Product Model Data Research Articles

Amplified temperature sensitivity of extreme precipitation events following heat stress

This study investigates global extreme precipitation events (EPEs) during warm seasons, with a particular focus on EPEs preceded by extreme heat stress (EPE-Hs) and a comparative analysis with those not (EPE-NHs). Using reanalysis product and Earth System Model data, the spatiotemporal characteristics and temperature sensitivities of EPEs are analyzed. Results show that EPE-Hs, while less frequent, have longer duration and greater magnitude compared to EPE-NHs, particularly in high latitude regions. In the future, a significant increase is projected in the characteristics of EPE-Hs, in contrast to the stable duration and magnitude of EPE-NHs. EPE-Hs demonstrate substantially higher temperature sensitivity than EPE-NHs, especially in low latitudes. The precipitation-temperature scaling relationships diverge markedly between EPE-Hs and EPE-NHs, with notable regional variations. These insights are pivotal for crafting region-specific early warning and adaptation strategies to mitigate the risks associated with extreme precipitation under the backdrop of global warming.

Data models in ship design and construction – insights from 4D BIM

The lack of a cohesive understanding of a ship product data model, from design to operations, is currently a limiting factor in realizing more efficient ship lifecycle management and design processes. The paper sheds light on the history and gaps in realizing an integrated, interoperable, and multi-domain ship product data model. It also explores practices from BIM (Building Information Modelling) as an inspiration for solutions to overcome challenges related to information modeling, integrated design environment, and 4D engineering and planning.

Optimizing the Execution of Product Data Models

Optimizing the Execution of Product Data Models

A Methodology for Integrating Hierarchical VMAP-Data Structures into an Ontology Using Semantically Represented Analyses

Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.

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.

Snow Cover on the Tibetan Plateau and Topographic Controls

Snow cover plays a critical role in global energy and water cycles. Snow cover on the Tibetan Plateau (TP) provides vital water sources in western China and Himalayan regions, in addition to its weather and climate significance. The massive high mountain topography of the TP is the main condition for the presence and persistence of snow cover on the plateau at the mid-low latitudes of the Northern Hemisphere (NH). However, how the mountain topography controls snow-cover distribution on the TP remains largely unclear, and the relationship is not well quantified. Here, the spatial distribution and the topographic controls of snow cover on the TP are examined based on snow cover frequency (SCF) derived from MODIS snow cover product (MOD10A2 v005) and digital elevation model (DEM) data. The results show that snow cover on the TP is spatially unevenly distributed, and that it is characterized by rich snow and high SCF on the interior and the surrounding high mountain ranges, with less snow and low SCF in inland basins and river valleys. Snow cover on the TP presents elevation dependence: the higher the altitude, the higher the SCF, the longer the snow cover duration, and the more stable the intra-annual variation. The annual mean SCF below 3000 m above sea level (m a.s.l) is less than 4%, and it reaches 77% above 6000 m a.s.l. The intra-annual snow cover variation below 4000 m a.s.l features a unimodal distribution, while above 4000 m a.s.l it presents a bimodal distribution. The mean minimum SCF below 6000 m a.s.l occurs in summer, while above 6000 m a.s.l it occurs in winter. Due to differences in solar radiation and moisture condition caused by the mountain slope and aspect, the mean SCF generally increases with mountain slopes, and it is the highest on the north-facing aspect and the lowest on the south-facing aspect.

ManuChain II: Blockchained Smart Contract System as the Digital Twin of Decentralized Autonomous Manufacturing Toward Resilience in Industry 5.0

In Industry 5.0 vision, machines are empowered with the interaction capability to autonomously make local decisions and coordinate with each other as well as humans. However, how to form a group consensus on the rapid self-organizing of the manufacturing process is critical for achieving manufacturing resilience under disturbances and disruptions. Based on our formerly developed system ManuChain (Leng et al., 2020), this article proposes a blockchained smart contract system (BSCS), named ManuChain II, as the digital twin of a decentralized autonomous manufacturing system for achieving resilience in Industry 5.0. A blockchain-secured multiagent system architecture together with a product data model is established to form the BSCS. The BSCS could prevent tampering with data and enhance the transparency of the product manufacturing process. In BSCS, two types of blockchained smart contracts (SCs) are established with a bi-level interplay computing architecture. The lower-level contracts perform the predefined and learned patterns of task coordination for achieving resilience under internal disruptions. The upper-level contracts are incorporated with communication-efficient decentralized deep-learning algorithms for the learning, updating, transferring, and sharing of coordination patterns used in the autonomous decision in lower-level SCs, thereby achieving the continuous improvement of the system’s decentralized autonomous intelligence. Via incorporating the decentralized deep learning algorithms into blockchained SCs, this study reveals a new way to realize the self-organizing intelligence of the manufacturing system for enhancing resilience toward Industry 5.0.

