Geometric dimensioning, tolerancing and inspection

The high-performance car industry demands increasingly lightweight and durable components, driving the development of innovative manufacturing techniques and increasingly high performing materials. Among critical automotive components, mechanical hinges with close supports play a key role in ensuring high functionality in limited spaces while contributing to the structural efficiency and safety of vehicles. This study examines an accurate design and manufacturing process for hinges with close-range bushings. In particular, the manufacturing technology of the Polytetrafluoroethylene (PTFE)-coated bronze bushing connected with a FeZnNi pin inside the hinges was analysed and optimised. A predictive mathematical model was developed and validated with measurements using an appropriate experimental set-up created by the authors. Specifically, the experimental tests were performed by simulating and automating the rotation of the hinge; this made it possible to accurately assess the resistant torques due to assembly errors, tightening torques, geometric and dimensional tolerances. Through the proposed methodology, the resistant torque was related to the correct manufacture, the PTFE coefficient of friction, and the operating temperatures, guaranteeing long-lasting performance and reliable integration of hinge components. The results demonstrated that the proposed methodology enhances the manufacturing process by allowing the production of reliable and high-performance mechanical hinges.

Keywords: Fe-Zn-Ni alloy , Geometric and dimensional tolerances , Long-lasting performance , PTFE-coated bronze bushing , Resistant torque

Maintenance activities on transportation infrastructures, particularly on the underside of bridges and viaducts, present critical challenges related to safety, accessibility and cost-efficiency. Conventional solutions, such as scaffolding or mobile platforms, often exhibit substantial operational limitations. In this context, modular articulated robotic arms present a promising, safer, and more flexible alternative. This study introduces a functionality and data-driven design methodology for the modular development of articulated robotic arms specifically tailored for bridge maintenance applications. The proposed approach integrates Finite Element Analysis with clustering algorithms to identify mechanically equivalent joint modules based on their structural response under realistic loading conditions, including self-weight and external forces. Subsequently, topology optimisation techniques are applied to refine the geometry of selected joints, enhancing their stiffness-to-weight ratio while preserving manufacturability and modularity. Additionally, the application of Geometric Dimensioning and Tolerancing ensures mechanical reliability and precision during assembly. The effectiveness of the methodology is demonstrated through comparative Finite Element Analysis assessments between the initial and optimised designs, along with the construction of a functional prototype. The proposed framework paves the way for scalable, robust, and repeatable robotic solutions in the infrastructure maintenance sector.

Keywords: Bridge Maintenance , Clustering Algorithms , Geometric Dimensioning and Tolerancing , Modular Robotic Arms , Topology Optimisation

Origami-based compliant mechanisms have unlocked new possibilities in the design of adaptable structures, resulting in lightweight, reconfigurable, and material-efficient systems. However, translating the complex 3D geometries needed for practical robotic applications into fabrication-ready 2D crease patterns remains a significant challenge. The need to accurately preserve kinematic behavior while ensuring flat-manufacturability introduces additional complexity in the design process. The fabrication of origami through multimaterial extrusion additive manufacturing (MME-AM) offers a compelling solution by integrating rigid and flexible materials in a single print. Still, in turn, it poses difficulties due to the complexities related to pattern generation, its intricate assembly, unfolding, and folding sequences. To address these challenges, this work introduces OrigaMatic, a parametric computational algorithm that converts zero-thickness kinetic origami models into fabrication-ready, 3D-printable flat layouts. These developed structures can be further re-folded into their intended 3D configurations, ensuring both kinematic accuracy and structural integrity. By automating this process, the workflow accelerates the transition from concept to fabrication. It eliminates the need for manual intervention in unfolding complex mechanisms, reducing time in the design-to-fabrication process while enhancing reproducibility. The effectiveness of OrigaMatic is demonstrated by experimental validation through the fabrication of compliant prototypes, with a focus on parallel mechanisms relevant to the fields of small-scale machinery and robotics. The investigation highlights the ability of the algorithm to streamline the origami-inspired mechanism design while preserving structural integrity and kinematic functionality.

