Manufacturing data-driven process adaptive design method
Open accessAbstract
Previous design methods are mostly based on simulation and experiment, so product/process design has poor adaptability to the manufacturing environment. A data-driven approach can provide feedback information in manufacturing to design. Therefore, this paper researches the method of data-driven process design based on data mining. We propose a backward design pattern of ”data + knowledge + decision,” which can fully dig up the potential value of manufacturing data. Therefore we expound the steps and methods of manufacturing data mining and takes the welding process as an example to demonstrate the driving role of data mining in process adaptive design.
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Four of them [5,42,45,48] use filtering to clean noise.
“Four of them [5,42,45,48] use filtering to clean noise.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [45]
The data-cleaning passage in section 2.1 says a smoothing filter can be used to remove noise. It does not establish that this paper actually used filtering, as the citing sentence states. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFive primary studies [5,18,42,45,48] provide data cleaning techniques that address data noise.
“Five primary studies [5,18,42,45,48] provide data cleaning techniques that address data noise.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [45]
Half of the data cleaning techniques [8,14,29,42,43,45,49] in the SLR have not been evaluated.
“Half of the data cleaning techniques [8,14,29,42,43,45,49] in our SLR have not been evaluated.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [45]
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A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 68 · see citing works
- Authors
- Wei, W., Yuan, J., & Liu, A.
- Affiliations
- Beihang University (CN)
- UNSW Sydney (AU)
- Corresponding author
- Wei, Wei
- Venue
- Procedia CIRP
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 91
- Pages
- 728-734
- ISSN
- 2212-8271 (ISSN-L)
- OpenAlex
- W3075786327
- Cited by
- 8
- References
- 18
- Citations by year
- 2026: 1 · 2023: 4 · 2022: 2 · 2021: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia CIRP (publishedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (51675028)
- Aeronautical Science Foundation of China (20171651015)
- State Key Lab of Digital Manufacturing Equipment and Technology (DMETKF2017020)
- National Key Research and Development Program of China (2017YFB1104201, 2018YFB1701703)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- data-driven design, welding processes, production data, process design
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