Data-driven cost estimation for additive manufacturing in cybermanufacturing

Chan, Siu Lai; Lu, Yanglong; Wang, Yan

doi:10.1016/j.jmsy.2017.12.001

Cited by 114 publications

(41 citation statements)

References 61 publications

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“…Many researchers have explained the phenomena of smart manufacturing, or industry 4.0 technologies, in terms of an augmented and virtual reality ( Wu et al., 2013 ; Rüßmann et al., 2015 ; Kolberg and Zühlke, 2015 ), additive manufacturing ( Huang et al., 2013 ; Chan et al., 2018 ), internet of things ( Wu et al., 2017 ), big data analytics ( De Mauro et al., 2015 ; Addo-Tenkorang and Helo, 2016 ; Lenz et al.,2018 ), and cyber-physical systems ( Monostori, 2014 ; Lee et al., 2015 ; Zhong and Nof, 2015 ). Wu et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Multistage implementation framework for smart supply chain management under industry 4.0

Shao

Liu

et al. 2021

Technological Forecasting and Social Change

147

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Section: Literature Reviewmentioning

confidence: 99%

Multistage implementation framework for smart supply chain management under industry 4.0

Shao

Liu

et al. 2021

Technological Forecasting and Social Change

147

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show abstract

“…These advantages encourage tool sharing to decrease the time and cost in product realization. Chan et al [105] developed a novel cost assessment framework according to big data analytics tools being able to estimate the production cost based on a new job, similar to ones in the past. This framework can be implemented in AM processes, where the similarities of processes and parts are established by recognizing related features.…”

Section: Advanced Additive Manufacturing Processesmentioning

confidence: 99%

The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

et al. 2019

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Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations.

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“…A data-driven predictive planning system for production was proposed to demonstrate its superiority in terms of prediction of energy consumption and machining time based on data from physical and virtual shop floors [91]. In addition, for estimating the costs of additive manufacturing, a framework based on BDA was demonstrated with errors between 0.93% and 6.51% [92]. Further, a framework based on Bayesian inference and Gibbs sampling, using the data of 20 lots of 500 wafers in a semiconductor manufacturing line with 100 process stages, revealed potential critical factors influencing the yield in an improved manner than domain knowledge alone used [40].…”

Section: Systematic Review and Inter-disciplinarymentioning

confidence: 99%

A Product/Service System Design Schema: Application to Big Data Analytics

Sakao

Neramballi

2020

Sustainability

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The challenge of environmental sustainability has required product/service systems (PSSs) to play a substantial role. New technologies such as big data analytics (BDA), which have high potential to improve or enable PSSs, are increasingly implemented in industry. However, research achieved in the past and research opportunities in the intersection of PSS design and BDA are unclear in the literature. Therefore, this article took an inter-disciplinary approach and aimed to pave the way forward for research and development in PSS design and show opportunities to improve PSS design and delivery using BDA. The research methods adopted were literature synthesis and systematic literature review. The synthesis of PSS design literature resulted in a schema consisting of 10 design steps for PSS conceptual design. The systematic review of BDA literature found 11 research works, including industrial applications, which were then mapped on to the PSS design schema. This revealed the achievement of applied research using BDA for some of the PSS design steps as well as opportunities of research for the others. The two inter-related areas of research, PSS design and BDA, were connected with each other more clearly, so that further research could be anchored and motivated with more specificity.

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Data-driven cost estimation for additive manufacturing in cybermanufacturing

Cited by 114 publications

References 61 publications

Multistage implementation framework for smart supply chain management under industry 4.0

Multistage implementation framework for smart supply chain management under industry 4.0

The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

A Product/Service System Design Schema: Application to Big Data Analytics

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