Milling Features Recognition Methodology of Rectangular Fillet Blind Pocket for Universal Data Structure

Various studies have focused on developing an interface system that could integrate the machining process with CNC (Computer Numerical Control) machine tools by producing tool paths using STEP (Standard for the Exchange of Product model data) file as an input function. Studies to recognize features in STEP file by encoding the data of geometric entities of a CAD (Computer-Aided Design) design has been done by many researchers. However, they only focus on Cutter Location (CL) which eventually describes the machining profile in the x and y planes without considering the depth of the machining feature in the z-axis. Identifying and recognizing machining features in the STEP file is important since STEP is a platform for converting industry-standard CAD systems and software data models. The aim of this paper is to identify profiles of geometric machining features for rectangular fillet blind pockets using the Geometric Data Extraction (GDE) approach. Three cases have been carried out namely single rectangular fillet blind pocket, double rectangular fillet blind pocket and triple rectangular fillet blind pocket. The results were verified by manual graph plotting and automatic graph plotting using CPM 3D Plotter.

GraphSTEP: concurrent, cloud-based, lifecycle-oriented editing and updating of product data

A product's digital representation enables various activities in its lifecycle. In a neutral, file-based exchange format, like the STandard for the Exchange of Product model data (STEP), asynchronous access is possible throughout different software solutions, increasing the work interoperability across different stakeholders. However, the file-based handling of product data from the top to the shop floor and further lifecycle phases, subject to industrial practice, contradicts the Digital Twin (DT) concept embodying a centralized, single source of trust. To address this deficit, we propose GraphSTEP: a cloud-based platform to store, access, and manipulate product data, enabling concurrent, collaborative data management while preserving history through versioning.

A semantic similarity-based method to support the conversion from EXPRESS to OWL

Abstract Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS, the descriptive language of STEP, to Web Ontology Language (OWL). In most research, this mapping is a manual process which is time-consuming and prone to misunderstandings. To support this conversion, this research proposes an automatic method based on natural language processing techniques (NLP). The similarities of language elements in the reference manuals of EXPRESS and OWL have been analyzed in terms of three aspects: heading semantics, text semantics, and heading hierarchy. The paper focusses on translating between language elements, but the same approach has also been applied to the definition of the data models. Two forms of the semantic analysis with NLP are proposed: a Combination of Random Walks (RW) and Global Vectors for Word Representation (GloVe) for heading semantic similarity; and a Decoding-enhanced BERT with disentangled attention (DeBERTa) ensemble model for text semantic similarity. The evaluation shows the feasibility of the proposed method. The results not only cover most language elements mapped by current research, but also identify the mappings of the elements that have not been included. It also indicates the potential to identify the OWL segments for the EXPRESS declarations.

STEP-NC Process Planning for Powder Bed Fusion Additive Manufacturing

Abstract Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses high-power beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, the selection of appropriate process parameters, environmental control, and machine functions play critical roles in maintaining fabrication consistency and reducing potential part defects such as cracks and pores. However, poor data representations in the form of approximated geometry and incoherent process plans can negatively impact the relationship between the selected parameters. To address this issue, the Standard for the Exchange of Product model data Numerical Control (STEP-NC) recently added standardized data entities and attributes specifically for AM applications. Yet, the current STEP-NC data representations for AM do not have definitions for process parameters and scan strategies that are commonly used in PBF processes. Therefore, there is a need for defining data models that link process parameters with process control. To bridge this gap, in this paper, an amended STEP-NC compliant data representation for PBF in AM is proposed. Specifically, the characteristics of the interlayer relationships in PBF, along with the technology and scan strategy controls, are defined. Simulation results demonstrate the feasibility of granular process planning control and the potential for producing high-quality parts that meet geometric requirements and tight tolerances. The contributions of this paper highlight the importance of information models in AM, promoting data representations as key enablers of the AM technology and supporting the neutrality and interoperability of data across AM systems.

Knowledge graph construction for product designs from large CAD model repositories

Product Design based Knowledge graphs (KG) aid the representation of product assemblies through heterogeneous relationships that link entities obtained from multiple structured and unstructured sources. This study describes an approach to constructing a multi-relational and multi-hierarchical knowledge graph that extracts information contained within the 3D product model data to construct Assembly-Subassembly-Part and Shape Similarity relationships. This approach builds on a combination of utilizing 3D model meta-data and structuring the graph using the Assembly-Part hierarchy alongside 3D Shape-based Clustering. To demonstrate our approach, from a dataset consisting of 110,770 CAD models, 92,715 models were organized into 7,651 groups of varying sizes containing highly similar shapes, demonstrating the varied nature of design repositories, but inevitably also containing a significant number of repetitive and unique designs. Using the Product Design Knowledge Graph, we demonstrate the effectiveness of 3D shape retrieval using Approximate Nearest Neighbor search. Finally, we illustrate the use of the KG for Design Reuse of co-occurring components, Rule-Based Inference for Assembly Similarity and Collaborative Filtering for Multi-Modal Search of manufacturing process conditions. Future work aims to expand the KG to include downstream data within product manufacturing and towards improved reasoning methods to provide actionable suggestions for design bot assistants and manufacturing automation.

