# Machine Learning-Supported Designing of Human-Machine Interfaces
## 1 Introduction  
User experience (UX) is identified as an essential topic for design and functionality in the development of user interfaces (UI) [1]. Today, machine learning-based solutions are widely used in all types of production development work [2], including the topic of human-machine interactions. The potential of machine learning techniques has been explored in areas such as air traffic management [3], collaborative robot control [4], and industrial PLC programming [5]. Industrial Human-Machine Interfaces (HMIs) are designed primarily based on the Piping and Instrumentation Diagram (P and ID) or the physical layout of the plant, rather than from a process perspective. As an effect of this design principle, operator actions must be performed through several displays, causing a time loss during the intervention. Time loss can cause decreased production volume, poor product quality, or even hazardous personal and environmental situations. Therefore, it is necessary to reduce the workload of plant personnel to ensure more efficient and safer process management, which is consistent with the Industry 5.0 paradigm [6], focusing on human-centric solutions [7]. The industry is aware of the shortcomings of P and ID-based HMI design, and the solution is to develop high-performance HMIs [8]. However, \"high performance\" was limited to the information visualisation part of the problem, which failed to touch on the content of the display of hierarchy levels 2 and 3. Drawing level 1 displays based on P and IDs is logical, but levels 2 and 3 need a more process control-related perspective. It is already recognised that HMIs should also be adaptable and easy to use [9]. Distributed Control Systems (DCS) and Supervisory Control And Data Acquisition (SCADA) systems provide a large amount of data stored in data warehouses, supporting the development of a data-driven analysis layer with machine learning (ML) functionality for the management of process safety, and artificial intelligence will become an essential part of smart factories [10]. Although thereare solutions for data-driven optimisation of industrial HMIs [9], systems containing parallel and overlapping processes require a more sophisticated method. Operator behaviour models were explored from the operation log [11] or based on semi-Markovian models [12]. The mentioned techniques are essential for developing an adaptive and interactive HMI, but are insufficient on their own. The idea of developing function-based HMI layouts has been discussed before [13], but the methodology is still based on broad system knowledge and the P and IDs. Although state-of-the-art HMI devices are already available [14] for the \"greenfield\" development of HMI, it is worth optimising existing systems, as \"brownfield\" investments offer a reasonable solution for modernisation [15]. These brownfield investments can be executed cost effectively with the use of open-source and web-based software, providing high flexibility and customisation. The proposed method shares this approach, which is also aligned with the actual trend of using the Industrial Internet of Things philosophy in HP-HMI development tasks [16]. Our suggestion is to create a HMI hierarchy and displays based on typical process scenarios and operator interactions, which helps to apply the \"Navigation and Layout\" perspective of the ISA 101 standard [17]. This kind of information can be extracted from the system log files, but not directly. As an operator can perform several corrective actions belonging to different subprocesses in the same time frame, a segmentation of the log file is needed to accurately identify the subprocesses [18]. The operator action log must be merged with the plant log, pairing the alarm management data with all the actions taken on the HMI by the workers. This way, the number of steps taken to react to an alarm event can be analysed, indicating the necessary amendments for the HMI (shortcuts, pop-up notifications, structure hierarchy). Alarm floods are common in DCS systems.