# Machine Learning in Manufacturing towards Industry 4.0: From For Now' to 'Four-Know'
## 2 Overview of Machine Learning in Manufacturing
### Introduction of Four-Know and Four-Level  
According to the _Acatech_ Industrie 4.0 Maturity levels [16], the development towards Industry 4.0 in manufacturing can be structured into the following six successive stages: computerization, connectivity, visibility, transparency, predictive capacity, and adaptability. The first two stages, computerization and connectivity, provide the basis for digitization, while the rest are analytic capabilities required for achieving Industry 4.0. ML, as powerful data analytics tools are normally applied in the last four stages. Inspired by the _Acatech_ Industrie 4.0 Maturity levels, ML studies in manufacturing can be categorized into four subjects: Know-what, Know-why, Know-when, and Know-how, which to a degree overlap with visibility, transparency, predictive capacity, and adaptability, respectively. The Four-Know definitions are presented below:  
* _Know-what_ deals with understanding of the current states of machines, processes, or production systems, which can help in rapid decision-making. It should be noted that Know-what goes beyond visualization of real-time data. Instead, data should be processed, analyzed, and distilled into information which enables decision-making. For instance, typical examples of Know-what in manufacturing are defect detection in quality control [17,18], fault detection in process/machine monitoring [19,20], and soft sensor modelling [21,22].
* _Know-why_, based on the information from Know-what, aims to identify inner patterns from historical data, thereby discovering the reasons for a thing happening. Know-why includes the identification of interactions among different variables [23] and the discovery of cause-effect relationship between an event and other variables [24,25]. On one hand, Know-why can indicate most important factors for understanding Know-what. On the other hand, Know-why is the prerequisite for Know-when, as the reliability of predictions is heavily dependent upon the quality of casual inference.
* _Know-when_, built on Know-why, involves timely predictions of events or prediction of key variables based on historical data, allowing the decision-maker can take actions at early stages. For instance, Know-when in manufacturing includes quality prediction based on relevant variables [26,27], predictive maintenance via detection of incipient anomalies before break-down [28,29], and predicting Remaining Useful Life (RUL) [30,31].
* _Know-how_, on the foundation of Know-when, can recommend decisions that help adapt to expected disturbance and can aid in self-optimization. Examples in manufacturing include prediction-based process control [27,32], scheduling of predictive maintenance tasks [33,34], dynamic scheduling in the flexible production [35,36], and inventory control [34].  
The aim of applying ML in manufacturing is to achieve production optimization across four different levels: product, process, machine, and system. Therefore, the use cases for applying ML can be further categorized by these different levels, as shown in Figure 1 and Table 1, which answer RQ1 in terms of ML typical use cases.