# Machine Learning in Manufacturing towards Industry 4.0: From For Now' to 'Four-Know'
## 5 Challenges and Future Directions  
A large number of ML use cases have shown the great potential for addressing complex manufacturing problems, from knowing what is happening to knowing how employ self-adapting or self-optimizing systems. The data-driven mechanisms in ML enable broader applications in different fields as well as at different levels, from individual products to whole systems. However, in spite of the great potential and advantages offered by ML and numerous off-the-shelf ML models, there are critical challenges to overcome before the successful application of ML in manufacturing can be realized. The following demonstrate typical challenges that manufacturing industries might confront during the application and deployment of ML-based solutions, along with corresponding future directions for tackling these challenges (RQ3). * _Lack of data._ Preparing the data used for ML is not a simple task, as the scale and the quality of data can greatly affect the performance of ML models. The most common challenge involves preparing a large amount of organized input data, and ensuring high-quality labels if labels are needed. Despite manufacturing data becoming increasingly more accessible due to the development of sensors and the Internet of Things, gathering meaningful data is time-consuming and costly in many cases, for example, fault detection and RUL prediction. This issue might be alleviated by the Synthetic Minority Over-sampling Technique (SMOTE) [162]. However, SMOTE cannot capture complex representative data, as it often relies on interpolation [163]. Data augmentation [139; 164] or transfer learning [165] may address this problem. The aim of data augmentation is to enlarge dataset by means of transforming data [139], by transforming both data and labels, as with MixUp [166], or by generating synthetic data using generative models [167; 168]. On the contrary, instead of focusing on expanding  
Figure 15: Overview of the Reinforcement Learning approach based on [158]. data, transfer learning aims to leverage knowledge from similar external datasets. A typically used method in transfer learning is parameter transfer, where a pretrained model from a similar dataset is employed for initialization [165]. Another situation involving lack of data is that certain data cannot be shared due to data privacy and security issues. In confronting this problem, Federated Learning (FL) [169] might be a potential opportunity to enable model training across multiple decentralized devices while holding local data privately. * _Limited computing resources._ The high performance of ML models always comes with high computational complexity. In particular, obtaining high accuracy with a neural network requires on millions or even billions of parameters [170]. However, limited computing resources in industries makes it a challenge to deploy heavy ML models in real-time industrial environments. Possible approaches include model compression via pruning and sharing of model parameters [171] and knowledge distillation [172]. Parameter pruning aims to reduce the number of model parameters by removing redundant parameters without any effect on model performance. By contrast, seeking the same goal, knowledge distillation focuses on distilling knowledge from a cumbersome neural network to a lightweight network to allow it to be deployed more easily with limited computing resources. * _Changing circumstances._ Most ML applications in manufacturing focus only on model development and verification in off-line environments. However, when deploying these models in running production, their performance may be degraded due to changing circumstances, leading to changes in data distribution, that is, drift [173,174]. Therefore, manual model adjustment over time, which is time-consuming, is usually unavoidable [175]. However, this could be addressed in the future by automatic model adaption [174], in which data drifts are automatically detected and handled with less resources.