Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learning

TL;DR

This paper presents a deep learning framework using sequence-to-sequence models to automatically identify and establish causal relationships between source and knock-on errors in automotive manufacturing, improving error analysis accuracy.

Contribution

It introduces a novel approach combining sequence-to-sequence models with a new composite metric to analyze manufacturing errors and their causal links in real-world production data.

Findings

Transformer outperforms LSTM and GRU in error prediction accuracy.

71.68% of sequences contain either source or knock-on errors.

The proposed method effectively identifies causal error relationships in manufacturing.

Abstract

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.