# Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
## 4 An Application of the Framework: Use Case of Cold Rolling
### Application Use Case  
As mentioned in the introduction, cold rolling of steel is one example of an important process in steel making: it is the process of reducing the thickness of steel to produce narrow sheets that are rolled into coils. During this process, the material undergoes deformation by passing through a set of rotating work rolls [32]. The work rolls are under constant pressure during operation, and they become worn after heavy usage, requiring regular refurbishment [8]. During the refurbishment process, the worn surface is removed, decreasing the diameter of the work rolls. The quantity of diameter removed, also referred to as stock, is calculated based on the quantity of steel produced rather than the physical condition of the roll. Because of this, the work rolls are often refurbished prematurely or belatelly; hence, their efficiency and yield are suboptimal. Therefore, it is important for operators on the shop floor to halt operations at an optimal point in time in order to maximize the yield of the work rolls, while simultaneously not overworking the work rolls, which in turn results in the production of defective steel and huge additional refurbishment costs. Typically, in our study, work rolls begin their life with a diameter of 600 mm and are scrapped when approaching 520 mm. Work rolls are expected to be refurbished hundreds of times before being scrapped, which varies between each work roll. An average refurbishment on a healthy work roll removes approximately 0.2 mm of stock. Meanwhile, if a work roll is damaged or over-worn, it may result in a significantly greater stock reduction. In extreme cases, an over 10 mm stock removal may be necessary, which is a significant cut in lifespan.  
The acquisition of this knowledge involves active engagement with domain experts and stakeholders. Additionally, further interviews with domain experts are conducted to gather insights regarding the optimal timing to stop operation and refurbish the work rolls. Using this acquired knowledge, we construct a static set of expert rules designed to encapsulate these insights. This rule set is an essential component within the application, as these rules are applied to the real-time condition of the work rolls, influencing the final decision produced by the framework.  
Thus, at any given point during cold rolling operations, the real-time conditions of the work rolls can be captured as a timestamp and input into the application. During this process, the data is integrated into a knowledge graph containing historical information about the work rolls, including details such as their previous grindings and stock reduction values. Subsequently, the knowledge graph is passed through a semantic reasoner, which applies logical deduction to predict the live condition of the work rolls. This prediction is accomplished through the application of a rule-based random forest classification, as introduced in this paper. Once this classification is obtained, it is combined with the expert rule set, generating a status for the operator and offering clear advice on whether to proceed with the cold rolling operations, along with insights into the recommended tonnage. Ultimately, the decisions produced by the application are intended to assist the human in the loop with their decision-making process and can be utilized as guidance.  
In this use case, we apply the framework to the last 100 roll unit trips of our industrial partners and perform comparison with domain experts if the assistance provided is useful and accurate.