STEP File Research Articles

TPMS2STEP: Error-Controlled and [formula omitted] Continuity-Preserving Translation of TPMS Models to STEP Files Based on Constrained-PIA

Triply periodic minimal surface (TPMS) is emerging as an important way of designing microstructures. However, there has been limited use of commercial CAD/CAM/CAE software packages for TPMS design and manufacturing. This is mainly because TPMS is consistently described in the functional representation (F-rep) format, while modern CAD/CAM/CAE tools are built upon the boundary representation (B-rep) format. One possible solution to this gap is translating TPMS to STEP, which is the standard data exchange format of CAD/CAM/CAE. Following this direction, this paper proposes a new translation method with error-controlling and C2 continuity-preserving features. It is based on an approximation error-driven TPMS sampling algorithm and a constrained-PIA algorithm. The sampling algorithm controls the deviation between the original and translated models. With it, an error bound of 2ϵ on the deviation can be ensured if two conditions called ϵ-density and ϵ-approximation are satisfied. The constrained-PIA algorithm enforces C2 continuity constraints during TPMS approximation, and meanwhile attaining high efficiency. A theoretical convergence proof of this algorithm is also given. The effectiveness of the translation method has been demonstrated by a series of examples and comparisons. The code will be open-sourced upon publication.

An interior penalty coupling strategy for isogeometric non-conformal Kirchhoff–Love shell patches

This work focuses on the coupling of trimmed shell patches using Isogeometric Analysis, based on higher continuity splines that seamlessly meet the C1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C^1$$\\end{document} requirement of Kirchhoff–Love-based discretizations. Weak enforcement of coupling conditions is achieved through the symmetric interior penalty method, where the fluxes are computed using their correct variationally consistent expression that was only recently proposed and is unprecedentedly adopted herein in the context of coupling conditions. The constitutive relationship accounts for generically laminated materials, although the proposed tests are conducted under the assumption of uniform thickness and lamination sequence. Numerical experiments assess the method for an isotropic and a laminated plate, as well as an isotropic hyperbolic paraboloid shell from the new shell obstacle course. The boundary conditions and domain force are chosen to reproduce manufactured analytical solutions, which are taken as reference to compute rigorous convergence curves in the L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L^2$$\\end{document}, H1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H^1$$\\end{document}, and H2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H^2$$\\end{document} norms, that closely approach optimal ones predicted by theory. Additionally, we conduct a final test on a complex structure comprising five intersecting laminated cylindrical shells, whose geometry is directly imported from a STEP file. The results exhibit excellent agreement with those obtained through commercial software, showcasing the method’s potential for real-world industrial applications.

Advancements in CNC Machining: Rectangular Boss Feature Validation and Toolpath Precision using STEP AP242 and B-Rep Data Structure

In the context of a CNC machining application, this study proposes an alternate approach for creating toolpaths designed explicitly for machining rectangular bosses utilizing the STEP AP242 file format as a data source. The suggested methodology ensures data accuracy and integrity maintenance by extracting exact geometric information from digital models using Geometric Data Extraction (GDE) tools. The value of the Cartesian multi-point decimal point in the data structure and variations in the reference number of the Cartesian point every time a user accesses and converts to a STEP (AP203) file are two drawbacks of AP203 when compared to the widely utilized AP203 in prior technique studies. This method makes it possible to create G-code efficiently, which is necessary for managing computer numerical control (CNC) machines, by utilizing the power of STEP AP242 and GDE. The process entails finding and separating the rectangular island milling features in the STEP AP242 files, taking out critical geometric characteristics, and creating toolpaths that are optimal for CNC machining. Through this approach, the final G-code instructions are guaranteed to be precisely calibrated to consider every island feature's distinct quality while preserving overall machining efficiency. Several real-world case studies and valuable applications are investigated to confirm the efficacy of the suggested method. The results show significant increases in productivity, decreased waste material, and improved precision of CNC machining, all of which contribute to a more effective and economical production process.

