Geometric dimensioning, tolerancing and inspection

Additive manufacturing components are prone to defects, including warping and cracking, which can significantly impact their mechanical performance. Determining deformations in lattice structures remains challenging. This paper presents a methodological framework for computing deformation matrices in additively manufactured lattice samples using 3D scanning. The sample's geometry is acquired through 3D scanning, registered to a reference model, and vertices are determined and used for computing deformation metrics, including volumetric scaling factors, isovolumetric scaling, and angular deformations. A sensitivity analysis comparing four association criteria demonstrates the reliability of the Gaussian Best Fit Plane External. Assessing deformation in lattice structures can be useful for optimizing printing parameters and compensating for process-induced deformations.

Keywords: 3D scanners , Additive Manufacturing , Deformations , Lattice Structures

This study aims to define the assembly shift for “nx” fit patterns, considering scrap and the influence of increasing elements. The methodology includes rejection rate estimation via Monte Carlo simulation, gap distribution estimation, and assembly shift computation based on gap distribution. It also proposes a design methodology for dimensioning tolerances to meet assembly shift requirements. Results show narrower assembly shift distributions with more pattern elements, revealing an exponential relationship.

Keywords: Assembly shift , Boundary Condition , clearance fit , Rejection Rate , Tolerance Analysis , Virtual Condition

When pattern of fits are not designed following the boundary condition design criterion, the rejection rate due to failure in assemblability need to be considered. Since in a pattern of fits it is not possible to define an assembly equation it is not trivial to create a tolerance stack-up. The paper proposes a strategy to achieve a generalized rejection rate computation for nx patterns based on an interpolation model derived from Monte Carlo simulations. The rejection rate as function of the number of element in the pattern is simulated for different size and location tolerances. An exponential convergent function is fitted to the data and a generalize regression model is used to estimate the function parameters. Validation of the proposed methodology is provided.

Keywords: Boundary Condition , Rejection Rate , Tolerance Analysis , Tolerancing , Virtual Condition

This paper presents an optimization analysis of the geometrical accuracy of metal binder jetting products. An orthogonal array L9 was designed using Taguchi method to study four process parameters (printhead speed, dark body, powder applicator speed and shell thickness) at three levels. Printed parts were measured by a coordinate measuring machine, and parallelism error was evaluated, both in planes aligned with binder injection and with powder spreading direction. Samples geometry was characterized by a squared through hole to evaluate differences in external and internal features. In the nine experiments, the parallelism varied in a range between 0.01 and 0.04 mm. No significant difference was observed in relation to plane direction and feature type, while parallelism tended to decrease with the increase in the sample's distance from the printing platform. A grey relational analysis was employed to optimize process parameters, in order to improve the geometrical accuracy. Through the analysis of means of grey relational grade, it was found that powder applicator speed has the highest effect on response variables, while printhead speed has the lowest contribution.

Keywords: Binder Jetting , Geometrical accuracy , Taguchi method

Nowadays, modern vehicles comprise a vast number of components made from various materials and manufactured using different techniques, making the management of assemblability, functionality, and cost highly complex. This complexity necessitates controlling geometrical and dimensional deviations to meet product and process demands. Dimensional Management (DM), supported by standards such as ASME-GD&T and ISO-GPS, is increasingly employed for geometrical specification and tolerance analysis. Through Computer-Aided Tolerancing (CAT) tools, tolerances can be considered early in the design process using Design for Tolerancing (DfT) approaches. This study aims to develop and define a methodology for geometric specification allocation and tolerance stack-up analysis of automotive components. The methodology integrates predictive models, DM, and DfT approaches via computer-aided tolerance specification and analysis, enabling comprehensive modeling and simulation of tolerance effects on performance, quality, and assemblability.

