Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the black-box nature of DL towards model transparency.

Automated summary

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing aims to improve production throughput and reliability beyond the state-of-the-art under Industry 4.0. Despite the opportunities offered by the widespread application of deep learning (DL), challenges such as data quality and model interpretability hinder its widespread acceptance for real-world applications. Research in data curation and model interpretation aims to address these challenges by providing quality data for DL-based analysis and promoting model transparency.