An overview of last decade’s developments in RayXpert®, a 3D Monte Carlo code

This article provides an overview of the developments made over the last 10 years within RayXpert®, a CAD-based geometry 3D Monte Carlo software developed by TRAD – Tests & Radiations. The main features of RayXpert® are its 3D Monte Carlo engine and its CAD-based geometry. It is also possible to import STEP file, automatically detect overlaps, and perform parallel Monte Carlo computations. During the last 10 years, numerous new features were added to the software: TTB approximation, dose and flux mapping, computation resumption, radioactive decay computation, script support, MPI computation, advanced convergence indicators, etc. New features such as advanced biasing or neutron-induced activation simulation are under development. This article presents the past and future functionalities of RayXpert®.

Semantic part segmentation of spatial features via geometric deep learning for automated control cabinet assembly

AbstractIndustries with batch size one manufacturing philosophies for highly customized products have been largely limited in manufacturing automation. The control cabinet industry is particularly affected by this problem during the mounting and wiring of components due to high variety, variance, and complexity of components as well as handling tasks. Rapid advances in the field of machine learning are opening new possibilities for automating previously manual processes. This paper proposes a concept for identifying geometric features of electrical components that starts from STEP files and transforms them into modular metrics relevant to build a digital twin and (automatic)manufacturing. The architecture is tested on a self-aggregated and processed dataset of control cabinet components and achieves an average dice score of 65.27% and an intersection over union of 51.41% across all segmentation classes. In addition to semantic part segmentation of the components, the cluster, volume and surface centroids, the normal vectors and the size of each feature are computed. The paper evaluates the suitability of cutting-edge techniques such as diffusion as well as established deep learning architectures. The result is a hybrid end-to-end inference pipeline suitable for general spatial assembly processes.

A Parallel Algorithm to Parse STEP Files Based on Multiple Threads

A Parallel Algorithm to Parse STEP Files Based on Multiple Threads

AAGNet: A graph neural network towards multi-task machining feature recognition

Machining feature recognition (MFR) is an essential step in computer-aided process planning (CAPP) that infers manufacturing semantics from the geometric entities in CAD models. Traditional rule-based MFR methods struggle to handle intersecting features due to the complexity of representing their variable topological structures. This motivates the development of deep-learning-based methods, which can learn from data and overcome the limitations of rule-based methods. However, some existing deep learning methods compromise geometric and topological information when using certain representations such as voxel or point cloud. To address these challenges, we propose a novel graph neural network, named AAGNet, for automatic feature recognition using a geometric Attributed Adjacency Graph (gAAG) representation that preserves topological, geometric, and extended attributes from neutral boundary representation (B-Rep) models. Furthermore, some existing methods (such as UV-Net, Hierarchical CADNet) lack the capability of machining feature instance segmentation, which is a sub-task of feature recognition that requires the network to identify different machining features and the B-Rep face entities that constitute them, and it is a crucial task for subsequent process planning. AAGNet is designed as a multi-task network that can perform semantic segmentation, instance segmentation, and bottom face segmentation simultaneously for recognizing machining features, the faces associated with those features, and their bottom faces. The AAGNet is evaluated on various open-source datasets, including MFCAD, MFCAD++, and the newly introduced MFInstSeg dataset with over 60,000 STEP files and machining feature instance labels. The experimental results demonstrate that AAGNet outperforms other state-of-the-art methods in terms of accuracy and complexity, showing its potential as a flexible solution for MFR in CAPP.

G-code converter using interface system for a STEP file (ISO 10303)

The STEP file is the “standard for the exchange of product model data,” which is usually used to exchange geometric data in boundary representation (B-rep) between different computer-aided design (CAD) platforms. These data can be fully utilized and integrated into a larger manufacturing organization, such as a computer-aided manufacturing (CAM) environment for computer numerical control (CNC) machining applications based on ISO 6983. The 3D models of 2D machining profiles were created in CAD software and saved as STEP files. The data structure was analyzed by comparing the geometric entities of the CAD model and the STEP file. The algorithm was created using the hypertext preprocessor (PHP) programming language and produced a computer interface system to convert STEP files into G-code format. The machining blocks with profile machining features were simulated using a CNC simulator and PC-based open architecture control (OAC) software. The G-code was validated on a three-axis CNC milling machine, and the result was compared to the CAD model to confirm the machining profile. The integrated interface system I2S demonstrates its ability to interpret all 2D profiles and generate machining tool paths and G-code, allowing data flow between CAD and CAM environments and shortening product development cycles.

Effect of crown stiffness and prosthetic screw absence on the stress distribution in implant-supported restoration: A 3D finite element analysis.

