Predictive Maintenance in the Production of Steel Bars: A Data-Driven Approach

The ever increasing demand for shorter production times and reduced production costs require manufacturing firms to bring down their production costs while preserving a smooth and flexible production process. To this aim, manufacturers could exploit data-driven techniques to monitor and assess equipmen’s operational state and anticipate some future failure. Sensor data acquisition, analysis, and correlation can create the equipment’s digital footprint and create awareness on it through the entire life cycle allowing the shift from time-based preventive maintenance to predictive maintenance, reducing both maintenance and production costs. In this work, a novel data analytics workflow is proposed combining the evaluation of an asset’s degradation over time with a self-assessment loop. The proposed workflow can support real-time analytics at edge devices, thus, addressing the needs of modern cyber-physical production systems for decision-making support at the edge with short response times. A prototype implementation has been evaluated in use cases related to the steel industry

Nonterrestrial utilization of materials: Automated space manufacturing facility

Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into significantly a more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning and non-deep learning methods, to the close-form transformation, we evaluate their potential for improving surface texture control in temper strip steel manufacturing

SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel

Proposal on Electric ARC Furnace Utilization to Achieve Cost Optimization in Slab Steel Plant PT Krakatau Steel

Steel Business environment has changed a lot lately, and competition is getting stronger. The global competitive pressure has changed the nature of economy Causing changes in methods of operation in industries. Faced with these situation, the company must be Able to survive and win the competition by performing measurements include all aspects that are future oriented. Krakatau recently imported a lot of semi-finished slab regarding low price for commercial grade steel. In order to win a competition all the effort was done by continuous improvement to reduce production costs. Steel slab plant is part of an integrated stainless steel production facility of Krakatau play the biggest role of production cost responsibility. It is by the caused of the major metallurgical task to convert iron ore to steel and non-metallic raw material to slag. Secondly steel making consume huge slab of electrical energy to melt the solid material to liquid steel. Excellent strategy and controlling tights will get very high benefit to earn much profit.The final assigment is focused on formulating a production strategy based on market requirements and production cost. Key point to get succesfull implementation of these depends on the top management strategy comitment, the Efforts of socialization, and the participation of all staff and employee. The feedback pattern always makes the learning more effective strategic and structured and continuous improvement smootly runs

DIGITAL MUSEUM TECHNOLOGY FOR WORCESTER’S INDUSTRIAL HISTORY

Working alongside the Worcester Historical Museum (WHM), our group collected historical and technological information to aid the reconstruction of the Fuller Industrial History Gallery. Experts, scholarly literature, and digital platforms were surveyed to ascertain modern digital methods of museum display. This report also presents potential new artifacts for the Gallery with information regarding the evolution of the wire and metal trades industries especially Morgan Construction in the early 20th century and Kinefac in the late 20th and early 21st centuries. This IQP provides recommendations on smart technologies and industrial artifacts for inclusion in the renovated Gallery

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00

Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

Energy management structure and behaviour and motivation analysis within each sector

The understanding of the organizational aspects of achieving increased energy efficiency in In-dustrial Water Circuits (IWC) over the course of the WaterWatt project has benefitted from two parallel processes. On the one hand, conducting case studies in a variety of countries and sec-tors has helped to identify and to distinguish what might be referred to as ‘universal’ and ‘local’ factors that influence the degree of energy efficiency in IWC. On the other hand, discussions within the WaterWatt consortium, partly informed by insights established during the case study research, about the direction and focus of the organizational aspects of the project have moved forward. The aim of conducting the case studies as part of WaterWatt Project is to understand how indus-trial water circuits work in practice and in particular contexts. For our technical colleagues, the case study approach has been important to help them in their efforts to incorporate the modelling of water circuits into the E3 Platform. From our sociological perspective, the case study ap-proach has proved to be an excellent method to develop our understanding of the organizational dimensions of achieving greater energy efficiency. The case studies have helped us to formulate organizational factors, as well as produce a list of relevant contextual factors, which essentially represent the conclusions of this work (see D3.3). The following sets of case studies have been conducted - A steelwork in Germany in June 2016 - A steel plant and a non-ferrous metal plant in Norway in October 2016 - A paper & cardboard and a sugar plant in Portugal in November 2016 - A steel plant in the United Kingdom in May 201