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Bibb, Richard; Taha, Zahari; Brown, Robert; Wright, David K.

doi:10.1023/a:1008920512663

Cited by 31 publications

(8 citation statements)

References 4 publications

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“…These tools offer different stages of automation. Some require the user to input details regarding the part geometry [34,35]. However, most software-based approaches use automated analyses to retrieve the required part parameters.…”

Section: Dfam Design Analysismentioning

confidence: 99%

Evaluation of design support tools for additive manufacturing and conceptualisation of an integrated knowledge management framework

Ellsel¹,

Werner²,

Göpfert³

et al. 2021

DS 111: Proceedings of the 32nd Symposium Design for X

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Over time, additive manufacturing (AM) has gained more and more adoption into series production. Software tools are developed to assist designers to incorporate specific design for additive manufacturing (DfAM) guidelines into their product development processes. In this paper, an overview of already existing DfAM functionality is given by a review of scientific publications and commercial software products. Additionally, interviews with members of development teams which design AM parts are carried out to assess specific workflows and problems in the current design process. In both investigations, the lack of an integrated and automated software solution emerged. Based on these results, a structure for an integrated design support toolchain is proposed.

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Section: Dfam Design Analysismentioning

confidence: 99%

Evaluation of design support tools for additive manufacturing and conceptualisation of an integrated knowledge management framework

Ellsel¹,

Werner²,

Göpfert³

et al. 2021

DS 111: Proceedings of the 32nd Symposium Design for X

View full text Add to dashboard Cite

show abstract

“…It consists of a knowledge model and an inference system. Certain knowledge can be represented by rule-based models [23], case-based models, and ontology [24]. Uncertain knowledge can be represented by Bayesian networks [25] and fuzzy logic [26].…”

Section: Typical Ai Modelsmentioning

confidence: 99%

Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives

Wang

Zheng

Peng

et al. 2020

Sci. China Technol. Sci.

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Additive manufacturing (AM) has been increasingly used in production. Because of its rapid growth, the efficiency and robustness of AM-based product development processes should be improved. Artificial intelligence (AI) is a powerful tool that has outperformed humans in numerous complex tasks. AI-enabled intelligent agents can reduce the workforce required to scale up AM production and achieve higher resource utilization efficiency. This study provides an introduction of AI techniques. Then, the current development of AI-enabled AM product development is investigated. Existing intelligent agents are used for problems in product design, process design and production stages. Based on the review, current research gaps and future research directions are identified. To guide future development of more efficient and comprehensive intelligent agents, a smart AM framework based on cloud-edge computing is proposed. Global consideration can be realized in the cloud environment, and a fast response can be achieved at the edge nodes. additive manufacturing, artificial intelligence, product development, cloud-edge computing

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“…One of the first tools for AM process selection was developed by Bibb et al (1999), focusing specifically on AM as a means of rapid prototyping. This knowledge based tool provided guidance based on data on part geometry, process capability, production volume and the physical properties of the components.…”

Section: Approaches For Selecting Between Am Materials and Processesmentioning

confidence: 99%

“…Despite material and process selection for AM receiving attention since the late 1990s (Bibb et al 1999), only a limited number of approaches have been proposed to support the selection process between conventional materials and processes and AM materials and processes. Different studies have highlighted how AM can be, in some circumstances, the most suitable manufacturing route (Atzeni et al 2010;Sonova 2017).…”

Section: Am In Generic Materials and Process Selectionmentioning

confidence: 99%

A framework for mapping design for additive manufacturing knowledge for industrial and product design

Pradel

Zhu

Bibb

et al. 2018

Journal of Engineering Design

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Design for Additive Manufacturing (DfAM) is a growing field of enquiry. Over the past few years, the scientific community has begun to explore this topic to provide a basis for supporting professional design practice. However, current knowledge is still largely fragmented, difficult to access and inconsistent in language and presentation. This paper seeks to collate and organise this dispersed but growing body of knowledge, using a single and coherent conceptual framework. The framework is based on a generic design process model and consists of five parts: Conceptual design, Embodiment design, Detail design and Process planning and Process selection. 81 articles on DfAM are mapped onto the framework to provide, for the first time, a clear summary of the state of the art across the whole design process. Nine directions for the future of DfAM research are then proposed.

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Untitled

Cited by 31 publications

References 4 publications

Evaluation of design support tools for additive manufacturing and conceptualisation of an integrated knowledge management framework

Evaluation of design support tools for additive manufacturing and conceptualisation of an integrated knowledge management framework

Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives

A framework for mapping design for additive manufacturing knowledge for industrial and product design

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