Attention-SP-LSTM-FIG: An explainable neural network model for productivity prediction in aircraft final assembly lines

The use of machine learning models for productivity prediction in complex manufacturing systems has garnered significant attention. However, the implementation of conventional black-box machine learning models is challenging due to the complexity and limited data availability in aircraft final assembly lines. These challenges stem from a lack of explainability, leading to compromised accuracy and diminished trustworthiness. To address this issue, this paper proposes an explainable neural network model, the Attention-SP-LSTM-FIG, specifically designed for productivity prediction in aircraft final assembly lines. First, the serial-parallel LSTM is utilized to map the process sequence within each assembly station, and a customized attention mechanism maps the requirements of workers and materials. This process results in independent sub-models corresponding to each station. Subsequently, the outputs from each sub-model are combined using a specially-designed final integrated gate to produce the final output. Finally, a post-hoc analysis of the weight data in the model is performed following the backpropagation order to identify the various production bottlenecks in the assembly line. The performance of the proposed model has been evaluated through an industrial case study. In terms of accuracy, the Attention-SP-LSTM-FIG surpasses other benchmark neural network models in terms of error, correlation, and precision metrics. Regarding explainability, the Attention-SP-LSTM-FIG can precisely identify bottlenecks for practitioners, thus facilitating a better understanding of the rationale behind the model's outputs.