Keywords: Geometric Dimensioning and Tolerancing , Geometrical Product Specification , Model Based Definition , Tolerance design

In the nuclear fusion framework the robots employed for inspection and maintenance tasks, are extremely afflicted by high amplitude oscillation due to the inertial loads that these robots must manage. To optimize space and weight of a robotic end-effector, remove components and not essential mechanism is the first step. However it's crucial to ensure a satisfactory level of precision and performance in terms of grasping of the gripper. Therefore in remote handling all the design aspects have to face with the restricted volume allowed by the environment in which the robotic system has to be employed. The fusion machines, akin tokamaks, are characterized always by narrow access port or confined spaces; these aspects affect the design of the cross–section of the manipulators and of the end-effector. The objective of this work is to evaluate the implementation of an innovative compliant mechanism suitable for remote handling tasks. More specifically in this work an innovative gripper design that significantly reduces the need for complex internal mechanisms and transmissions is presented. This novel approach utilizes a kinematic system based on the deformation of the main body of the gripper itself. By leveraging the intrinsic flexibility and material properties of the structure of the end-effector; this design achieves the necessary motion and force transmission through its own deformation, simplifying the overall design, enhancing reliability and reducing the mass of the manipulator at the tip. The FACT (Freedom And Constraint Topology) is a topological method, employed to obtain a first conceptualization of the kinematic.

Keywords: Compliant-mechanism , Fusion , Gripper , Inspection , Maintenance

Geometric Dimensioning and Tolerancing (GD&T) are among the basic concepts of functional fitness and quality assurance in modern manufacturing. The historical development of GD&T took place primarily in the ambit of subtractive manufacturing; the advent of Additive Manufacturing (AM) now presents novel challenges due to the complexity of geometries, material variability, and process-induced variances. The present Perspective Paper briefly hints at key challenges for the future of GD&T in AM, with an eye to the necessary adaptation of tolerancing principles to AM-specific geometries, integration of Model-Based Definition (MBD) in digital threads, and development of new standards for surface texture and tolerance stack-up. New inspection techniques are also highlighted for the AM parts, which would become more prominent.

Keywords: additive manufacturing , geometric dimensioning and tolerancing , ISO , model-based definition , surface texture features

This paper describes a design method based on muscle synergies to minimize the number of sensors on surface electromyography (sEMG) shorts for monitoring motor and fun activities with playground equipment. We started from data collected during an experimental campaign with four playground equipment in Kinder Joy of Moving parks. In details, five sEMG located on the leg and one IMU located on the back were employed. From this data, muscle synergies extraction using non-negative matrix factorization algorithm was carried out. Then, the minimum number of sEMG sensors that allows the reconstruction of all sEMG patterns with a VAF for each muscle and each playground over 0.90 was defined. The results underlined how with two sEMG sensors placed on the vastus lateralis and semitendinosus, is possible to reconstruct five sEMG patterns for all four-playground equipment guaranteeing an accuracy in terms of VAF cumulative over 0.97. This work might pave the way to simplify the design of sEMG shorts for motor and fun activities reducing invasiveness, costs and environmental impact.

Keywords: design methods , muscle synergies , playground activity , surface electromyography , wearable sensors

Traditional industrial robots offer significant operational flexibility and adapt well to reconfigurable production systems, although they face limitations in applications demanding high motion performance and spatial positional accuracy. While novel manufacturing solutions supporting small batch productions of custom products are widely researched, they are not yet fully available at industrial level. With the aim to advance in this domain, the present work, conducted in the context of the EU project OPeraTIC, reports the development of a novel manipulator for advanced three-dimensional laser surface treatment of large industrial components. The proposed robotic platform presents a decoupled kinematic architecture, with direct drive actuation in all axes. Its open control ensures adaptability to diverse manufacturing scenarios, making it a versatile tool for modern production lines. Starting from the description of its embodiment design and mechanical layout, the paper delves into robot virtual prototyping focusing on kinematic and dynamics aspects. In particular, a detailed behavioral model covering direct and inverse kinematic calculations, also allowing the precise evaluation of all actuation forces/torques, has been developed using analytical approaches. The model is validated with a commercial solver imposing different spatial motions. The generated performance maps illustrate the robot operational capabilities across a range of work scenarios.

