# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 6 Conclusions Trends  
This paper identifies the main common obstacles of industrial AI for smart manufacturing as well as their respective solutions. The identified challenges are data quality, data secrecy and model reliability. Several possible solutions from transfer learning to DNN encryption and uncertainty estimation have been presented and summarized, alongside their smart manufacturing applications. Table 4 lists the applications using these algorithms in smart manufacturing. Part of these algorithms are at the infancy stage and have not been applied in industry yet, although many of them are currently in the spotlight, such as training permutation and out-of-distribution detection.  
With increasingly deeper research on the solutions for industry 4.0, more problems with the AI methods in smart manufacturing will emerge and be identified. We can foresee some of them here for discussion. The obstacles discussed in this paper usually can not appear individually, which adds complexity to the solutions. For instance, domain adaption is usually applied altogether with semi-supervised learning since data labeling in practical run time can sometimes be impossible [153]. The algorithms in Section 3 have not paid much attention to data secrecy, such as privacy-preserving transfer learning [154]. These issues can be obstacles to building robust industrial deep learning applications that have not been addressed yet.  
Furthermore, related to the three challenges discussed in this paper, data sharing [155; 156] and adaptive AI [157] have been proposed in the last two years. Data sharing is a straightforward solution for data scarcity in that the entities can directly learn from the data from their peers in the community. According to [155], more than 70% of global data and analytics decision-makers are expanding their ability to use external data. However, as pointed out in [156], the data sharing in industry encounters the challenges of data secrecy, lack of common ontology, and platforms and services. Data secrecy can be stressed with the approaches introduced in Section 4 so that confidential business information can not be derived externally. A common data ontology aims to ensure data from all entities in the community can be interpreted and merged with each other. Internal and external hardware facilities, such as edge, fog and cloud computing, are needed for collecting, storing and sharing data [158].  
As discussed in Sections 3.5 and 5, data can evolve with time so that retraining is necessary to secure the ML model performance. Adaptive AI enables the model to be self-updated to meet the evolving of datasets. From our point of view, following technical problems should be addressed for adaptive AI: (1) how to improve retraining accuracy and speed up retraining; (2) how to schedule manufacturing to well maintain retraining and normal inference; (3) which hardware architecture is suitable for adaptive AI? For (1), deep continual learning can still not achieve comparable accuracy with vanilla retraining when history data and retraining time are limited. Methods should be proposed to balance accuracy, history data volume and retraining time so that a hybrid retraining strategy can be in action to secure the performance. For (2), one question should be considered--whether the manufacturing process should wait for the retraining or if there is a smarter way to make progress in both simultaneously; For (3), in order to maintain the optimized solution in (1) and (2), economic budgets, data secrecy and individual hardware conditions of the manufactures, computing architectures should be carefully selected.  
Last but not least, building an AI system in smart manufacturing with high reliability not only