In the production of cold-rolled strip products, strip breakage is one of the most common failures during the cold rolling process. However, the existing prediction models on strip breakage use the conventional sliding window algorithm to process the time series data collected from the actual production, resulting in a massive amount of non-informative data, which increases the computational cost for data-driven modelling. In order to tackle this issue, this article proposed a sliding window filter method to optimise the data pre-processing of the strip breakage. Firstly, based on the existing research and understanding of strip breakage, the data characteristics in the process of strip breakage was analysed. Based on the analysis, sample variance (VAR) and length normalised complexity estimate (LNCE) were chosen to determine how informative the time window was related to strip breakage. Secondly, compared with the conventional sliding window approach, the sliding windows were classified through a filter using VAR and LNCE. Thirdly, the filtered data was fed into the Recurrent Neural Network (RNN) for strip breakage modelling. An experimental study based on actual production data collected by a cold-rolled strip manufacturer was conducted to verify this method's effectiveness. The results show that pre-processing data using the sliding window filter decreases the model's computational cost.

Human-centric smart manufacturing (HCSM) is one of the essential pillars in Industry 5.0. Hence, human-machine interaction (HMI), as the centre of the research agenda for the advances of smart manufacturing, has also become the focus of Industry 5.0. As Industry 5.0 proposed three core concepts of human-centric, sustainable and resilient, the design orientation of HMI needs to change accordingly. Through understanding the state-of-the-art of HMI research, the technology roadmap of HMI development in the smart manufacturing paradigm can be shaped. In this paper, the focus is to review how HMI has been applied in smart manufacturing and predict future opportunities and challenges when applying HMI to HCSM. In this paper, we provide an HMI framework based on the interaction process and analyse the existing research on HMI across four key aspects: 1) Sensor and Hardware, 2) Data Processing, 3) Transmission Mechanism, and 4) Interaction and Collaboration. We intend to analyse the current development and technologies of each aspect and their possible application in HCSM. Finally, potential challenges and opportunities in future research and applications of HMI are discussed and evaluated, especially considering that the focus of design in HCSM shifts from improving productivity to the well-being of workers and sustainability.