Keywords: dynamics , kinematics , multibody model , robotic design , simulation , virtual prototyping

This paper presents a novel metrological approach for the functional geometric characterization of lifting airfoils, utilizing an Iterative Closest Point (ICP) algorithm to assess deviations in camber, thickness, and form. Traditional geometric specification methods, such as line profile tolerances, often fail to capture the full impact of geometric deviations on airfoil performance. In response, the proposed methodology addresses this limitation by linking airfoil geometry more closely to functional requirements. The new methodology was validated using synthetic datasets and real-world data, demonstrating robustness in the absence of noise and highlighting areas for improvement in noise handling. The findings suggest that the ICP-based method is a valuable tool for airfoil manufacturing, enhancing conformity checks against design specifications.

Keywords: Airfoil Metrology , Form Error Analysis , Geometric characterization , Iterative Closest Point , Quality Control in Aerospace Design , Tolerance Verification

This work introduces the concept of variable-dependent admissible limits for tolerance stack-up analysis, where limits adapt based on geometrical and non-geometrical variables. Unlike traditional methods that assume fixed limits, the proposed approach integrates these dependencies into both variational and Monte Carlo analyses, enabling broader tolerances while maintaining functionality. A case study from the automotive sector demonstrates the methodology's effectiveness.

Keywords: Non-geometrical stack-up , Product development , Tolerancing

In product design and development, achieving the desired performance requires meeting specific product requirements within defined boundaries. These requirements are encapsulated in the Product Definition Dataset, which consists of two key models: the Nominal Model and the Specification Model. The Nominal Model outlines the product's properties, features, and relationships, while the Specification Model defines the targets, boundary conditions, requirements, allowable variation, and interrelationships essential for meeting these targets. This study focuses on the relationships between variable quantities and the limits set by geometrical product specifications. Currently, the limits defined within geometric specifications are considered static, excluding maximum and minimum material conditions. This implies that each variable is treated independently, meaning the actual state of one variable does not influence the functional limits of others. However, in actual parts and assemblies, variable quantities do affect each other, as they are not independent. In this paper, we explore the link between specifications and the variables they define through a case study, with the aim of fulfilling product requirements more effectively. By establishing these connections, it becomes possible to produce functional parts with greater allowable variation and reduced costs, while ensuring that specifications are grounded in actual performance requirements and physics. This approach aims to enhance the flexibility of product manufacturing and streamline the process of meeting both functional and cost-related objectives.

Keywords: Design Optimization , Functional Specification , Geometric Specification , Tolerance Optimization , Variable Correlation

This work aims at improving the quality, meaning both dimensional accuracy and green density, of binder jetting products by the optimization of printing parameters. Cubes with a squared through hole were printed using AISI 316L stainless steel powder, varying four printing parameters: dark body (binder saturation), shell thickness, printhead, and powder spreading speed. Taguchi methodology was employed to design nine orthogonal experiments, permutating the four factors at three levels. The green density was measured by mass to volume ratio, and the dimensions of the printed parts were derived by coordinate measuring machine (CMM) measurements. Green density and dimensional accuracy were the response variables to be optimized. It was observed that green density varied in the interval (0.53 ÷ 0.57) of relative density; ANOVA (ANalysis Of VAriance) showed that the highest contribution to maximize green density (81%) is given by dark body. Concerning dimensional accuracy, the difference between measured and nominal dimensions, referred to nominal dimensions, varied between 0.53% and −1.01%. Dimensions parallel to binder injection direction (X) are mostly affected by shell thickness and dark body (35% and 55%, respectively). Printhead speed and powder application speed, instead, mostly affect dimensions parallel to powder spreading direction (Y) and dimensions aligned with building direction (Z)—printhead speed 56% for Y and 37% for Z, powder application speed 30% for Y and 24% for Z, respectively. A physical explanation is discussed, moreover linear regression models are proposed for optimizing parameters.