Keywords: 3D-printing , compliant mechanisms and robots , design automation , folding and origami , linkages , parallel robots

Analysing the particle size distribution (PSD) of nanomaterials is essential for many scientific and industrial applications, as PSD offers important details about the distribution, average size, and range of particles within a sample. All these properties have a huge impact on the characteristics and behaviour of the materials. In this work we investigated image-based PSD recognition of nanoparticles from synthetic SEM images. For the task we considered an image classification pipeline based on five conventional (hand-crafted) texture descriptors and five pre-trained convolutional neural networks (CNN). Fusion strategies for combining the best performing feature sets were also investigated. We found that both the conventional and the CNN-based features achieved good performance (best accuracy respectively 87.6% and 88.2%), whereas the optimal performance (90.2%) was obtained by combining the two best performing CNN-based feature sets. Altogether the results are promising and demonstrate the feasibility of the method.

Keywords: Deep Learning , Image processing , Nanomaterials , Scanning electron microscopy , Texture

Industry X.0 robotic manufacturing demands higher accuracy and flexibility, enabling continuously adaptive processes designed and optimized through simulations and Digital Twins. To achieve this level of flexibility and productivity in high value-added processes, where limited robot position accuracy becomes a critical constraint, advanced engineering methods and digital tools are required. These solutions must predictively compensate for inevitable robot positional accuracy errors, eliminating the need for manual pose refinement and enabling the generation of “first-time-right” robot code. This work aims to address these challenges by introducing an engineering tool capable of predictively correcting robot positioning inaccuracies across the workspace, enabling accurate point-to-point motion generation. It is intended for tasks with limited process interaction forces, where positioning errors are dominated by geometric, compliance, and joint-related effects. The tool leverages a multi-parameter Machine Learning (ML) error predictor trained on a reduced experimental dataset, minimizing data acquisition time and production downtime. Realized as a Python-based framework, it can be seamlessly integrated into commercial offline programming environments to automatically generate validated robot programs. The paper details the framework structure, focusing on the definition of the ML-based position error predictor, and its implementation on a robotic cell equipped with a high-payload KUKA robot and a FARO laser tracker. A preliminary experimental analysis identified payload, approach direction, and point location as the key operational parameters, accessible at the code level, that influence positioning accuracy. These insights guided feature selection and the design of reduced training datasets. In particular, a uniform spatial grid of only 64 points, corresponding to about one hour of measurement time, was sufficient to achieve near-optimal model accuracy. Several ML algorithms were compared, with the Tabular Prior-data Fitted Network achieving superior generalization on small datasets. Experimental validation on the KUKA robot showed up to a 98.4 % reduction in positioning error and consistent performance across all tested points, confirming the tool robustness and suitability for deployment across different industrial environments. All datasets, source code, and implementation scripts are openly released to enable reproducibility and facilitate industrial deployment.

Keywords: Engineering tool , Industrial robot , Machine learning , Offline compensation , Position accuracy , Robot simulation

Additive Manufacturing (AM) displays unique capabilities in terms of fabricating parts including unconventional geometries. Much research is conducted to investigate the performances of AM from both the mechanical and the quality viewpoint. This research is aimed to complete the transition of AM from a rapid prototyping technology to a mature manufacturing option. Interestingly, this research is routinely conducted on standard geometries instead of those kind of topologies for which AM is an advantageous fabrication system. The study presented here originally compares the accuracy of AM for standard and complex shapes. As a case study, we used the model of a leg of a quadruped robot obtained with generative design in previous research. The model includes standard and non-standard geometries, with the latter being predominant. Ten copies of this model have been produced by AM, specifically by means of a 3D printer working with Fused Deposition Modelling. Two different orientations have been used for the model (five copies each). The 3D-printed physical models have been 3D-scanned and compared with the reference CAD model, which has allowed us to evidence deviations. The deviations have been displayed by using both global and local best-fit options in a quality assessment software application. The first analysis of achieved data suggests that printing parameters and the position of surfaces affect the accuracy of AM more than the characterization of surfaces as standard and non-standard.

