Development of a Design for Manufacturing Rules Database for Use in Instruction of DFM Practices

Phelan, Keith; Wilson, Crystal; Summers, Joshua D.

doi:10.1115/detc2014-34187

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The recent rapid growth of HM technologies launched an entirely new world of design freedom and manufacturability improvements. 6,8 The research works cited in Bralla, 9 Edwards, 10 Phelan et al, 11 Zhao and Shah, 12 van Vliet and van Luttervelt, 13 Campi et al, 14 Patel et al, 15 Fagade and Kazmer, 16 and Ranjan et al 17 confirm that manufacturing difficulties must be considered in design phase. In the context of computer aided process plan (CAPP), several researches contributed to the integration of the manufacturing constraints in HM.…”

Section: State Of the Artmentioning

confidence: 95%

“…9 DFM principles and rules are widely accepted in the engineering community and considers forming, subtractive machining, casting, and assembly processes. 10 Both knowledge-based [11][12][13][14] and historical-based 15,16 approaches are used in DFM tools. A web-based operational database for DFM rules was developed in Phelan et al 11 The database is built using entityrelationship model and it is adapted for use in industry.…”

Section: State Of the Artmentioning

confidence: 99%

“…10 Both knowledge-based [11][12][13][14] and historical-based 15,16 approaches are used in DFM tools. A web-based operational database for DFM rules was developed in Phelan et al 11 The database is built using entityrelationship model and it is adapted for use in industry. However, DFAM guidelines are relatively recent and currently in progress of being integrated into the design phase.…”

Section: State Of the Artmentioning

confidence: 99%

“…The volume V HM2 to be built with AM process is defined by the relation in equation (11); such as f i is the feature filling percentage and Vi the volume of finished part. 15% is the default f i value and it can be modified by user.…”

Section: Removed Volumementioning

confidence: 99%

See 3 more Smart Citations

Combined manufacturing and cost complexity scores-based process selection for hybrid manufacturing

Tlija

Al-Tamimi

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Smart manufacturing involves the use of emergent technologies and requires dynamic feedback of customer’s demands. These concerns need a rapid Decision Support System (DSS) considering emergent manufacturing processes such as Additive (AM) and Hybrid (HM) Manufacturing and tracking the product changes. This paper proposes a DSS for process selection based on manufacturing complexity and cost. The complexity parameters, deduced from design for manufacturing (DFM), design for additive manufacturing (DFAM) and design for hybrid manufacturing (DFHM) rules, are automatically extracted from computer aided design (CAD) model to follow the product changes. Cost models are defined for each manufacturing process type. In design phase, the manufacturing cost estimation allows considering the cost as a selection factor. The combined complexity based on manufacturing difficulty and cost represents a new paradigm for process selection. The case studies show the reliability of the proposed DSS and its ability to respect the company resources and strategy.

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Section: State Of the Artmentioning

confidence: 95%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Removed Volumementioning

confidence: 99%

See 2 more Smart Citations

Combined manufacturing and cost complexity scores-based process selection for hybrid manufacturing

Tlija

Al-Tamimi

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Adoption of design repositories in research and industry has been slow due to resource commitment, human curation, intuition-based knowledge extraction, and lack of well-structured product data. Efforts have been made to improve design repository generation by limiting subjectivity through taxonomy standardization [41,42,43,6,44]. Furthermore, recent approaches have been introduced to streamline data addition to design repositories [45].…”

Section: Taxonomy-based Design Repositories and Knowledge Discoverymentioning

confidence: 99%

Classifying Component Function in Product Assemblies with Graph Neural Networks

Ferrero¹,

Hassani²,

Grandi³

et al. 2021

Preprint

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Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Functionbased design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learning from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F 1 -score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems and Design-for-X consideration in function-based design.

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A Unit Cell Design Guideline Development Method for Meso-Scaled Periodic Cellular Material Structures

Fazelpour

Shankar

Summers

2019

Journal of Engineering Materials and Technology

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Much research has been conducted on effective elastic properties of meso-scaled periodic cellular material (MPCM) structures; however, there is only limited research providing guidelines on how to develop improved unit cell (UC) topologies and shapes for a given set of loading requirements and conditions. This paper presents guidelines to improve the shear flexibility of the MPCMs while maintaining the effective shear modules by changing the topology or the shape of a unit cell. The guidelines are intended to use design knowledge for helping engineers by providing recommendations at any stage of the design process. In this paper, the guidelines are developed by changing topology characteristics to achieve a desired effective property of the MPCM structure. The effects of individual members, such as side connection, transverse connection, vertical legs, and curved beams of MPCM structure, when subjected to the in-plane shear loading are investigated through conducting a set of numerical simulation on UCs with similar topology and shape characteristics. Based on the simulation results, the unit cell design guidelines are developed to provide recommendations to engineers on improving the shear flexure of MPCM during the design process. Ultimately, a unit cell design guideline development method is offered and demonstrated by developing two new design guidelines.

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Development of a Design for Manufacturing Rules Database for Use in Instruction of DFM Practices

Cited by 6 publications

References 6 publications

Combined manufacturing and cost complexity scores-based process selection for hybrid manufacturing

Combined manufacturing and cost complexity scores-based process selection for hybrid manufacturing

Classifying Component Function in Product Assemblies with Graph Neural Networks

A Unit Cell Design Guideline Development Method for Meso-Scaled Periodic Cellular Material Structures

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