A heuristic approach to detect CAD assembly clusters

Bonino, Brigida; Raffaeli, Roberto; Monti, Marina; Giannini, Franca

doi:10.1016/j.procir.2021.05.105

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…In contrast, Han et al and Bonino et al perform spectral clustering for CAD assembly segmentation. The latter is defined as connected sets of parts sharing some ICED23 characteristics (Bonino et al, 2021). CAD assembly model is also described by an AAG where the node and its attribute represent respectively part assembled and attributes' information of the part, edges, their connection relationship, and assembly constraints between parts (Han et al, 2019).…”

Section: Cad Models Clusteringmentioning

confidence: 99%

Clustering of Sequential Cad Modelling Data

et al. 2023

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Automating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

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Section: Cad Models Clusteringmentioning

confidence: 99%

Clustering of Sequential Cad Modelling Data

et al. 2023

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“…The belief function Bel(A) for a set A is defined as the sum of all the masses of subsets of the set of interest according to formula (1).…”

Section: Proposed Approachmentioning

confidence: 99%

“…In the design and manufacturing process, the importance of product assembly increases when customized products are offered. The assembly process is complex, so managing it during the manufacturing and product design phases is a challenge [1]. Researchers have focused on different aspects of the assembly process in their analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Möhring et al [5] present future opportunities related to new approaches to intelligent process control in woodworking. Tools _____________ 1 Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, Poland development of in intelligent mechatronic systems is discussed by Guergov [6]. Dalvi [7] uses artificial neural network (ANN) and motion study as tools for assembly sequence optimization.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge base development for assembly planning using evidence theory.

Kutschenreiter-Praszkiewicz¹,

Matuszny²

2022

Journal of Machine Engineering

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This paper presents an approach to assembly planning in the early phase of product development. The product specification, workstation, environment, equipment and tools are not fully known in the early stage of product development. When comparing product variants at this stage there is a lack of data that affects the efficiency of the manufacturing process. It is therefore necessary to apply methods useful in processing incomplete and uncertain data. The main indicator which helps in comparing different product variants is manufacturing time standard. This papier is focused on assembly tool selection which is one of important data influenced assembly time. Based on the proposed algorithm and case study, a tool selection method using a decision tree induced from a training set with reduced uncertainty is presented.

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