Process Control & Automation
The Internet of Things (IoT) has seen an explosion in the development of sensing devices gathering and producing ubiquitous amounts of high-volume and high-velocity data contributing to the big data environment. Today, Industry 4.0 is driving advances in innovative technologies to help digitize manufacturing processes, enabling business to make more informed decisions. Sensors play a fundamental role in either monitoring or controlling the continuous casting process. Sensors continuously collect and transmit measurements and contain rich information about the condition of equipment. This paper explores the use of data mining techniques to generate failure prediction models where the pre-failure conditions are learnt from historical sensor data. The suitability of two failure detection approaches, anomaly detection and classification, are reviewed in collaboration with Tata Steel UK. Ultimately, this information will be applied in real time to predict future failures and to assist in optimizing the scheduling of maintenance.
One requirement in wire rod production is head and tail coil trimming to remove rings with property variations. In-line shears prior to coiling at the laying head are available, but are difficult to operate and maintain. Most mills rely on manual trimming at a coil inspection station — a labor-intensive and potentially dangerous task. A new system has been developed for trimming within coil handling. A vision-assisted robot equipped with a ring separator and trimmer identifies, trims and removes unwanted rings. The new system’s capabilities are presented with results from a pilot facility and an operating wire rod mill.
In modern steel manufacturing plants, the possibility of ensuring a consistent surface quality of the rolled stock has now become a fundamental feature. Nowadays almost all steelmaking plants are equipped with a laser section profile gauge but few of them have a surface defect detection system. The increasing requirement for a combined system for profile measuring and defect detection led Danieli Automation to develop a solution to provide a complete production quality assurance package. A high-definition 3D surface reconstruction and accurate defect detection are obtained by the combination of very high-speed cameras and technologically advanced machine vision mathematical algorithms.
An Intelligent quality monitoring system (IQMS) has been developed at the continuous galvanizing line at Tata Steel Jamshedpur with an objective to monitor the critical process parameters, thereby ensuring consistent product quality. The system captures continuous data from level 2 automation and based on the control limits for each parameter the coil is put on auto-hold in case of deviation observed. Prior to deployment of IQMS, there was no system to take objective decisions. Post-implementation of IQMS, there has been a substantial drop in the customer claims and has led to a reduction in internal quality downgrading due to the faster response.
This paper describes a recently developed continuous galvanizing process that is flexible in handling various types of cut-length long steel products, such as rebar, rolled thread bars, agriculture and highway posts, and structural shapes. The benefits of the new process include automated and efficient material handling, significant material savings, high productivity and consistent, superior product quality. The process utilizes abrasive blasting, an aluminum-compatible flux, induction heating and a continuous galvanizing grade zinc bath in an inert environment, followed by air wiping, water cooling and passivation. The elimination of the pickling process is environmentally friendly, while reducing the potential risk of hydrogen embrittlement. The limited exposure to elevated temperatures better preserves the mechanical properties of high-strength steels. The paper also outlines zinc and zinc alloy coating development and discusses in detail the established and ongoing research studies related to continuous galvanized long steel products.
The average steelworker’s age is increasing while the head count is leaner, providing fewer opportunities to transfer knowledge from more experienced to less experienced people. Operational art is being lost while skill gaps and productivity pressures are increasing. Augmented reality (AR), the human-machine interface of Industry 4.0, is already helping. An industrial AR system called Manifest has been implemented by Tata Steel Europe to capture and share operational knowledge. This paper will provide a background of AR technology and provide examples of how Manifest is being used by Tata Steel to digitalize knowledge transfer.