TY - GEN
T1 - A proposed mapping method for aligning machine execution data to numerical control code
AU - Monnier, Laetitia
AU - Bemstein, William Z.
AU - Foufou, Sebti
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - The visions of the digital thread and smart manufacturing have boosted the potential of relating downstream data to upstream decisions in design. However, to date, the tools and methods to robustly map across the related data representations is significantly lacking. In response, we propose a mapping technique for standard manufacturing data representations. Specifically, we focus on relating controller data from machining tools in the form of MTConnect, an emerging standard that defines the vocabulary and semantics as well as communications protocols for execution data, and G-Code, the most widely used standard for numerical control (NC) instructions. We evaluate the efficacy of our mapping methodology through an error measurement technique that judges the alignment quality between the two data representations. We then relate the proposed methodology to a case study, that includes verified process plans and real execution data, derived from the Smart Manufacturing Systems Test Bed hosted at the National Institute of Standards and Technology.
AB - The visions of the digital thread and smart manufacturing have boosted the potential of relating downstream data to upstream decisions in design. However, to date, the tools and methods to robustly map across the related data representations is significantly lacking. In response, we propose a mapping technique for standard manufacturing data representations. Specifically, we focus on relating controller data from machining tools in the form of MTConnect, an emerging standard that defines the vocabulary and semantics as well as communications protocols for execution data, and G-Code, the most widely used standard for numerical control (NC) instructions. We evaluate the efficacy of our mapping methodology through an error measurement technique that judges the alignment quality between the two data representations. We then relate the proposed methodology to a case study, that includes verified process plans and real execution data, derived from the Smart Manufacturing Systems Test Bed hosted at the National Institute of Standards and Technology.
KW - Data Mapping
KW - Design Decisions
KW - MTConnect
KW - Product Lifecycle Data
KW - Smart Manufacturing
KW - Standards
UR - http://www.scopus.com/inward/record.url?scp=85072966284&partnerID=8YFLogxK
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U2 - 10.1109/COASE.2019.8842832
DO - 10.1109/COASE.2019.8842832
M3 - Conference contribution
AN - SCOPUS:85072966284
T3 - IEEE International Conference on Automation Science and Engineering
SP - 66
EP - 72
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Y2 - 22 August 2019 through 26 August 2019
ER -