Details
| Original language | English |
|---|---|
| Pages (from-to) | 980-985 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 81 |
| Early online date | 24 Jun 2019 |
| Publication status | Published - 2019 |
| Event | 52nd CIRP Conference on Manufacturing Systems, CMS 2019 - Ljubljana, Slovenia Duration: 12 Jun 2019 → 14 Jun 2019 |
Abstract
Nowadays, high flexibility and responsiveness towards capacity adjustments are key to successful production planning and control in manufacturing. Moreover, many companies – especially job shops – have to deal with short-term re-scheduling. This article presents an approach for knowledge-based process planning to enable an economic evaluation of re-scheduling in the manufacturing system. For that purpose, the manufacturing costs for each workpiece are calculated based on determined parameter sets and process time under consideration of potential capacity adjustments. The knowledge-based process planning is necessary to derive reliable process times for re-scheduling and cost calculating. Hence, a pre-study is carried out to define flexible machine learning algorithms for knowledge-based process planning.
Keywords
- Adaptive manufacturing, Knowledge based system, Machine learning, Scheduling
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 81, 2019, p. 980-985.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Knowledge-based process planning for economical re-scheduling in production control
AU - Denkena, Berend
AU - Dittrich, Marc André
AU - Stamm, Siebo Claas
AU - Prasanthan, Vannila
N1 - Funding Information: The authors thank the German Research Foundation (DFG) for its financial and organizational support of the Collaborative Research Center 653 “Gentelligent Components in their Lifecycle” within subproject K2 and the Collaborative Research Center 1153 “Process chain for manufacturing hybrid high performance components by Tailored Forming” within subproject B4.
PY - 2019
Y1 - 2019
N2 - Nowadays, high flexibility and responsiveness towards capacity adjustments are key to successful production planning and control in manufacturing. Moreover, many companies – especially job shops – have to deal with short-term re-scheduling. This article presents an approach for knowledge-based process planning to enable an economic evaluation of re-scheduling in the manufacturing system. For that purpose, the manufacturing costs for each workpiece are calculated based on determined parameter sets and process time under consideration of potential capacity adjustments. The knowledge-based process planning is necessary to derive reliable process times for re-scheduling and cost calculating. Hence, a pre-study is carried out to define flexible machine learning algorithms for knowledge-based process planning.
AB - Nowadays, high flexibility and responsiveness towards capacity adjustments are key to successful production planning and control in manufacturing. Moreover, many companies – especially job shops – have to deal with short-term re-scheduling. This article presents an approach for knowledge-based process planning to enable an economic evaluation of re-scheduling in the manufacturing system. For that purpose, the manufacturing costs for each workpiece are calculated based on determined parameter sets and process time under consideration of potential capacity adjustments. The knowledge-based process planning is necessary to derive reliable process times for re-scheduling and cost calculating. Hence, a pre-study is carried out to define flexible machine learning algorithms for knowledge-based process planning.
KW - Adaptive manufacturing
KW - Knowledge based system
KW - Machine learning
KW - Scheduling
UR - http://www.scopus.com/inward/record.url?scp=85068442671&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2019.03.238
DO - 10.1016/j.procir.2019.03.238
M3 - Conference article
AN - SCOPUS:85068442671
VL - 81
SP - 980
EP - 985
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 52nd CIRP Conference on Manufacturing Systems, CMS 2019
Y2 - 12 June 2019 through 14 June 2019
ER -