Model-based Definition Research Articles

Integrating MBD with BOM for consistent data transformation during lifecycle synergetic decision-making of complex products

Model-based definition (MBD) can be applied to normalized define and associate the whole lifecycle data of complex products by a three-dimensional (3D) digital model, which can further provide single data source for bill of material (BOM) transforming of different lifecycle stages. Therefore, a promising application combining MBD and BOM is to ensure the accuracy and completeness of information during various BOM transformation, and to provide consistent and reliable data support for lifecycle synergetic decision-making. However, the studies in the above field are facing many challenges. For example, the research and application of MBD in the whole lifecycle is still in its infancy. MBD and BOM are studied separately, and the overall solution that integrates these two pillars for lifecycle synergetic decision-making is almost vacant. A novel approach for consistent lifecycle data transformation is proposed to address these challenges. First, an overall conceptual framework is developed to show how the approach can be implemented. Second, a unified lifecycle data representation method with the MBD main-domain dataset is proposed. Third, based on the single data source from MBD dataset, xBOM multi-view transforming are realised by designing a set of mapping models and rules. Finally, an applicable scenario for an electric multiple units (EMU) bogie is presented to illustrate the effectiveness of the proposed approach. The result shows that the proposed approach can provide significant potential for industrial practitioners to carry out synergetic and optimal decision-making in the whole lifecycle management processes.

Surveying Quality Management Methodologies in Wooden Furniture Production

Furniture production is a specific industrial sector with a high human labor demand, a wide range of materials processed, and short production runs caused by high customization of end products. The difficulty of measuring the aesthetic requirements of customers is also specific to furniture. This review of academic papers identifies and explains effective quality management strategies in furniture production. The reviewed literature highlights a range of quality management methodologies, including concurrent engineering (CE), total quality management (TQM), lean manufacturing, lean six sigma, and kaizen. These strategies encompass a variety of pro-quality tools, such as 5S, statistical process control (SPC), quality function deployment (QFD), and failure mode and effects analysis (FMEA). The strengths of these quality management strategies lie in their ability to enhance efficiency, reduce waste, increase product diversity, and improve product quality. However, the weaknesses concern implementation challenges and the need for culture change within organizations. Successful quality management in furniture production requires tailoring strategies to the specific context of the furniture production industry. Additionally, the importance of sustainability in the furniture industry is emphasized, which entails incorporating circular economy principles and resource-efficient practices. The most important finding from the literature analysis is that early detection and correction of poor quality yields the most beneficial outcomes for the manufacturer. Therefore, it is essential to strengthen the rigor of quality testing and analysis during the early stages of product development. Consequently, a deep understanding of consumer perspectives on required furniture quality is crucial. The review identified two research gaps: (1) the impact of unnecessary product over-quality on the efficiency of furniture production and (2) the influence of replacing CAD drawings with a model-based definition (MBD) format on quality management in furniture production.

Ontology-based methodology for the intelligent detection of product manufacturing information semantic representation errors in the model-based definition

Ontology-based methodology for the intelligent detection of product manufacturing information semantic representation errors in the model-based definition

QSCC: A Quaternion Semantic Cell Convolution Graph Neural Network for MBD Product Model Classification

Model-based Definition (MBD) 3D engineering has gained wide popularity which enables smart manufacturing. Its tip orientation to the effective reuse of MBD-based product models for creating efficient digital twins. The deep learning-based 3D model classification is an important way in the most recent years, but the existing methods only concern geometric information with very little or no manufacturing information. In this paper, we proposed quaternion semantic cell convolution (QSCC), the quaternion semantic cell convolution graph neural network for MBD product model classification. It is mainly composed of the quaternion product semantic cell(QPSC) and the quaternion simple graph convolution(QSGC). The QPSC is established to fully express the digital twins' semantic information, which includes the multi-dimensional PMI information of the MBD model. The QSGC is established by embedding QPSC into the quaternion vector space, which serializes the multi-granularity semantic features to learn the context semantics through GRU. Compared with existing methods, our approach can solve the problem of model classification with similar geometry but different functions. Specifically, our approach can solve the ambiguity problem caused by blend features such as chamfer. We give quantitative and qualitative evaluations to demonstrate the accuracy and efficiency.