This in-silico investigation evaluated the mechanical impact of Morse tape implant-abutment interface and retention system (with and without screw) and restorative materials (composite block and monolithic zirconia) by means of a three-dimensional finite element analysis (3D-FEA). Four 3D models were designed for the lower first molar. A dental implant (4.5 × 10 mm B&B Dental Implant Company) was digitized (micro CT) and exported to computer-aided design (CAD) software. Non-uniform rational B-spline surfaces were reconstructed, generating a 3D volumetric model. Four different models were generated with the same Morse-type connection, but with a different locking system (with and without active screw) and a different crown material made of composite block and zirconia. The D2 bone type, which contains cortical and trabecular tissues, was designed using data from the database. The implants were juxtaposed inside the model after Boolean subtraction. Implant placement depth was simulated for the implant model precisely at crestal bone level. Each acquired model was then imported into the finite element analysis (FEA) software as STEP files. The Von Mises equivalent strains were calculated for the peri-implant bone and the Von Mises stress for the prosthetic structures. The highest strain values in bone tissue occurred in the peri-implant bone interface and were comparable in the four implant models (8.2918e-004-8.6622e-004 mm/mm). The stress peak in the zirconia crown (64.4 MPa) was higher than in the composite crown (52.2 MPa) regardless of the presence of the prosthetic screw. The abutment showed the lowest stress peaks (99.71-92.28 MPa) when the screw was present (126.63-114.25 MPa). Based on this linear analysis, it is suggested that the absence of prosthetic screw increases the stress inside the abutment and implant, without effect on the crown and around the bone tissue. Stiffer crowns concentrate more stress on its structure, reducing the amount of stress on the abutment.

Using semantic Geometric Dimensioning and Tolerancing (GD &T) information from STEP AP242 neutral exchange files for robotic applications

This article proposes the use of Geometric Dimensioning and Tolerancing (GD &T) information from STEP AP242 for robotic manufacturing applications. This information can be directly added to the relevant features of the 3D model as per Model Based Definition methodology during the design phase of the product life cycle. STEP AP242 neutral exchange files enable the availability of product definition at the downstream operations, thus completing the Digital Thread as part of Industry 4.0 practices. This article discusses two methods; using custom Unicode strings and the standard entities; for including GD &T using the STEP AP242 Edition 2 neutral file exchange format. A method to form a single Unicode string to add all the GD &T information to the STEP files is described in this paper. The GD &T information in the Unicode string is fully semantic and can easily be parsed to extract the relevant PMI for tolerance analysis. A novel process of extracting and interpreting the relevant PMI for robotic manufacturing applications is described in detail. This article discusses various applications of this information for robotic manufacturing through two use-cases using the second edition of the STEP AP242 standard.

A Multivariate Local Descriptor Registration Method for Surface Topography Evaluation

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.

Study on Process Design Based on Language Analysis and Image Discrimination Using CNN Deep Learning

At present, machining with numerically controlled (NC) machine tools is mostly performed by NC programs generated by computer-aided design and computer-aided manufacturing (CAD/CAM) systems. However, even if the machining shape to be machined is the same, there are numerous machining processes involving a series of operations such as determining the machining area, machining order, and machining conditions. These are entrusted to the user, and automation is difficult. In addition, these tasks depend on the experience and know-how of skilled engineers, and it is very difficult to convert them into algorithms and reflect them in the creation of NC programs. Therefore, in this study, artificial intelligence (AI) was used for the process design of multi-tasking machine tools, with the goal of determining and automating the process design using shape examples. We propose a shape recognition method that includes image analysis by AI. This image analysis makes it possible to determine the characteristics of the machining shape, and the machining operator can easily judge the machining process based on the CAD model. Furthermore, because there are shapes that cannot be determined from image data alone, shape features are also extracted from the STEP file of the CAD model. A language analysis of the STEP file can find the characteristic components and their numerical information to determine the coordinates of the shape features. By combining image analysis and language analysis, the method can easily judge the process based on the information in the CAD model. Finally, using the generated learning model and analysis program, we conducted a test to determine whether a multitasking machine tool is necessary for machining.

