Similarity Comparison of Mechanical Parts

Hong, Taesik; Lee, Kunwoo; Kim, Sung‐Chan; Chu, Chong-Nam; Lee, Hyunchan

doi:10.1080/16864360.2005.10738339

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…In contrast, the literature in the area of feature-based comparison for the purpose of similarity assessment [14][15][16] or similar model retrieval is quite rich. Similarity and difference are two sides of the same coin; however, there is a delicate point to be highlighted.…”

Section: Feature-based Comparisonmentioning

confidence: 99%

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Jiřík¹,

DiCarlo²,

Liska³

et al. 2020

CADandA

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A semantic model difference identification (MDI) solution can be defined as an MDI solution that identifies and represents the differences between two compared models in terms of meaningful engineering information. Semantic representation of the differences between 3D CAD models is especially challenging due to the variety of modeling solutions used across industries, the non-uniqueness of modeling sequences and the use of low-level information, e.g. B-rep STEP files, in engineering communications. This work proposes an MDI method that represents the differences between 3D CAD models based on engineering semantics through features. Brake-and hydro-formed aerospace sheet metal parts are used as the application domain in which to propose and illustrate the proposed method. This method consists mainly of a pose registration stage and a difference identification stage. The pose registration method considers that all the features of a part serve specific functions, some of which are fundamental to the part's essential functionality, and that they are intertwined with the design intent of the part, which is particularly true for aerospace sheet metal parts. This provides the opportunity to semantically register feature-based 3D CAD models according to the unique purpose of the features in this specific domain of application. Difference identification is approached by primarily identifying and segregating the commonality between the compared 3D CAD models and then identifying the differences. The differences between 3D CAD models are classified as added, removed or differed features. Differed features are those features that are of the same type, but whose definition varies. As an outcome, the proposed method describes a way to fully pose-register 3D CAD models and identify their differences semantically based solely on their features, and, by extension, their design intent.

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Section: Feature-based Comparisonmentioning

confidence: 99%

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Jiřík¹,

DiCarlo²,

Liska³

et al. 2020

CADandA

View full text Add to dashboard Cite

show abstract

“…The assessment of similarity in 3D mechanical parts has garnered considerable attention. In a study by Hong et al [2], previous research on shape similarity techniques was categorized into two groups. The first category focuses solely on geometric similarity, while the second compares predefined typical features.…”

Section: Comparison Tools In the Literaturementioning

confidence: 99%

“…There are several methods for comparing CAD models. The following are some of the different methods that may be used [2]: Geometric comparison is the most important one. This technique consists of comparing the geometry of CAD models [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Automated Assessment Tool for 3D Computer-Aided Design Models

Eltaief,

Ben Amor,

Louhichi

et al. 2024

Applied Sciences

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Computer-aided design (CAD) has become an integral part of engineering education, particularly for those studying mechanical engineering. By providing practical skills that are highly valued in the engineering industry, proficiency in CAD systems enhances students’ employability. Generally, CAD systems provide students with the tools and knowledge necessary to excel in their engineering education and future careers. In order to help teachers to give the best training to their students and to make the right evaluations, an automatized tool is needed to support the evaluation of CAD models during training sessions. After an extensive bibliographical search, this paper proposes a CAD Model Automatized Assessment (MAA) Tool for mechanical courses called the CAD MAA Tool. This tool is mainly based on a developed model that takes into account different aspects of modeling, such as geometric, feature-based, and parametric modeling. To correctly evaluate a given part compared to a reference one, the proposed model uses different coefficients fixed by the teacher according to their teaching strategies or course objectives.

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“…The grouping algorithm is an algorithm that groups objects that are similar in shape or size, and it is developed on the basis of a total shape-based comparative methodology to find similar shapes and sizes [9]. The grouping algorithm sets a bounding box around a part, and divides the box into 1000 smaller cells, and creates points in cells where the part's edge falls in.…”

Section: The Grouping Algorithmmentioning

confidence: 99%

Intelligent 3D packing using a grouping algorithm for automotive container engineering

Joung

Noh

2014

Journal of Computational Design and Engineering

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Storing, and the loading and unloading of materials at production sites in the manufacturing sector for mass production is a critical problem that affects various aspects: the layout of the factory, line-side space, logistics, workers' work paths and ease of work, automatic procurement of components, and transfer and supply. Traditionally, the nesting problem has been an issue to improve the efficiency of raw materials; further, research into mainly 2D optimization has progressed. Also, recently, research into the expanded usage of 3D models to implement packing optimization has been actively carried out. Nevertheless, packing algorithms using 3D models are not widely used in practice, due to the large decrease in efficiency, owing to the complexity and excessive computational time. In this paper, the problem of efficiently loading and unloading freeform 3D objects into a given container has been solved, by considering the 3D form, ease of loading and unloading, and packing density. For this reason, a Group Packing Approach for workers has been developed, by using analyzed truck packing work patterns and Group Technology, which is to enhance the efficiency of storage in the manufacturing sector. Also, an algorithm for 3D packing has been developed, and implemented in a commercial 3D CAD modeling system. The 3D packing method consists of a grouping algorithm, a sequencing algorithm, an orientating algorithm, and a loading algorithm. These algorithms concern the respective aspects: the packing order, orientation decisions of parts, collision checking among parts and processing, position decisions of parts, efficiency verification, and loading and unloading simulation. Storage optimization and examination of the ease of loading and unloading are possible, and various kinds of engineering analysis, such as work performance analysis, are facilitated through the intelligent 3D packing method developed in this paper, by using the results of the 3D model.

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Similarity Comparison of Mechanical Parts

Cited by 7 publications

References 12 publications

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Algebraic Filtering of Surfaces from 3D Medical Images with Julia

Automated Assessment Tool for 3D Computer-Aided Design Models

Intelligent 3D packing using a grouping algorithm for automotive container engineering

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