An Automated Approach for Segmenting Numerical Control Data With Controller Data for Machine Tools

Abstract

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. We previously 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 bd hosted at the National Institute of Standards and Technology. Our analysis shows that dynamic time warping achieves the best point registration with the least errors compared with other alignment techniques.