Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

Abstract

A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing factors including plate thickness and roll force in each rolling pass. For example, the estimation or prediction of the in-process thickness is utilized to select the control values (e.g., roll gap) in the next pass of rolling. However, adverse manufacturing conditions can interfere with accurate detection for such manufacturing factors. Although the state-of-the-art gamma-ray camera can be used for measuring the thickness, the outputs from it are influenced by adverse manufacturing conditions such as the high temperature of plates, followed by the evaporation of lubricant water. Thus, it is inevitable that there is noise in the thickness estimation. Furthermore, installing such thickness measurements for each passing step is costly. The precision of the thickness estimation, therefore, significantly affects the cost and quality of the final product. In this paper, we present machine learning (ML) technologies and models that can be used to predict the in-process thickness in the SPM operation, so that the measurement cost for the inprocess thickness can be significantly reduced and high-quality steel plate production can be possible. To do so, we investigate most-known technologies in this application. In particular, Data Clustering based Machine Learning (DC-ML), combining clustering algorithms and supervised learning algorithms, is introduced. To evaluate DC-ML, two experiments are conducted and show that DC-ML is well suited to the prediction problems in the SPM operation. In addition, the source code of DC-ML is provided for the future study of machine learning researchers. INDEX TERMS Intelligent manufacturing systems, machine learning, regression analysis, steel industry, thickness control.

A Framework for Off-Line Operation of Smart and Traditional Devices of IoT Services

“…In an IoT scenario, edge computing provides computational power, secures access to the edge data, reduces latency for time-sensitive applications, supports performance in low-bandwidth environments and eases overall network congestion [ 25 ]. In this way, merging multiple end devices into a single module can significantly reduce the calculation amount of the end device, and edge computing can be implemented [ 6 , 7 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

“…In past research, edge computing was implemented to ensure the reliability in local, and the communication latency reduced [ 6 , 7 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, in these works of literature, most of them are assisted by an additional device server; this is an additional overhead to the hardware cost and should consider whether it is necessary, for example, Smart Farm, where communication of the network is poor.…”

“…Therefore, this study moves the essential functions of IoT applications to the gateway. Compared with [ 6 , 7 , 13 , 18 ] and this study, it is feasible to use the more substantial computing power of the gateway to perform edge computing to assist other end devices to compute. Then, edge devices can not only transmit data but also preprocess data and analyse real-time data, which can support edge computing and provide logic control services.…”

“…The modular framework proposed in this paper is suitable for various environments, including a smart warehouse, which contains all kinds of machines such as robotic arms, unmanned aircraft, unmanned vehicles, take-off and landing machines and robots [ 2 , 6 , 7 , 39 , 40 ]. Such a large environment typically comprises large shelves, shelters, high places and corners that can cause instability for wireless signals.…”

“…The device-to-device connection enables automatic control to improve people’s quality of life. With the emergence of Industry 4.0, IoT, big data, cloud computing, machine learning and artificial intelligence can effectively contribute to the industrial field [ 6 , 7 ]. Various devices in the industrial field are connected to the IoT through wireless sensor networks to produce different applications.…”