SemPower: General power analysis for structural equation models

Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect the hypothesized effect. However, applications of SEM rarely consider statistical power in informing sample size considerations or determine the statistical power for the focal hypothesis tests performed. One reason is the difficulty in translating substantive hypotheses into specific effect size values required to perform power analyses, as well as the lack of user-friendly software to automate this process. The present paper presents the second version of the R package semPower which includes comprehensive functionality for various types of power analyses in SEM. Specifically, semPower 2 allows one to perform both analytical and simulated a priori, post hoc, and compromise power analysis for structural equation models with or without latent variables, and also supports multigroup settings and provides user-friendly convenience functions for many common model types (e.g., standard confirmatory factor analysis [CFA] models, regression models, autoregressive moving average [ARMA] models, cross-lagged panel models) to simplify power analyses when a model-based definition of the effect in terms of model parameters is desired.

GNN-based deep reinforcement learning for MBD product model recommendation

ABSTRACT Digital twin is more and more widely used, and the delivery demand of digital twin is more and more prominent at the same time of product physical delivery. Research on the digital twin product model recommendation method is of great significance for the rapid construction and reuse of digital twins. The methods currently in use, however, principally concentrate on geometric reuse and pay little attention to functional or knowledge reuse. In this paper, a graph neural network (GNN)-based deep reinforcement learning (DRL) for product model recommendation is presented. First, an MBD (model-based definition)-based semantic feature attribute adjacency graph (MSFAAG) is introduced to structured MBD model as the carrier of the digital twin product model. The MSFAAG is then embedded into continuous vector spaces using a GNN to obtain the categorization of these MBD models. Finally, DRL is used to adaptively identify more important semantic features, including manufacturing semantics and functional semantics, to obtain more detailed model classification results. The experiment effectively improves the reuse efficiency of the non-geometric aspects of the digital twin product and MBD model. Compared with other traditional recommendation algorithms, the algorithm proposed in this paper has higher accuracy and can well meet the design requirements of users.

NDE 4.0: Progress, promise, and its role to industry 4.0

“Industry 4.0” stands for the fourth industrial revolution, the transition from production by computer controlled isolated machines to the concept of a smart factory, where machines, materials, and personnel are digitally connected, to actively adapt to changes in workflow. It emerges from the confluence of operational technologies (OT) and information technologies (IT), which allows for higher flexibility and manufacturing of unique custom products to suit individual customer requirements.To assure quality, embracing digital transformation of non-destructive evaluation is essential. It can be an integral part of the cyber-controlled production and asset life-cycle maintenance. These two value propositions covering the asset life cycle, require digitally controlled NDE procedures and qualitative data to support automated decision making, for most known situations. With that scenario, NDE must be considered in conjunction with other engineering disciplines. Reliability assessments become a necessary prerequisite for the use of NDE data. In addition, the approach to inspector certification and experience needs to be revised.The digital technologies that are being deployed to enhance production under industry 4.0 can very well be the enablers of digitalized NDE and their integration with the asset and its owner. Model-based definition, smart robots, artificial intelligence, augmented reality, and digital twins can all be used to enhance NDE inspections to a new level of performance in quality and safety assurance. These new NDE trends are summarized under the term “NDE 4.0” to meet the needs of Industry 4.0.The present review paper is aimed at capturing recent advances in digital technologies for non-destructive inspections, examples of their use, and other aspects that must be addressed to shift the paradigm. The paper also highlights the global collaboration and current trends, with an intent to inspire industry professionals to engage with NDE 4.0 – a must for Industry 4.0.

Exploring the Acceptance of the Technical Disclosure Method Based on 3D Digital Technological Process by Construction Workers through the Perspective of TAM

Effective pre-control methods for construction workers during the construction phase are important means to ensure the final quality of the construction product. The 3D digital technological process introduces model-based definition (MBD) to the construction industry and enhances construction process management to some extent by combining it with building information modeling (BIM). However, the proper application of the 3D digital technological process requires a good knowledge of the factors associated with its acceptance. This study introduced the 3D digital technological process into technical disclosure and further explored the factors that may influence its acceptance among the construction worker population. Several new extrinsic variables, cognitive level, subjective norms, technology anxiety, and enjoyment, were added and evaluated in the technology acceptance model (TAM), and a total of 314 data samples were collected to verify the hypothesized paths. The results showed that the proposed model was effective in predicting the attitude of the worker population towards this method.