Keywords: Additive Manufacturing , Deviations , freeform , Generative Design

Featured Application: The methodology presented in this work enables the direct extrusion-based 3D printing of soft, low-hardness RTV-2 silicone elastomers using industrial additive manufacturing systems. By adapting commercially available general-purpose silicones through viscosity tuning, the proposed approach expands the material palette for silicone additive manufacturing beyond proprietary formulations. This capability is particularly relevant for applications requiring highly compliant, biocompatible, and geometrically complex parts, such as soft robotics components, customized prosthetic and orthotic devices, medical simulation models, wearable interfaces, and flexible seals or gaskets. A particularly relevant industrial scenario for the proposed methodology is the small- to medium-batch production of customized silicone components, where traditional mold-based casting becomes economically inefficient due to tooling costs and long lead times. This study investigates the relationship between viscosity and manufacturability of two-component silicones in extrusion-based additive manufacturing. A methodology is proposed to adapt commercially available, low-viscosity general-purpose silicones for direct 3D printing using the material extrusion system provided by Lynxter S300X. EcoFlex™ 00-50 silicone was modified through controlled additions of a thixotropic agent (THI-VEX), producing formulations with progressively increased viscosity. After a preliminary qualitative viscosity assessment, formulations were printed using identical process parameters and evaluated through a set of dedicated geometric benchmark specimens targeting critical failure modes, including unsupported thin walls, overhangs, gaps, and slender structures. Print outcomes were assessed via multi-rater visual inspection with inter-rater reliability analysis to ensure consistency. Results reveal a strong correlation between thixotropy and geometric fidelity, identifying the formulation containing 4.0 wt% THI-VEX as optimal under the tested conditions. The study provides practical design and process guidelines for silicone additive manufacturing and highlights the importance of integrated material–process optimization for reliable fabrication of soft, highly deformable materials.

Keywords: geometric printability , material extrusion , RTV-2 silicone elastomers

Piezoelectric material extrusion is a novel material extrusion technique that deposits thermoplastic materials in droplets, by means of a piezoelectric nozzle regulating the material flow. This improves the performance of the machine, with respect to Fused Filament Fabrication, by enabling higher precision in material placement and better control over deposition volume. A common limitation of this technology lies in the fabrication of overhanging structures, where precise droplet deposition is inherently more complex and prone to inaccuracy. The research investigates the impact of a key process parameter, specifically Droplet Aspect Ratio, on the dimensional accuracy of printed specimens and on the quality of overhang surfaces. The research considers bridging and the prioritization of infills in the deposition strategy as additional variables. An experiment based on the fabrication of 50 specimens, optimized for overhang quality evaluation, was performed. Specimens were analysed through 3D scanning techniques. The study demonstrates a strong correlation between Droplet Aspect Ratio and the dimensional accuracy of overhanging surfaces. The integration of bridging structures significantly improved surface quality by providing additional support, with the greatest benefits observed at 27° inclination. Prioritizing infill deposition before outer contours resulted in a marked reduction in defect levels at high DAR values.

Keywords: additive manufacturing , droplet aspect ratio , overhang , pellet-based material extrusion , piezoelectric nozzle

Soft robotics represents a paradigm shift in robotics, focuses on creating adaptable, flexible robots inspired by biological systems to engage safely and efficiently with intricate environments and living organisms. This study focus on the investigation of the performance characteristics of 3D-printed Soft Pneumatic Actuators (SPAs) fabricated from thermoplastic polyurethane using Fused Filament Fabrication. Through systematic experimental analysis, we evaluate how geometric parameters and operating conditions affect actuator behavior, specifically examining the relationships between the number of bellows (N), wall thickness (T), and operating pressure (P) on the resulting bending angle (α) and maximum force output (F). Tests were conducted on 45 actuator specimens, with bellows counts of 9, 11, and 13, wall thicknesses of 1.6, 2, and 2.4 mm, and operating pressures ranging from 1 to 3 bar. Results demonstrate approximately linear relationships between design parameters and actuator performance metrics. Increasing the operating pressure showed strong positive linear correlations with bending angle, while wall thickness demonstrated a strong negative linear correlation. For force output, operating pressure maintained a strong positive linear correlation, wall thickness showed a moderate negative linear correlation, while the number of bellows had only a weak positive correlation. Both bending angle and force output exhibited approximately linear relationships with these parameters, with deviations from linearity remaining fairly low across all configurations. The predictable, near-linear responses provide a robust foundation for performance-driven design optimization of SPAs for applications requiring precise control of movement and force generation.