MRADSIM-Converter: A new software for STEP to GDML conversion

Radiation effects analysis of instruments operative in harsh radiation environment is crucial for performance and functionality of electronic devices and components. Engineering design of instruments is usually carried out in Computer Aided Design (CAD) engineering software. Geant4-based Monte Carlo codes are extensively used for particle transport simulation and analysis. However, Geant4 is not prepared to accept CAD Standard for The Exchange of Product data (STEP) format. MRADSIM-Converter is a new software for STEP to Geometry Description Markup Language (GDML) format conversion, readable by Geant4-based Monte Carlo codes. Its validation with two different converters confirms its higher speed for importing CAD geometries with arbitrary size and complexity having a user-friendly interface for modifying volumes properties. Program summaryProgram title: MRADSIM-ConverterCPC Library link to program files:https://doi.org/10.17632/8kzsyn79gh.1Licensing provisions: LGPLProgramming language: C++Supplementary material: CMake, Qt5, OpenCascadeNature of problem: Geant4 toolkit simulation code developed at CERN is widely used for high energy physics community as well as health physics and radioprotections communities. However, Geant4 is not prepared to receive the CAD formats file, and the geometry implementation in Geant4 is a time-consuming task as it requires the creation of each volume using specific classes.Solution method: MRADSIM-Converter is a software designed to perform a conversion between the files written in STEP format, prepared by CAD software, into the GDML format, accepted as input in Geant4. All existent volumes in the input STEP file are visualized in the Graphical User Interface (GUI), where it is possible to interactively assign the material to the components and modify each volume's properties, such as material, color, and name.Additional comments including restrictions and unusual features: MRADSIM-Converter is tested under Ubuntu, versions 18 and 20. All the libraries necessary to run the program are provided in the package hence no additional library or software is required to be installed. Under MacOS and Windows, MRADSIM-Converter is running through virtual machine (VM) with above-mentioned Ubuntu versions.

Automatic machining feature recognition from STEP files

ABSTRACT Automatic machining feature recognition (AMFR) is a critical component of CAD/CAPP/CAM integration. Multiple intersecting feature intersection causes a major problem in the research field. Due to this issue, an automated machining feature recognition method is presented to overcome this issue. This research aims to group the data of symmetrical faces and efficiently sort the faces according to their Cartesian values. The machining feature (MF) recognition algorithm can differentiate between hole segments of toroidal, prismatic, cylindrical and conical with varied groove blinds and feature attributes. Four distinct case studies were conducted in this research, which consist of geometrical and topological feature data extraction from the part, sorting of faces throughout the part and recognition of holes and groove blinds. The feature extraction and recognition techniques are implemented using Python language for rotatable components to detect rotatable parts with the prismatic shape of holes and groove blinds by employing the STEP file; this is often assessed using different case studies, respectively.

Smart Cloud Collocation: Geometry-Aware Adaptivity Directly From CAD

Computer Aided Design (CAD) is widely used in the creation and optimization of various industrial systems and processes. Transforming a CAD geometry into a computational discretization that be used to solve PDEs requires care and a deep knowledge of the selected computational method. In this article, we present a novel integrated collocation scheme based on smart clouds. It allows us to transform a CAD geometry into a complete point collocation model, aware of the base geometry, with minimum effort. For this process, only the geometry of the domain, in the form of a STEP file, and the boundary conditions are needed. We also introduce an adaptive refinement process for the resultant smart cloud using an a posteriori error indication. The scheme can be applied to any 2D or 3D geometry, to any PDE and can be applied to most point collocation approaches. We illustrate this with the meshfree Generalized Finite Difference (GFD) method applied to steady linear elasticity problems. We further show that each step of this process, from the initial discretization to the refinement strategy, is connected and is affected by the approach selected in the previous step, thus requiring an integrated scheme where the whole solution process should be considered at once. • CAD files are used to generate smart point clouds without any intermediate step. • Exact normal vectors and boundary conditions are based on the CAD geometry. • ZZ-type error indicators are the basis for model adaptivity and are quick to compute. • Model adaptivity speeds up the convergence rate of the solution. • Model adaptivity is based on the exact CAD geometry.