An uncertainty management framework to support model-based definition and enterprise

In pursuit of industrial digitalisation, the manufacturing industry is going through a transformation in the methods for product definition from the traditional two-dimensional drawing to three-dimensional digital model known as the model-based definition (MBD). The real benefit of this digitalisation lies in the adoption of model-based definition across all stages of the product lifecycles throughout the enterprise and its supply chain which is termed as model-based enterprise (MBE). However, the current application of this technology is partial, due to the involvement of several associated uncertainties. This paper proposes a novel framework for the management of uncertainties in the adoption of the model-based definition and presents a system in support of the proposed framework. The development commenced with the collaboration of two major aerospace industries. The framework comprises five phases including the preliminary phase, identification, assessment, analysis, and response phase. A systematic process is followed in developing the framework while Numeral, Spread, Assessment, and Pedigree (NUSAP), and Analytical Hierarchy Process (AHP) are used in the assessment of uncertainties. The developed system consists of a user interface, a database of uncertainties, an assessment module, an analysis and prioritisation module, and a knowledge base of mitigation strategies for key uncertainties. The system facilitates the analyst to select the relevant uncertainties from a defined list, systematically assess and analyse each of the uncertainty and obtain recommendations for mitigation of the prioritised uncertainties in the project. The framework and the developed system were validated through expert interviews with two world-class aerospace companies. This system facilitates identifying the various types of uncertainties of MBD, quantifying their impact on the project rationally, and formulating a suitable management strategy for achieving the status of a model-based enterprise.

A Functional Classification of Text Annotations for Engineering Design

Describing and supplementing geometric shapes (parts) and layouts (assemblies) with relevant information is key for successful product design communication. 3D annotation tools are widely available in commercial systems, but they are generally used in the same manner as 2D annotations in traditional engineering drawings. The gap between technology and practices is particularly evident in plain text annotations. In this paper, we introduce a functional classification of text annotations to provide an information framework for shifting traditional annotation practices towards the Model-Based Definition (MBD) paradigm. In our view, the current classification of dimensions, tolerances, symbols, notes, and text does not stress the inherent properties of two broader categories: symbols and text. Symbol-based annotations use a symbolic language (mostly standardized) such as Geometric Dimensioning and Tolerancing (GD&T) to provide precise information about the implications of geometric imperfections in manufacturing, whereas notes and text are based on non-standardized and unstructured plain text, and can be used to convey design information. We advocate that text annotations can be characterized in four different functional types (objectives, requirements, rationale, and intent), which should be classified as such when annotations are added to a model. The identification and definition of a formalized structure and syntax can enable the management of the annotations as separate entities, thus leveraging their individual features, or as a group to gain a global and collective view of the design problem. The proposed classification was tested with a group of users in a redesign task that involved a series of geometric changes to an annotated assembly model.

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.

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Aiming at the low efficiency and intelligence degree in the parameter setting of computer-aided manufacturing (CAM) numerical control (NC) programming caused by manual work, an intelligent decision-making method of CAM NC programming parameters based on model-based definition (MBD) is proposed. The hierarchical process model uses MBD technology to express the process flow and manage the process information. The tightness between process and NC machining is enhanced by sorting out NC machining steps and extracting their process information automatically. Then a back propagation (BP) neural network is used to mine the experience of NC programming in historical cases. Sparrow search algorithm (SSA) and other methods are used to optimize BP neural network’s initial weight and threshold, learning rate, and feedback process. A CAM parameter decision model based on BP neural network is constructed. Taking the connecting rod of a certain type of diesel engine as the experimental object, the method increases programming efficiency by 47%, with an accuracy rate of 80%. It is proved that the intelligent decision-making method proposed in this paper has practicability and feasibility.

Three-Dimensional Point Cloud Task-Specific Uncertainty Assessment Based on ISO 15530-3 and ISO 15530-4 Technical Specifications and Model-Based Definition Strategy

Data-driven manufacturing in Industry 4.0 demands digital metrology not only to drive the in-process quality assurance of manufactured products but also to supply reliable data to constantly adjust the manufacturing process parameters for zero-defect manufacturing processes. Better quality, improved productivity, and increased flexibility of manufacturing processes are obtained by combining intelligent production systems and advanced information technologies where in-process metrology plays a significant role. While traditional coordinate measurement machines offer strengths in performance, accuracy, and precision, they are not the most appropriate in-process measurement solutions when fast, non-contact and fully automated metrology is needed. In this way, non-contact optical 3D metrology tackles these limitations and offers some additional key advantages to deploying fully integrated 3D metrology capability to collect reliable data for their use in intelligent decision-making. However, the full adoption of 3D optical metrology in the manufacturing process depends on the establishment of metrological traceability. Thus, this article presents a practical approach to the task-specific uncertainty assessment realisation of a dense point cloud data type of measurement. Finally, it introduces an experimental exercise in which data-driven 3D point cloud automatic data acquisition and evaluation are performed through a model-based definition measurement strategy.