Keywords: 3D printing , pneumatic actuator , precise control , soft actuator , soft material

The growing use of wide-band-gap semiconductors in power electronics increases the demand for efficient yet low-cost thermal management. Pin-fin heat sinks offer a practical solution due to their robustness and ease of fabrication, but their geometric optimization via Computational Fluid Dynamics (CFD) models remains computationally demanding, particularly in multi-variable contexts. This work presents a novel surrogate modeling approach based on Non-Uniform Rational Basis Splines (NURBS) entities for predicting thermal and hydraulic performance in pin-fin cooling systems for traction inverters. Two CFD modeling strategies were explored: one allowing variable pin count, and another preserving topological consistency by adjusting the domain width. While the former introduced geometric discontinuities that degraded metamodel accuracy, the latter enabled the NURBS-based surrogate model to achieve high predictive performance with a few seconds evaluation times. Compared to Kriging surrogate models and Artificial Neural Networks (ANNs), the NURBS approach delivers superior computational efficiency, making it particularly well-suited for real-time applications, sensitivity analyses, and gradient-based optimization.

Keywords: Cooling systems , Metamodeling , NURBS , Surrogate Model , Power module

Lost-wax casting is an ancient technique of great precision, used for the creation of bronze artefacts. The artist makes the model in clay or plasticine and then creates the plaster or silicone mold. Inside the mould, wax is brushed to the ideal thickness of 5 to 6 mm and then wax “straws” were added at appropriate positions to allow optimal filling of the mould during bronze casting, starting with the lowest volumes. Finally, the wax model is incorporated into a refractory mass that surrounds it internally and externally and forms the cavity into which the bronze is poured. In this research, the results were obtained by analysing an innovative digital technique that avoids the creation of the mould and shrinkage during the intermediate stages of bronze production. In fact, using a 3D scanner and the latest rapid prototyping techniques, an accurate reproduction of the original clay model was recreated. The moulded model replaces the wax model. It is also enclosed between two forms of refractory material and, due to its similar melting temperature and flow properties, minimises build time and improves accuracy. The studied methodology also allows a more precise assignment and greater control of all surface thicknesses through a discretised digital finite element (FE) model analysis.

Keywords: Bronze sculptures' reconstruction , Laser scanning technology , Lost-wax bronze casting , Mold withdrawal , Rapid prototyping

Accurate three-dimensional (3D) reconstruction of individual dental elements from cone-beam computed tomography (CBCT) data is critical for adaptive prosthetic design and occlusal simulation in digital dentistry. However, challenges persist due to the complex morphology of teeth, the similar radiodensities of hard tissues, and the limited resolution of CBCT in capturing interproximal surfaces between adjacent teeth. This study presents a semi-automated, open-source compatible workflow that integrates CBCT image segmentation with the reconstruction of missing interproximal molar regions using Non-Uniform Rational B-Spline (NURBS) surface modeling. Molar datasets from CBCT acquisitions were segmented into 3D Slicer open-source software and refined to isolate individual teeth. Missing surface regions were reconstructed using third-degree NURBS patches in Rhinoceros® ver. 8 commercial software. Method reconstruction accuracy was quantitatively evaluated on 14 M by applying the method to manually created voids on intact tooth models. The average root-mean-square error (RMSE) and Hausdorff distance were 0.041 ± 0.015 mm and 0.049 ± 0.023 mm, respectively. The resulting models exhibited high surface continuity and anatomical fidelity, making them suitable for Computer Aided Engineering (CAE) analysis and prosthetic customization. This approach enables reproducible, high-precision dental modeling and represents a promising step toward fully automated 3D reconstruction workflows in clinical practice.

Keywords: CBCT segmentation , Digital prosthodontics , Molar morphology subdivision , NURBS modeling

Car seat comfort represents a fundamental dimension of automotive ergonomics, yet its assessment and optimization remain predominantly reliant on subjective feedback and iterative physical prototyping, limiting scalability and responsiveness in the design process. This study presents a novel data-driven framework that synergistically integrates statistical analysis and deep learning architectures enhanced with attention mechanisms to predict perceived driver discomfort from pressure distribution patterns. A comprehensive dataset acquired from 192 participants was used to extract biomechanical descriptors, such as contact area and peak pressure, across 9 anatomical regions, which were subsequently correlated with subjective discomfort ratings collected on a 10-point Likert scale. The proposed hybrid deep neural network achieved a modelling accuracy of R2 = 0.82 for overall discomfort, with peak pressure emerging as the most influential factor (Spearman's r = 0.78). Moreover, Finite Element Analysis (FEA) was employed to validate geometry refinements suggested by the AI model, resulting in an 18% reduction in peak seat pressure without compromising structural integrity. The findings underscore the transformative potential of integrating artificial intelligence with CAD–CAE methodologies to enable rapid, user-centered, and simulation-driven ergonomic design early in the vehicle development cycle, minimizing the need for physical prototypes and enhancing occupant comfort.