Constraint Definition for Gripper Selection and Grasp Planning for Robotic Assembly Using Product Manufacturing Information from STEP AP242Ed2 Files

This article uses the Product Manufacturing Information (PMI) from STEP AP242 neutral files for gripper selection and grasp planning in a robotic assembly operation. The PMI, along with the part geometry and dimensions, are used in identifying various handling features of the parts and selecting an appropriate gripper. The required PMI, like material, volume, surface finish, threading and coating information, are added to the STEP AP242 files. The PMI is semantically included in the STEP files following the Model Based Definition (MBD) methodology. Two methods are described to add the PMI to the STEP files, one using a custom string and another using the standard entities defined in ISO 10303 AP242: 2020 standard. The entire process is demonstrated in a use case.

Disassembly assessment from CAD-based collision evaluation for sequence planning

Disassembly sequence planning is the foundation for disassembly process planning. Furthermore, integrating efficient geometrical disassembly feasibility of non-destructive operations can contribute to designing robotic disassembly and remanufacturing systems. Non-destructive operations increase the chance of repurposing parts with less cost but require collision-free trajectories between components. Thus, generating geometrically feasible disassembly sequences is critical for determining the optimal disassembly sequencing and disassembly trajectories. In this work, geometric collision data has generated to develop the disassembly precedence matrix that provides fundamental information for disassembly planning methods. Current methods often require significant manual resources to locate geometrically feasible sequences for disassembly of products. This paper describes the results of utilizing disassembly collision tests to automatically extract interference data from STEP CAD assembly files to determine feasible disassembly operations. A method to determine a precedence matrix that describes all geometrically feasible disassembly operations for disassembly of a selected part or a whole assembly to its components has been developed and tested. Extracted data from STEP files is used to characterize the spatial properties of a component in an assembly and is the basis for the collision test to determine the geometrically feasible disassembly operations. The precedence matrix also identifies the feasible disassembly directions for each component based on the outcomes of the collision tests.

Using an ultra-compact optical system to improve lateral flow immunoassay results quantitatively

The lateral flow immunoassay (LFIA) is a paper-based platform with extensive application in point-of-care (POC) testing and many fields. However, its clinical application is severely limited due to the lack of quantitative ability of standard LFIA tests; this augmentation provides the system with quantifying the signal from magenta-colored AuNPs. To address this issue, we proposed an ultra-compact optical system that allowed LFIAs to be performed more accurately and objectively. The experimental setup consisted of multiple optical accessories manufactured by 3D printing (STEP files were included). A high-resolution printer was used to print out a magenta card model for the LFIA, whose color code, ranging from 255, 255, 255 to 255, 0, 255 in the RGB (red, green, blue) format, represents different levels of magenta color intensity (from 0% to 100%) and thus the results of LFIA test strips. A mathematical model was built using a calibration curve to describe the relationship between magenta color value and reflectance spectrum. In addition, a spectrum module was integrated into the proposed system to identify and quantify LFIA results. This integration represents a pioneering step in developing portable detection techniques that facilitate quantifying LFIA results. Finally, we expect this ultra-compact optical spectroscopy system to have great potential for novel clinical applications.

A complex machinery design-oriented digital prototype model driven method

Because of information loss during the conversion between a geometric digital prototype and a performance digital prototype in developing a complex mechanical product, the model-driven architecture of a digital prototype was constructed. By analyzing the constituent elements of a STEP file, the platform independent model based on the digital prototype was established. The platform specific model conversion method from the model-driven platform independent model to the UG software, the Adams software and the Abaqus software was proposed. Based on the heterogeneous software interface and the scripting language, the model-driven modules of the above three types of software were developed and verified with numerical instances. The verification results show that the use of the model-driven method can share the model information on the special software for a mechanical digital prototype and that the model-driven modules thus developed can perform the quick simulation analysis and output its results in time.

A universal method to compare parts from STEP files

Model Based Definition (MBD) captures the complete specification of a part in digital form and leverages (at least) the universal “Standard for the Exchange of Product” (STEP) file format. MBD has revolutionized manufacturing due to time and cost savings associated with containing all engineering data within a single digital source. This work presents a novel method to transform digital definitions in any given STEP file into a tensor-like structure that is unique for each part and can be used to regenerate the original STEP file completely. Resulting STEP tensors are amenable to part comparison based on various part specifications in a general and straightforward manner. Here, part similarity is evaluated among sets of parts according to specific geometry, material composition, and design intent. Importantly, specification similarity can be quantified using only the tensors’ structure. As such, this approach is not limited to families of geometric shapes, part types, or fabrication methods; nor does it require any prior knowledge about the parts being compared.