TY - JOUR

T1 - Identifying cause-and-effect relationships of manufacturing errors using sequence-to-sequence learning

AU - Reimer, Jeff

AU - Wang, Yandong

AU - Laridi, Sofiane

AU - Urdich, Juergen

AU - Wilmsmeier, Sören

AU - Palmer, Gregory

N1 - Funding Information: The authors gratefully acknowledge, that the proposed research is a result of the research project “IIP-Ecosphere”, granted by the German Federal Ministry for Economics and Climate Action (BMWK) via funding code 01MK20006A.

PY - 2022/12/25

Y1 - 2022/12/25

N2 - In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and is processed according to the order requirements. The timely completion of orders depends on the individual station-based operations concluding within their scheduled cycle times. If an error occurs in one station, it can have a knock-on effect, resulting in delays on the downstream stations. To the best of our knowledge, there exist no methods for automatically distinguishing between source and knock-on errors in this setting, as well as establishing a causal relation between them. Utilizing real-time information about conditions collected by a production data acquisition system, we propose a novel vehicle manufacturing analysis system, which uses deep learning to establish a link between source and knock-on errors. We benchmark three sequence-to-sequence models, and introduce a novel composite time-weighted action metric for evaluating models in this context. We evaluate our framework on a real-world car production dataset recorded by Volkswagen Commercial Vehicles. Surprisingly we find that 71.68% of sequences contain either a source or knock-on error. With respect to seq2seq model training, we find that the Transformer demonstrates a better performance compared to LSTM and GRU in this domain, in particular when the prediction range with respect to the durations of future actions is increased.

AB - In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and is processed according to the order requirements. The timely completion of orders depends on the individual station-based operations concluding within their scheduled cycle times. If an error occurs in one station, it can have a knock-on effect, resulting in delays on the downstream stations. To the best of our knowledge, there exist no methods for automatically distinguishing between source and knock-on errors in this setting, as well as establishing a causal relation between them. Utilizing real-time information about conditions collected by a production data acquisition system, we propose a novel vehicle manufacturing analysis system, which uses deep learning to establish a link between source and knock-on errors. We benchmark three sequence-to-sequence models, and introduce a novel composite time-weighted action metric for evaluating models in this context. We evaluate our framework on a real-world car production dataset recorded by Volkswagen Commercial Vehicles. Surprisingly we find that 71.68% of sequences contain either a source or knock-on error. With respect to seq2seq model training, we find that the Transformer demonstrates a better performance compared to LSTM and GRU in this domain, in particular when the prediction range with respect to the durations of future actions is increased.

UR - http://www.scopus.com/inward/record.url?scp=85144637278&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2205.02827

DO - 10.48550/arXiv.2205.02827

M3 - Article

C2 - 36567383

AN - SCOPUS:85144637278

VL - 12

JO - Scientific reports

JF - Scientific reports

SN - 2045-2322

M1 - 22332

ER -