Keywords: AI-Driven Comfort Models , Deep Learning with Attention Mechanisms , Ergonomic Seat Design Optimization , Finite Element Validation of Human-Centric Design , Pressure Mapping and Biomechanical Analysis

This study investigates the development of advanced technologies for subsurface monitoring, with significant implications in geophysics, natural hazard mitigation, and environmental engineering. Specifically, the research focuses on the application of Distributed Acoustic Sensing using a 28 km long scientific electro-optical cable deployed in deep-sea conditions as a sensor for the detection of vibrations and mechanical disturbances along its path. DAS enables continuous, high-resolution monitoring of the underwater acoustic environment. However, the system's sensitivity and accuracy are strongly influenced by the degree of mechanical coupling between the optical fiber and the surrounding medium, which in turn depends on both the structural characteristics of the cable and the nature of the seabed where it is installed. To address these challenges, a digital twin of the experimental configuration has been developed. This virtual model allows for the simulation and optimization of the cable's mechanical behavior under various environmental conditions. Particular emphasis is placed on the analysis of the cable's static equilibrium state, which is essential for understanding the actual transmission of mechanical stress to the optical fiber and for the calibration of numerical models against experimental data. The manuscript is organized as follows: Introduction, Cable Architecture, Experimental Setup, Methodology, Results, and Conclusions.

Keywords: Digital twin , Distributed Acoustic Sensing (DAS) , Mechanical Coupling , Optical Fibers

This investigation examined the perceptual-motor distortions induced by mixed reality head-mounted displays during spatial navigation tasks. Employing a two-phase experimental paradigm, we initially quantified kinematic differences between helmet and non-helmet conditions, subsequently assessing sensorimotor adaptation through repeated bidirectional trajectory navigation. Fifty participants executed standardized trajectories while three-dimensional movement kinematics were recorded via spatial tracking. Statistical analyses revealed significant alterations in movement parameters during helmet use, characterized by increased trajectory tortuosity and elevated oscillation frequency. Qualitative assessment of movement patterns demonstrated systematic lateral deviations perpendicular to the intended vector, suggesting distortion of depth perception and spatial mapping processes. Longitudinal analysis of adaptation metrics indicated a 42.72% reduction in oscillation frequency between early and late trials, though this effect did not reach conventional statistical significance. These findings contribute to the theoretical understanding of technologically induced perturbations to spatial cognition and motor control, while establishing trajectory tortuosity as a sensitive metric for quantifying mixed reality performance decrements. The documented adaptation phenomena have significant implications for training protocols in professional applications of mixed reality technology.

Keywords: Head-Mounted Display , Mixed Reality , Spatial Navigation , Tortuosity

Liquid Crystal Elastomers combine the elasticity of polymer networks with the anisotropic ordering of liquid crystals, thus enabling reversible shape modifications and stimulus responsive actuation. Unfortunately, manual LCE fabrication remains limited by operator-dependent variability, which can lead to inconsistent film thickness and manufacturing times inadequate for a mass production. This work presents a low-cost, automated manufacturing framework that redesigns the mechanical assembly steps of the traditional one-step LCE fabrication process. The design includes rubbing, slide alignment, spacer placement, and infiltration cell assembly to ensure consistent film quality and scalability. A customized Cartesian robot, built by adapting a modified X–Y core 3D printer, integrates specially designed manipulator systems, redesigned magnetic slide holders, automated rubbing tools, and supporting fixtures to assemble infiltration devices in an automated way. Validation tests demonstrate reproducible infiltration, improved mesogen alignment confirmed via polarized optical microscopy, and high geometric repeatability, although glass-slide thickness variability remains a significant contributor to deviations in final film thickness. By enabling parallelizable low-cost production, the designed hardware demonstrates its effectiveness in devising the scalable manufacturing of LCE films suited for advanced therapeutic and engineering applications.

