness and strength while often reducing density. What makes the new work distinctive is the use of marble dust, an abundant industrial waste stream from Rajasthan’s stone industry, which turns a disposal problem into a feedstock for advanced materials.

To create the composites, the team used a semi-automatic stir-casting process, one of the most economical and widely adopted liquid-state fabrication routes for metal matrix composites. Cleaned and cut AA6061 alloy rods were melted in a graphite crucible at 720 degrees Celsius, and the molten metal was fluxed to remove impurities. Around 2 weight percent of magnesium, added in the form of MgO, was stirred into the melt to improve wettability, a critical step because poorly wetted particles tend to cluster rather than disperse. Meanwhile, the marble dust was preheated to about 400 degrees Celsius for half an hour to drive off moisture and lower the surface energy of the particles, which further promotes uniform mixing. The heated reinforcement was then added incrementally while a graphite stirrer agitated the melt at 250 revolutions per minute for five minutes. The melt was superheated slightly to maintain fluidity and poured into a permanent mould, producing cast plates from which standard test specimens were machined.

Five compositions were prepared, spanning 0 to 6 weight percent marble dust in 1.5 percent increments, labelled M0 through M6. The researchers evaluated density, void content, tensile strength, flexural strength, hardness and impact strength for each formulation. A clear trend emerged: as the marble dust content increased, the void fraction dropped from roughly 12.5 percent to 6.66 percent, while every measured mechanical property improved. Tensile strength rose from about 238 megapascals in the unreinforced alloy to roughly 277 megapascals at 6 percent reinforcement. Hardness climbed from 75 to 90 on the Rockwell B scale, flexural strength increased from 180 to about 205 megapascals, and impact strength grew from 16.5 to 21 kilojoules per square metre. The team attributes these gains to two mechanisms: the rising dislocation density and the build-up of a stronger matrix-particle interface, which reduces voids and improves load transfer, and the ability of hard particles to act as barriers to dislocation motion, producing classic dispersion strengthening.

The tribological behaviour of the composites was assessed under dry sliding conditions, with the specific wear rate ranging from about 2.006 to 2.528 times ten to the minus six cubic millimetres per newton-metre. Three operating variables were systematically varied: normal load from 10 to 50 newtons, sliding distance from 800 to 4000 metres, and sliding velocity from 1 to 2 metres per second. Across the composition range, wear rate and friction coefficient increased with each of these parameters but fell as marble content rose. The M6 composite, containing the maximum 6 weight percent reinforcement, consistently delivered the lowest wear and friction. The researchers explain this through the same microstructural logic that governs the mechanical results: fewer voids mean a more continuous, better-bonded material that transfers load efficiently between matrix and particles, so the surface resists softening, ploughing and debris generation during sliding.

Scanning electron microscopy of the worn surfaces revealed the underlying wear mechanisms in vivid detail. At the lowest load of 10 newtons, the M6 composite displayed clean primary surfaces with minimal debris, evidence of its intactness and strength. At 20 newtons, mild ploughing and pitting appeared, and by 30 newtons deep ploughing grooves and a secondary layer of laminated wear debris became visible. At 40 and 50 newtons the damage turned aggressive, with massive debris layers and extensive pitting. The team attributes this escalation to rising interfacial temperatures at higher loads, which soften the surface, promote debonding and generate hard debris particles that then scour the interface in a destructive three-body abrasion mechanism. These micrographs directly link the macroscopic wear measurements to observable surface physics.

To optimise the sliding wear process, the researchers turned to Taguchi’s design of experiments, arranging trials in an L25 orthogonal array and using signal-to-noise ratios with a smaller-the-better objective to minimise wear. Analysis of variance on the results showed that normal load was the most influential parameter, contributing 33.33 percent of the variability in specific wear rate, followed by sliding distance at 13.36 percent, reinforcement content at 12.98 percent and sliding velocity at 9.64 percent. The highest F-value, 2.49 for normal load, confirmed its dominant role. The optimal parameter combination produced a signal-to-noise ratio of 58.60 decibels, and a confirmation experiment run at randomly selected settings validated the model with an error of only 3.6 percent, demonstrating that Taguchi’s approach can reliably guide wear-minimising process design for these materials.

Perhaps the most novel element of the study is its use of hybrid multi-criteria decision-making techniques to rank the five composites across all their performance metrics simultaneously. The two-phase AHP-TOPSIS algorithm first converts expert judgment about the relative importance of criteria into quantitative weights using Saaty’s nine-point scale, checking consistency to ensure the weights are trustworthy, and then measures each alternative’s closeness to an ideal solution. The hybrid AHP-R method follows the same first phase but uses reciprocal rank-based weighting in the second. In both analyses, the criteria hierarchy placed hardness, tensile strength, flexural strength and impact strength at the top, with density and wear rate weighted lower, and the consistency ratio of roughly 0.0044 fell far below the accepted 10 percent threshold. Critically, the two independent ranking methods converged on exactly the same order: M6 outperformed M4.5, which beat M3, with M1.5 and M0 trailing, confirming the subjective assessment that the highest marble content is optimal.

The convergence of experimental measurement, statistical optimisation and decision theory gives the findings unusual robustness, and the practical implications are considerable. The authors suggest that composites with low void content and strong mechanical properties are well suited to tribological applications, and that these marble-dust-reinforced aluminium alloys could serve as substitute materials in rollers, brakes, guideways, bearings and gears. Because the reinforcement is essentially industrial waste, the cost and environmental footprint of producing the composites should be far lower than those of conventional ceramic-reinforced alternatives. The work also adds to a growing body of literature showing that hybrid decision-making algorithms such as AHP-TOPSIS and AHP-R, borrowed from operations research, can rapidly and objectively rank material alternatives, sparing engineers from grappling with conflicting criteria by intuition alone.

The study was conducted with support from the Advanced Research Lab for Tribology and the Material Research Centre at Malaviya National Institute of Technology Jaipur, and the authors report no competing interests. While the research remains at the laboratory scale, the combination of a cheap, abundant reinforcement, a scalable casting process and statistically validated performance gains suggests a credible pathway from quarry waste to engineered components. For industries under pressure to cut both costs and carbon, the message of this work is striking: the material of the future for lightweight, wear-resistant metal parts may already be piling up in the dust of India’s marble workshops, waiting to be stirred into the melt.

Subject of Research: Marble dust particle-reinforced AA6061 aluminium matrix composites evaluated for mechanical strength and sliding wear performance using hybrid decision-making techniques

Article Title: Mechanical and sliding wear performance analysis of AA6061 − marble particulates reinforced alloy composites via hybrid decision-making techniques

Article References: Kumar, A., & Kumar, M. (2026). Mechanical and sliding wear performance analysis of AA6061 − marble particulates reinforced alloy composites via hybrid decision-making techniques. Journal of Materials Science: Metallurgy, 1(1), Article 15. https://doi.org/10.1007/s44492-026-00015-z

Image Credits: AI Generated

DOI: 10.1007/s44492-026-00015-z

Keywords: AA6061 alloy, marble dust particulates, metal matrix composites, stir casting, sliding wear, mechanical properties, Taguchi method, ANOVA, hybrid AHP-TOPSIS, AHP-R method, tribology, automotive materials