Managing Engineering Change within the Paradigm of Product Lifecycle Management

Managing change in organizations is a laborious task that consumes value added time in various segments of the product lifecycle including design and development, production, delivery, and product disposition. Product lifecycle management plays an important role in minimizing the time required for managing engineering changes. This research aims to perform an extensive survey of the literature in this area. There is no consolidated review available in this area summarizing advances in engineering change management vis-à-vis product lifecycle management. Thus, the paper gives an overview of product lifecycle management-based thinking and change management. This review puts forward the most relevant research regarding the practices and frameworks developed for managing engineering change in an organization. These include model-based definition, digital twin, process-based semantic approach, service-oriented architecture, Unified Modeling Language, and unified feature modeling. The gaps between the extents of conformance to success factors have been identified as extent of integration, standardization, versatility of application, support of existing systems, and the extent of product lifecycle management support.

Research on 3D Digital Technological Process of Construction Products with Model-Based Definition

To guide the site staff to produce complex construction products with high quality, this paper proposes a three-dimensional digital technological process method for construction products. Firstly, the process information of construction products is processed from the perspective of quality control and quality management. Then, the geometric entities in the product construction process are modeled based on the building information model (BIM). Then, the process information is expressed and integrated into geometric entities by the model-based definition (MBD) technology, and the interactive files guiding product construction are output. Finally, the technological process expression example of the infill wall masonry project shows that the proposed digital technological process method can fully utilize the advantages of three-dimensional space and desktop virtual reality, help to deepen process understanding, improve product construction quality, reduce project cost, and provide new ideas for digital research in the construction industry.

A novel graph neural networks approach for 3D product model retrieval

ABSTRACT With the quick development of intelligent manufacturing technology, increasing of 3D product models data resource raises the need for product models retrieval method that can fully exploit model information, whereas the existing methods only concern geometric information with little or no manufacturing information. Model-Based Definition (MBD) 3D engineering has gained wide popularity in recent times which enables smart manufacturing by attaching product manufacturing information. Its tip orientation to the effective reuse of MBD for reducing development time and creating an efficient digital thread. In this paper, we proposed the MBD-based attribute adjacency graph neural networks (MAAGNN) with attention mechanism. Firstly, the MBD-based attribute adjacency graph (MAAG) is established to fully express the product semantic information, which includes geometric and non-geometric information of the MBD model. Then graph neural network (GNN) is employed to embed the MAAG into continuous vector spaces, and attention mechanism is introduced to adaptively recognize more important features information, which can solve the ambiguity problem caused by blend features such as chamfer. Finally, the model is trained to achieve fast and accurate retrieval of the MBD models. Experiments demonstrate its potential advantages to meet the requirements of engineering applications.

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.

Automatic machining feature recognition based on MBD and process semantics

Automatic feature recognition (AFR) bridges CAD and CAPP systems. Existing AFR methods mainly consider feature geometries, and the recognized machining features have limited process attributes for process planning. With the model-based definition (MBD) becoming more advanced in the industry, AFR methods need to be improved and upgraded. This manuscript investigates a systematic AFR method for machining parts that converts the part MBD model to a structuralized feature model (SFM) based on process semantics. At its core, process semantics is the abstraction of machining objects and methods, which represents the machining requirements of machining position, machining type, machining accuracy and other process elements. Distinguished from existing AFR methods, the method in this study employs a part MBD model as the data source and provides a new definition of machining features from a process semantic perspective. Detailed data extraction and preprocessing methods of the MBD model are furnished. To identify feature geometries, a new classification methodology of machining features for defining the stable topology structure (SAF) and semi-stable topology structure (SSF) features is presented. An improved center-subgraph method is further proposed to incorporate hint-based and rule-based methods to enhance the performance of the traditional graph method. Furthermore, the generalized GAAG has been presented with an implementation method to address the completeness of graph representation and to reduce the computational effort of graph search. In addition, to extend the feature domain, the recognition methods for high-level composite features are discussed in detail. The feature attributes were solved based on the achieved feature subgraph, and the extraction methods were detailed according to process planning requirements. The feature recognition results can be directly applied for downstream machining process planning.

What is democratic backsliding?

What is democratic backsliding?

Design rationale representation approach based on intent-driven ontology and MBD model in design change

Aiming at the problem that the design rationale (DR) in design change has a wide range of sources and various forms, which makes designers difficult to reuse them, this paper proposes a DR representation approach for design change. Firstly, the overall three-layer structure of the approach is introduced; Use OWL to express the concepts related to the designer and product model in design change, and use SWRL and SQWRL to mine and extract useful DR information; A DR representation tool is developed based on CAD software to export the low semantic level information to the ontology as well as import the high semantic level information back to the Model Based Definition (MBD) model. The approach combines the expressing ability of ontology and visuality of 3D annotation to capture, represent and reuse DR information, which promotes the design change under the MBD environment. Finally, a stamping part is taken as an example, and designers can refer to the experience of the predecessors related to the changed parameter through using the tool.