The DOI matches its Crossref record.
Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation
Open accessAbstract
Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just "old wine in new bottles"? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense. We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).We identify the common key characteristics of CBDM.We define a requirement checklist that any idealized CBDM system should satisfy.We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.We describe an idealized CBDM application example scenario.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
Self-citationTowards the online computer-aided design of catalytic pockets
Falivene, L., Cao, Z., Petta, A., Serra, L., Poater, A., Oliva, R., Scarano, V., & Cavallo, L. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0319-5 · Cited by 1,290
Li, Y., Dai, J., & Cui, L. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107777 · Cited by 922
Babiceanu, R. F., & Seker, R. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.02.004 · Cited by 461
Web-based digital twin modeling and remote control of cyber-physical production systems
Liu, C., Jiang, P., & Jiang, W. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.101956 · Cited by 223
Reference architectures for smart manufacturing: A critical review
Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.10.006 · Cited by 219
Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect
Hu, L., Nguyen, N.-T., Tao, W., Leu, M. C., Liu, X. F., Shahriar, M. R., & Al Sunny, S. M. N. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.07.155 · Cited by 219
Digital twins and industrial internet of things: uncovering operational intelligence in Industry 4.0
Attaran, S., Attaran, M., & Celik, B. G. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100398 · Cited by 122
Current cyber-defense trends in industrial control systems
Rubio, J. E., Alcaraz, C., Roman, R., & Lopez, J. (2019) · Computers & Security · doi:10.1016/j.cose.2019.06.015 · Cited by 113
Metadata
- Authors
- Wu, D., Rosen, D. W., Wang, L., & Schaefer, D.
- Venue
- Computer-Aided Design
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 59
- Pages
- 1-14
- ISSN
- 0010-4485 (ISSN-L)
- OpenAlex
- W2046182115
- Cited by
- 614
- References
- 0
- FWCI
- 104.26
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.