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The purpose of this paper is to focus on the characterization and classification of parts with respect to the meshing issue, and notably the meshing of thin parts difficulty handled automatically and which often requires adaptation steps. The objective is to distinguish the so-called thin parts and parts with thin features from the other parts.

The concepts of thin part and part with thin features are introduced together with the mechanisms and criteria used for their identification in a CAD models database. The criteria are built on top of a set of shape descriptors and notably the distance distribution which is used to characterize the thickness of the object. To speed up the identification process, shape descriptors are computed from tessellated parts.

A complete modular approach has been designed. It computes shape descriptors over parts stored in a directory and it uses criteria to distinguish three categories: thin parts, parts with thin features and other parts. Being the three categories identified, the user can spend more time on the parts that are considered as more difficulty meshable.

The approach is limited to the three above mentioned categories. However, it has been designed so that the values corresponding to the shape descriptors and associated meshing qualities can easily be inserted within a machining learning tool later on.

The use of the developed tool can be seen as a pre-processing step during the preparation of finite element (FE) simulation models. It is automatic and can be run in batch and in parallel.

The approach is modular, it is simple and easy to implement. Categories are built on top of several shape descriptors and not on a unique signature. It is independent of the CAD modeler. This approach is integrated within a FE simulation model preparation framework and help engineers anticipating difficulties when meshing CAD models.

This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.

The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.

The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.

The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.

This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

The purpose of this study is the use of 3D printing technology to perform maintenance on damaged parts on site. To maintain damaged parts, the user needs experience in the parts design and 3D printing technology. To help users who have little or no experience on 3D printing, a part library-based information retrieval and inspection framework was proposed to support the process of manufacturing replaceable parts using a 3D printer.

To establish the framework, 3D printing-based maintenance procedure was first defined, comprising retrieval, manufacturing and inspection steps, while identifying the technical components required to perform the procedure. Once the technical components are identified, part library-based information retrieval and inspection framework was defined based on the technical components and the relationships between the components. For validation of the concept of the framework, prototype system is developed according to the proposed framework.

The feasibility of the proposed framework is proved through maintenance experiments on gaskets and O-rings.

The main contribution of this study is the proposal of the framework, which aims to support the maintenance of damaged parts for the user who has little or no experience in part design or does not know how to operate a 3D printer.

Three-dimensional computer-aided design (CAD) assembly model has become important resource for design reuse in enterprises, which implicates plenty of design intent, assembly intent, design experience knowledge and functional structures. To acquire quickly CAD assembly models associated with specific functions by using product function requirement information in the product conceptual design phase for model reuse, this paper aims to find an approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model before functional semantic-based assembly retrieval.

An approach for structure-function correlations analysis and functional semantic annotation of CAD assembly model is proposed. First, the product knowledge model is constructed based on ontology including design knowledge and function knowledge. Then, CAD assembly model is represented by part attributed adjacency graph and partitioned into multiple functional regions. Assembly region and flow-activity region are defined for structure-function correlations analysis of CAD assembly model. Meanwhile, the extraction process of assembly region and flow-activity region is given in detail. Furthermore, structure-function correlations analysis and functional semantic annotation are achieved by considering comprehensively assembly structure and assembled part shape structure in CAD assembly model. After that, a structure-function relation model is established based on polychromatic sets for expressing explicitly and formally relationships between functional structures, assembled parts and functional semantics.

The correlation between structure and function is analyzed effectively, and functional semantics corresponding to structures in CAD assembly model are labeled. Additionally, the relationships between functional structures, assembled parts and functional semantics can be described explicitly and formally.

The approach can be used to help designers accomplish functional semantic annotation of CAD assembly models in model repository, which provides support for functional semantic-based CAD assembly retrieval in the product conceptual design phase. These assembly models can be reused for product structure design and assembly process design.

The paper proposes a novel approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model. Functional structures in assembly model are extracted and analyzed from the point of view of assembly structure and function part structure. Furthermore, the correlation relation between structures, assembled parts and functional semantics is expressed explicitly and formally based on polychromatic sets.

This paper aims to present the classification, representation and extraction of adhesively bonded assembly features (ABAFs) from the computer-aided design (CAD) model.

The ABAFs are represented as a set of faces with a characteristic arrangement among the faces among parts in proximity suitable for adhesive bonding. The characteristics combination of the faying surfaces and their topological relationships help in classification of ABAFs. The ABAFs are classified into elementary and compound types based on the number of assembly features exist at the joint location.

A set of algorithms is developed to extract and identify the ABAFs from CAD model. Typical automotive and aerospace CAD assembly models have been used to illustrate and validate the proposed approach.

New classification and extraction methods for ABAFs are proposed, which are useful for variant design.

As most existing computer‐aided design systems do not provide part feature information which is essential for process planning, automatic part feature recognition systems serve as an important link between Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). Attempts to provide a structural framework for understanding various issues related to part feature recognition. Reviews previous work in the field of part feature recognition and classifies known feature recognition systems for the sake of updating information and future research. Briefly introduces about 12 systems. Studies 31 systems and lists them in the Appendix based on 60 references. Comments on future research directions.

In order to present a significant usage of the computer-aided design (CAD)/computer-aided manufacturing (CAM) systems in the apparel and textile industry, the current literature has been observed. Although the CAD/CAM systems have also been increasingly applied to all fields apparel and textile manufacturing for the last few decades, improving the precision, productivity and the organization of the information flow, they have not been fully utilized in these industrial fields. The paper aims to discuss these issues.

The paper is structured in three main sections showing the vast applicability of the CAD/CAM systems, the benefits provided by them and the future trend in their development.

Although the initial development of the CAD/CAM systems strived to completely eliminate manual and time-consuming operations, they have not been accepted in practice due to their inflexibility at making changes and the time needed for regenerating a complex parametric model. The textile and apparel industries show slow progress in acquiring the CAD/CAM systems.

This CAD/CAM technology enabled the customization in the design process according to individual needs and directed the textile and the apparel industry to moving into new directions such as the mass customization to personalization. The paper makes clear that although this technological concept is rather old, the use of the CAD/CAM systems will inevitably broaden in terms of applicability to new production stages.

For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination.

The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined.

A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency.

The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

The 4D CAD model has been accepted for better conceptualizing and comprehending the sequences and spatial constraints in a construction schedule. The purpose of this paper is to identify the deficiencies of the visualization of the 4D CAD model and to propose improvements.

The presentation abilities of the existing 4D CAD model are analyzed and compared with the other conventional methods, namely Gantt chart, network diagram, and the calendar. Four aspects of the visualization are addressed, namely the overview of a schedule, the duration of an activity, the relationship of an activity, and the project progress tracking. The proposed improvements employed different visual properties of 3D CAD objects such as color, line weight, and line type to represent the different activities' performing statuses. A prototype of the 4D CAD model with enhanced visualization was developed on a construction project case.

The model evaluation showed that this development could enhance the visualization of the 4D CAD model and provide a more informative construction schedule.

It is anticipated that the 4D CAD model with these enhancements can substitute for conventional presentation methods of construction schedules.