Integrated Interface System of STEP Data Models for the Tool Path Generation

The STEP (STandard for the Exchange of Product model data) is a worldwide standard for exchanging products between diverse computer systems and industrial applications. STEP files (ISO 10303) contain 3D data in the universal data format with a boundary representation (B-rep) that allows the exchange of data models between CAD, CAM, and CNC systems. Traditionally, G-code generation relies on CAM software as an add-on, which incurs significant installation costs. The purpose of this paper is to describe the development of an Integrated Interface System (I2S) for Tool Path Generation (TPG) for the CNC milling operations utilizing neutral STEP files.The algorithm was interpreted in Hypertext Preprocessor (PHP) programming and produced a computer interface system that is capable to convert STEP file geometric data to G-code format.The geometric features of the sample block were modeled using a 3D CAD model. The result was validated using machining simulation, and the machining is performed on a three-axis CNC milling machine with a PC-based controller.In modern manufacturing systems based on Open Architecture Controller (OAC), the development of the I2S provides an alternative to generating machining tool paths and G-code by intercepting CAM software.

An Additive Manufacturing Direct Slicing Algorithm Based on a STEP Model

The Standard Template Library (STL) file is the most common data format for the description of an additive manufacturing (AM) geometric model, but it has some disadvantages, such as large errors of the geometric model description, the easy loss of topology information, data duplication, large file sizes, and so on. Aiming at these problems, a direct slicing algorithm based on a Standard for the Exchange of Product Model Data (STEP) model was proposed. For the parts composed of basic types of surfaces such as boundary curves, spherical surfaces and cylindrical surfaces, the traditional geometric method was used to calculate the intersection. For the parts with complex surfaces, the three-dimensional models were described based on Non-Uniform Rational B-Spline (NURBS) surfaces. The NURBS surfaces were layered using a discrete tracking algorithm, the tracking starting point was determined, the intersection line between the tangent plane and each NURBS sub-surface was obtained, and the closed layer contour was formed. Finally, the slicing simulations and printing experiments of solid parts were carried out using the direct slicing algorithm based on the STEP model. It was shown that the dimensional accuracy and surface quality of the printed parts from this algorithm had been significantly improved.

Adaptive CAM planning to support co-design in the building industry

Co-design describes a collaborative design process between different stakeholders. These stakeholders usually come from various domains. To enable co-design, data has to be exchanged between the stakeholders. Current approaches to data management in the building industry are usually oriented along the process chain and therefore only allow a unidirectional flow of information. This is an obstacle in the implementation of co-design within the building industry.As more and more buildings are built by utilizing prefabricated modules to accelerate the on-site building process and to reduce the cost, a major effort in terms of implementing co-design is to link the design information of the building industry with the production information of the manufacturing industry. The problem occurs as the building industry mainly uses the fndustry Foundation Classes and the manufacturing industry uses the Standard for the Exchange of Product Model Data as a data format to exchange information. This leads to a break in the flow of information. Furthermore, information about the manufacturability of products, that is generated during the computer-aided manufacturing process, cannot be easily returned to the architect who can utilize the information to adapt the design in the computer-aided design process.This paper addresses these problems by defining a data processing pipeline that enables the conversion of the geometric design information stored in IFC into the STEP data format. From there, the CAM strategy can be defined and results in an executable G-Code. The information of the CAM planning is returned to the architect, who can adapt the design by interpreting the results of the CAM processing. To close the co-design loop, the G-Code for the design adaptions can be generated automatically by reusing the predefined CAM strategy.

A New Implementation of ontostep: Flexible Generation of Ontology and Knowledge Graphs of express-Driven Data

Abstract ontostep is a method to translate the STandard for the Exchange of Product Model Data (STEP) schema and its instances to an ontology and knowledge graphs represented in the web ontology language (owl). ontostep models can be integrated with any owl models to enrich their semantics. However, the current implementation has several limitations, mainly in (1) supporting the latest International Organization for Standardization (ISO) 10303 schemas and (2) generating various representation types depending on the purpose of use. We present an improved implementation of ontostep to overcome these limitations. In this article, we demonstrate that the new implementation can successfully translate STEP schemas and instances in a faster and more flexible way, thus furthering the adoption of the full capabilities of ISO 10303. By encoding STEP entities in owl, we facilitate integration with other standards through knowledge graphs.