Keywords: automated manufacturing , cartesian robotics , liquid crystal elastomers (LCEs) , mesogen alignment

Capturing accurate texture maps from physical materials remains a challenge in digital prototyping and projection-based spatial augmented reality (P-SAR). This paper presents an open-source material scanning system based on photometric stereo, designed for affordability, simplicity, and efficient operation. The system combines a consumer-grade digital camera, multifaceted reflector (MR16) LED lighting, and Arduino-controlled automation to acquire material data up to A4 size within 15 s. Accurate colour reproduction is achieved through a hybrid calibration workflow that integrates camera profiling with a 3D lookup table. The resulting images are processed in a streamlined Substance 3D Designer pipeline to generate albedo and normal maps compatible with physically based rendering (PBR). To evaluate performance under realistic conditions, two fabric samples were scanned and qualitatively compared with professionally digitised references. Albedo maps were assessed based on dominant colour accuracy using CIEDE2000 (ΔE00), while normal maps were evaluated through visual rendering comparisons and directional distribution analysis. Scanning and processing times were also measured to verify workflow efficiency. Results demonstrate that the proposed system produces perceptually consistent textures suitable for real-time rendering applications while offering a low-cost and customisable solution for material digitisation.

Keywords: Custom scanner , Material digitisation , PBR textures , Photometric stereo

This study presents a genetic algorithm-based methodology for reconstructing the nominal profile of airfoils belonging to the NACA four- and five-digit series. By minimizing the geometric deviations between measured point clouds and parametrically generated airfoil profiles, the algorithm identifies the best-fitting nominal geometry. The approach was implemented using Rhino 8, Grasshopper, and the Galapagos plugin, and validated through extensive testing on 3D-printed samples. Across 200 test runs, the algorithm consistently identified the correct nominal geometry, demonstrating robustness despite inherent stochastic variability and computational challenges. The average number of iterations needed to converge was found to be 953 across all cases. This methodology offers a valuable tool for reverse engineering and metrological applications, providing a parametric and efficient alternative to traditional free-form surface reconstruction.

Keywords: Airfoil Design , Airfoil inspection , Genetic Algorithm , Reverse Engineering

Additive Manufacturing (AM) is no longer limited to prototyping, as it is gaining an increasingly large market share. With this expansion, there is a growing demand for the development of standardized rules to establish product specifications and verification procedures. AM offers unique opportunities in free-form shape design and multi-material processing, in contrast to traditional manufacturing processes and communication methods. Because of AM's unique features, existing standards struggle to describe its product specifications. New regulations are therefore under development This paper reviews the state of the art in Additive Manufacturing (AM) product specification. This research examines the most up-to-date literature and standards published by the ISO, and ASME committees. A research gap has been identified in the accurate determination of design intent, which is crucial for defining product specifications. “Profile tolerance” is recognized as the most versatile specification for free-form geometries; however, new tools can be developed to control lattice structures. Additionally, the authors identified a significant gap in the assessment of dimensional and geometrical deviations in AM processes, as these are often estimated using geometrical benchmarks designed for subtractive technologies. Finally, verification remains one of the most critical aspects of AM products. Computed Tomography (CT) currently represents the only viable approach for measuring inaccessible features; however, standardized reconstruction methods are still lacking, as is a specification method tailored specifically for this measurement technique.

Keywords: Additive Manufacturing , DfAM , Dimensional and Geometrical Specification , Product Specification

This study investigates the dimensional change on sintering of products printed via the binder jetting (BJ) additive manufacturing (AM) process, using a powder mixture of stainless steel 316 L and 1 wt% red mud. This research is a part of a vast project aiming to develop continuous-feeding anodes for direct carbon fuel cells (DCFC). Cubes with a through-hole were printed to assess dimensional change during sintering. The geometries of the samples were carefully reconstructed before and after sintering using points acquired by a coordinate measuring machine (CMM). The results reveal a slightly lower shrinkage of the powder mix compared to the reference 316 L material. Novel scaling factors have been proposed to compensate for dimensional changes in future anode production. Additionally, the shape of the cubes was highly distorted after sintering, especially along the powder spreading direction. Finite element simulations reveal the impact of the density gradient in the green state on the origin of sintering deformation. This density gradient, estimated at approximately 0.5% of the relative density per millimeter, is likely caused by the poor flowability of the powder mix.

Keywords: Binder Jetting , Composite Material , Density Gradient , Dimensional Accuracy , Red Mud , Sintering Simulation