Keywords: Additive manufacturing , Design of experiment , Dimensional accuracy

Metal Binder Jetting (MBJ) has a great potential for producing complex products in medium to large batches. However, challenges remain in achieving high dimensional and geometrical accuracy. This study investigates the influence of printing parameters and sample geometry on the accuracy of 316L stainless steel green products produced via MBJ. A Design of Experiments (DoE) approach was employed to study four factors at three levels. A Taguchi L9 orthogonal array was designed to evaluate the effects of printhead speed, binder saturation grade, blade speed, and shell thickness on dimensional and geometrical accuracy. Four different geometries were printed and then measured using a coordinate measuring machine (CMM) to assess the accuracy of external and internal dimensions, flatness, and parallelism. The results showed that dimensional accuracy was influenced by printing direction, feature size, and the feature type. External dimensions showed highest accuracy along the powder spreading direction (Y: 0.12% ± 0.21%), followed by building direction (Z: 0.25% ± 0.3%) and binder injection direction (X: 0.40% ± 0.2%). Significantly lower accuracy was found in internal features, showing a negative deviation (X: − 0.66% ± 0.32%; Y: − 0.41% ± 0.23%). Flatness and parallelism errors were found almost constant across experiments, with average values of 0.014 ± 0.008 and 0.024 ± 0.014, respectively. Parallelism appeared to be affected by geometry, specifically, increasing height-to-thickness ratios led to larger errors. Grey relational analysis identified printhead speed as the most critical parameter, followed by binder saturation grade, shell thickness, and blade speed in optimizing the accuracy of the printed parts. ANOVA analysis reveals that printhead has a statistical relevance showing a P-value of 0.029. This work provides novel insights into the printing factors affecting the quality of green parts produced by MBJ. This article helps in identifying the optimal printing parameters for binder jetting of SS 316L.

Keywords: Design for additive manufacturing , Dimensional accuracy , Geometrical accuracy , Grey relational analysis , Metal binder jetting

This study investigates the accuracy of microchannels fabricated using binder jetting. The research aims to contribute to the future development of continuous-feeding anodes for direct carbon fuel cells (DCFC) within the framework of the PRIN PNRR JETCELL project. The anodes are engineered with tailored microchannel architectures to maximize surface area to improve the electrochemical efficiency, facilitate the electrolyte flow, and prevent the fuel leakage. Stainless steel 316L and red mud composites were employed, as stainless steel provides high mechanical properties, while red mud enhances sustainability and functionality for anode applications. By systematically varying microchannel dimensions and spacings, the manufacturability and structural fidelity of micro-scale features critical to device performance have been assessed. Dimensional analysis, conducted via confocal microscopy, reveals key insights into shape retention and dimensional accuracy. To address sintering-induced shrinkage and dimensional deviation induced by printing process, scaling factors law as a function of nominal channel size have been proposed, paving the way for more predictable and precise fabrication of advanced functional components. © N. Shang et al.

Keywords: binder jetting , composite materials , dimensional accuracy , direct carbon fuel cell , Microchannel architectures , red mud

In the project development of large fusion machines, the alignment and assembly phases represent an open challenge. Each mechanical component that constitutes the whole machine assembly and fixtures is affected by an intrinsic geometrical variation due to manufacturing and assembly processes. The complexity of the assembly together with the unavoidable geometrical imperfection can affect the expected machine functionalities. In this context, Reverse Engineering activities, aiming at obtaining the as-built of the components, coupled with virtual fitting studies, defining optimum alignment using the real shape of the components, become powerful risk mitigation tools in the assembly phases. This work presents a systematic method, complementary to the methodology currently followed in the ITER project, for the as-built management and for the validation of alignment phases of such a complex assembly. The first phase is to identify the key assembly requirements which drive the alignment of the ITER assembly. Then, the database for the requirement collection is clearly organized following the proper nomenclature rules and its architecture for data collection and analysis is indeed set up.

Keywords: Alignment , As-built Management , Assembly Requirements , ITER tokamak , Requirement Management , Virtual Fitting

