# Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
## 1 Introduction  
Ontologies and knowledge graphs have become a well-established and recognised way of modelling and enriching knowledge within a particular domain. In the context of smart manufacturing, semantic technologies have become a promising solution for addressing Industry 4.0 challenges [1] and have many advantages including (1) the ability to provide a shared, machine-understandable vocabulary for data integration and exchange among components [2], (2) the capability to access and query data at a virtual level without physical data integration [3], and (3) simulating cognitive decision-making tasks through logical deductions, rules, and reasoning [4].  
Meanwhile, machine learning (ML) models such as random forest (RF) have been widely adopted in manufacturing to optimize, control, troubleshoot, and improve process operations and automatization [5]. However, these models are faced with challenges such as the lack of context-aware information within dynamic production environments and semantic interoperability [6]. Thus, the development of hybrid models that combine semantic technologies and ML has been proposed to address these challenges.  
Steel production is a particular example of a manufacturing process that requires extensive human knowledge, produces a vast amount of dynamical and static data, and where predictive analytics and maintenance are of utmost importance, usually associated with significant costs [7]. Cold rolling is one example of an important process in steelmaking, which is the process of reducing the thickness of steel strips to produce narrow sheets that are coiled. Work rolls are fundamental components of cold rolling, compressing the steel material, but they become worn after prolonged usage. They are required to be refurbished on a regular basis to remove the worn surfaces [8]. Presently, work rolls are refurbished based on the quantity of steel coils produced rather than their physical conditions. Onemotivation of this study is to develop an application to aid operators on the shop floor in critical decision-making tasks in order to optimize the yield, efficiency, and overall life of work rolls.  
In this paper, we demonstrate a hybrid model for predictive analytic purposes in the domain of steelmaking. The scheduling of the refurbishment process can then be targeted based on the condition of the rolls rather than an estimation based on the total tonnage produced. Additionally, anomalies and accidents within a steel plant such as spalling and overloads [8] can be identified and avoided pre-emptively with greater semantic interoperability.  
To achieve this goal, we propose a human-in-the-loop framework that leverages knowledge representations and reasoning mechanisms using machine learning models to provide semantically enriched classifications, which are further combined with expert knowledge to support decision-making tasks. The support provided by the framework is similar to that of an expert system in the domain of steelmaking; however, the framework itself is not exactly an expert system and serves a more significant purpose.  
The contributions of this paper are twofold: (1) we introduce the Random Forest Ontology (RFO) that captures and models a random forest at a conceptual level, which can represent and perform RF classification using rule-based reasoning and knowledge graphs, and (2) we demonstrate an iterative process to integrate external domain-expert knowledge with RF classification to provide more comprehensible decision-making assistance for the human-in-the-loop system.  
The outline of this paper is as follows. Section 2 introduces the related works exploring existing hybrid models that combine semantic reasoning with ML. Section 3 introduces the methodology of our proposed approach. In Section 4, a use case is presented, where the framework is applied to assist steel operators, and the results of the application are utilized to validate the framework in Section 5. Finally, we end with the conclusions in Section 6 and future work in Section 7.