Tool Path Generation for Linear Cutting Using Step File (ISO 10303) Data Structure

The ISO 10303 also known as the Standard for the Exchanged Product data (STEP) that describes how to represent and exchange the product manufacturing information. STEP’s data structure incorporates three-dimensional data of product design elements and manufacturing details which allows users to access 3D product model/information via different CAD applications. These product data model if exploited fully can be used in CAD/CAM and CNC system to perform machining simulation or physical machining operation. The paper describes the interface system development of tool path generation for linear cut using STEP file as input file. The system must be capable of identifying, extracting, processing the geometric data from STEP file and generate tool path according to G-code ISO 6983. A machined block sample with straight slot profile is designed from a 3D CAD modeler and saved in the STEP file format. This file was then upload into the Interface system to auto generate a G-code. The simulation and physical machining using 3-axis CNC milling as to validate the output.

STEP-NC-Compliant Implementation to Support Mixed-Control Technologies Applied to Stone-Processing Machines Based on Industrial Automation Standards

STEP-NC (Standard for the Exchange of Product Model Data–Numerical Control) for metal milling and turning is not implemented by industrial computer numerical controllers. Solutions reported are prototypes based on post-processing in G-code. Moreover, minority machining processes, such as stone cutting, have not yet been contemplated in the STEP-NC standard. This article takes that sector as a use case. An extended STEP-NC model for circular saw stone-cutting operations is proposed, and a prototype automation implementation is developed to work with this extended model. This article shows how modern technological resources for coordinated axes control provided by many industrial controllers for the automation of general-purpose machines can speed up the processes of implementing STEP-NC numerical controllers. This article proposes a mixed and flexible approach for STEP-NC-based machine automation, where different strategies can coexist when it comes to executing STEP-NC machining files, so controllers do not need to implement the standard in an exhaustive way for all the possible features, but only at selected ones when convenient. This is demonstrated in a prototype implementation which is able to process STEP-NC product files with mixed-feature types: standard milling and non-standard sawblade features for stone processing.

Establishment of a finite element model based on craniofacial soft tissue thickness measurements and stress analysis of medical goggles

In this study, an accurate finite element (FE) stress analysis of head-mounted products for Chinese users was performed. Using craniofacial computed tomography scans of 280 Chinese individuals, the total soft tissue thickness and thickness of the fat and muscle layers for 41 landmarks were measured. The data were used to construct FE head models (FEH). An FE stress test was conducted to analyse the wearing of medical goggles using two FE models based on one-layer (FEH 1) and three-layer (FEH 3) soft tissue material parameters. When compared with the experimental results, the modelling results for FEH 3 were more realistic than those for FEH 1. Wearing medical goggles led to stress concentration over five landmark areas, A: upper medial forehead, B: temporal, C: zygion, D: infraorbital fossa and E: rhinion, of which B, C and D caused the most discomfort during long-term goggle wear. Practitioner summary: A precise FE head model can reflect the complex contact pressure of a head-related product. Two FE models based on one- and three-layer soft tissue material parameters were established and tested separately with medical goggles. The model can be used to improve the comfort of head-related products. Abbreviations: FE: finite element; FEH: FE head models; FEH 1: FE models based on one-layer; FEH 3: FE models based on three-layer; VR: virtual reality; AR: augmented reality; 3D: three-dimensional; WSU: Wayne State University; WSUBIM: Wayne State University Brain Injury Model; CT: computed tomography; MRI: magnetic resonance imaging; CFSTT: craniofacial soft tissue thickness; FSR: force sensing resistor; NURBS: non-uniform rational basis spline; SPSS: statistical product and service solutions; STL: stereolithography; STP: standard for the exchange of product model data; BDF: glyph bitmap distribution format; EEG: electroencephalogram

A Data Management Approach Based on Product Morphology in Product Lifecycle Management

In the product life cycle from conception to retirement, there are three forms: conceptual products, digital products and physical products. The carriers of conceptual products are requirements, functions and abstract structures, and data management focuses on the mapping of requirements, functions, and structures. The carrier of digital products is digital files such as drawings and models, and the focus of data management is the design evolution of product. Physical products are physical entities, and their attributes and states will change over time. Existing data model research often focuses on one or two forms, and it is even impossible to integrate three forms of data into one system. So, a new data management method based on product form is presented. According to the characteristics of the three product form data, a conceptual product data model, a digital product data model, and a physical product data model are established to manage the three forms of data, respectively, and use global object mapping to integrate them into a unified data model. The conceptual product data model has a single data model for a single business stage. The digital product data model uses the core data model as the single data source, and uses one stage rule filter to add constraints to the core data model for each business stage. The physical product data model uses the core data model to manage the public data of the physical phase, and the phase private data model focuses on the private data of each business phase. Finally, a case of Multi-Purpose Container Vessel is studied to verify the feasibility of the method. This paper proposes three product forms of product data management and a unified data management model covering the three product forms, which provides a new method for product life cycle data.