PURPOSE. This study aims to compare the occlusal trueness and precision of teeth manufactured using two modern digital milling processes. MATERIALS AND METHODS. A total of 38 complete dentures (CDs) were fabricated and analyzed. CDs in Group 1 (monolithic) (n = 19) were produced using a monolithic bicolor resin disk, whereas in Group 2 (oversize) (n = 19) were fabricated using the oversize process, which involves two separate resin disks of different colors. Two investigation methods were developed to evaluate trueness and precision: cusp area analysis and cusp vertex analysis. The study included three levels of analysis: a comparison of the two measurement methods, an evaluation of the monolithic versus oversize processes, and an assessment of under-and overcontouring inaccuracies. RESULTS. Statistical analysis using the Welch two-sample t-test, the non-parametric Wilcoxon signed-rank test, and the modified signed-likelihood ratio test (SLRT) revealed a statistically significant difference (P < 2.2 × 10-16) between the two measurement methods (vertex vs. area) for both the monolithic and oversize groups, with the vertex method demonstrating greater accuracy.The analysis of over-and undercontouring inaccuracies revealed that 55% of the surface for the monolithic process exhibited overcontouring, compared to 99% for the oversize process, indicating a strong tendency toward surface roughness in the latter. CONCLUSION. The monolithic milling method exhibited significantly superior accuracy compared to the oversize process (P < 05). Additionally, the Reference Point System (RPS) metrological method proved more reliable thanthe best-fit method for comparing complex structures, offering more accurate estimates of both trueness and precision. © 2025 The Korean Academy of Prosthodontics This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.

Keywords: Accuracy , Complete denture , Computer aided manufacturing , Metrology , Milling machine

Background: Differentiating Parkinson's disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with 123I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed 123I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of 123I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging.

Keywords: 123I-Ioflupane , artificial intelligence , BasGanV2™ , DaTQUANT® , dopamine transporters , machine learning , movement disorders , Parkinson's disease , rehabilitation , SPECT

The design of delta robots poses significant challenges as their mechanical behavior depends on a high number of dimensional parameters and dynamic factors. This is further compounded by the presence of demanding performance requirements, particularly in terms of position accuracy during high-dynamics motion tasks. By leveraging theoretical models, dynamic optimization techniques and advanced simulations, the present paper aims to streamline the design process, providing a structured engineering method and tool to address the dimensional synthesis of delta robots, encompassing kinematics, dynamics, link flexibility, and ball joint clearance. The systematic design process incorporates user requirements, including bounding box specifications, cycles per minute for pick-and-place operations, end-effector accuracy tolerance, maximum static payload, and cost minimization. The methodology involves an initial dynamic optimization phase employing a genetic algorithm to derive optimal dimensional parameters. Analytical models implemented in Matlab expedite the iterative optimization process. Then, the optimized design is virtually prototyped in RecurDyn flexible multibody simulation tool for validation by including the link flexibility and the effect of ball joint clearances. The iterative approach ensures that the final design aligns with user expectations. Additionally, the paper addresses motor selection based on torque requirements and proposes an approach for evaluating the robot performance in terms of maximum end-effector acceleration and payload. Finally, the efficacy of the tool is evaluated through a case study focused on designing a manipulator as an integral part of a collaborative research project with an industrial partner.

Keywords: Delta robot , Design tool , Industrial robotics , Multibody simulation , Optimization

The automotive development of Electric Vehicles (EVs) is accelerating due to environmental concerns and technological advances. The complexity and performance demand of EVs require the use of Silicon-Carbide (SiC) technologies for the related efficiency, high-temperature resistance, and robust dynamic switching. To ensure the high performance of electric vehicles, the rigorous screening of the delivered SiC devices plays a pivotal role. Defects in SiC production can cause failures that directly affect the performance of electric vehicle engines, particularly in the traction inverter subsystem. To identify these defects early, advanced visual screening through optical microscopy and X-ray approaches has been proposed by leading car makers. These screening methodologies require advanced technical expertise and are still prone to significant errors, even when performed manually by experienced operators. To address these inefficiencies, the authors propose an ensemble deep learning pipeline for performing a robust automated visual inspection of SiC devices. By combining Multi-Head Attention blocks with adaptive input data distortion compensation through Jacobian regularization within convolutional architectures, the proposed model leverages global context and local feature maps, enhancing accuracy and robustness in multimodal defects detection. The proposed combined approach, tested on ACEPACK/TPACK DRIVE SiC power modules provided by STMicroelectronics, achieved an average accuracy of approximately 93% in both methodologies.

Keywords: Deep Learning , Defects analysis , Silicon-Carbide