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An automated approach for segmenting numerical control data with controller data for machine tools

Published

Author(s)

Laetitia Monnier, William Z. Bernstein, Sebti Foufou

Abstract

Developing a more automated industrial digital thread is vital to realize the smart manufacturing and Industry 4.0 vision. The digital thread allows for efficient sharing across product lifecycle stages. Current techniques are not robust in relating downstream data, such as manufacturing and inspection information, back to design for better decision making. In response, we presented a methodology that aligns numerical control (NC) code, a standard for representing machine tool instructions, to controller data represented in MTConnect, a standard that provides a vocabulary for generalizing execution logs from different machine tools and devices. This paper extends our previous work by automating the tool identification using a k-means clustering algorithm to refine the alignment of the data. In doing so, we compare different distance techniques to analyze the spatial-temporal registration of the two datasets, i.e., the NC code and MTConnect data. Then, we assess the efficiency of our method through an error measurement technique that expresses the quality of the alignment. Finally, we apply our 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.
Citation
ASME Journal of Computing and Information Science in Engineering

Citation

Monnier, L. , Bernstein, W. and Foufou, S. (2023), An automated approach for segmenting numerical control data with controller data for machine tools, ASME Journal of Computing and Information Science in Engineering, [online], https://doi.org/10.1115/1.4064036 (Accessed April 23, 2024)
Created November 24, 2023, Updated February 26, 2024