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

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Towards a methodology for integrated freeform manufacturing systems development with a control systems emphasis

A variety of fully integrated Freeform Fabrication (FFF) systems have been developed, a selected group for research and several for commercialization. The design methodology behind most of them is not documented, standardized, or rational. It is important to understand that the final product from any integrated system is affected not only by the unit manufacturing processes themselves, but also by the extent the individual units are assimilated into an integrated process. Thus, a scheme consisting of eight steps and the salient five elements necessary to create or retrofit an existing system to achieve an Integrated Freeform Manufacturing System (FFMS) is proposed in this thesis. Specifically, mass-change, deformation and consolidation unit manufacturing processes are emphasized, as the priority is focused on rapid prototyping (RP) technologies. To illustrate the proposed scheme, the University of Missouri-Rolla (UMR) Laser Aided Manufacturing Process (LAMP) system is presented --Abstract, page iv

Optimization of Process Parameters for CNC Turning using Taguchi Methods for EN24 Alloy Steel with Coated/Uncoated Tool Inserts

Coated and uncoated tool inserts offers certain degrees of control on the desired rate of tool wear and surface roughness to an extent. This work pursues the quest for realizing the optimal values for the significant process parameters that bears an influence on the response parameters. Experiments were conducted on the samples of EN 24 alloy steel material with the help of PVD coated TiAlN insert and uncoated carbide insert. The experimental runs carried out with proper variation in the levels. Levels are selected with the help of manufacturing catalogue and by pilot experimentation and results are recorded for further analysis. For this study, 9 runs designed using L9 orthogonal array of Taguchi Design of Experiment. Surface roughness was measured using a Mitutoyo surface tester at test lab and material removal rate is calculated by mathematical equation. The data was compiled into Minitab 17 software for analysis. The relationship between the machining parameters and the response variables were analyzed using the Taguchi Method. Optimization of process parameters is carried out by Grey Relational Analysis method (GRA). GRA method is a powerful and most versatile tool which can manipulate the input data as per requirement and comes with results that can be used to have best multi-objective in respective concerns

Intelligent machining methods for Ti6Al4V: a review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision- making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence- based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.publishe

PROCESS CONTROL SYSTEM IDENTIFICATION

System Identification is an art of dealing with a problem of generating workable model of dynamic response based on the observed dataset from the actual system. The modelling process is based on the observed input and output data of a system. The objective of this project is to design and implement System Identification for Liquid System Pilot Plant in UTP by applying both the conventional System Identification technique known as empirical modeling and the intelligent techniques, a computer based method named System Identification Toolbox. Then, the comparison study between intelligent techniques and conventional modelling technique is conducted for a better performance determination. System Identification procedure involves the construction of a model from actual data and the model validation process. The construction of a model engages with three basic entities that are data record, model structure and determination of the best model. By following the System I dentification procedure, the four steps taken in accomplishing this project were: (1) experimental design, (2) modelling via empirical modelling, (3) simulation of System Identification via MATLAB-Simulink and (4) investigate performance Comparison between empirical modelling and model predictor using System Identification Toolbox. Empirical modelling is a simple graphical and calculation technique. A linear transfer function that is obtained from this method is adequate for the project implementations. The second method is intelligent method which is carried out with the aid of MATLAB software. All the selected best models are capable to reproduce the observed data with minimum predicted error. At the end of the project, based on some comparison and analysis, the author concludes that an intelligent technique gives a better performance compared to the conventional technique.

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. 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Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper

Intelligent Painting Process Planner for Robotic Bridge Painting

Due to increased government regulations on environment, health, and safety, the cost of on-site bridge painting has quadrupled over the past several years. The construction industry faces a great challenge in how to control the increased costs of bridge painting and meet the regulations at the same time. A possible solution to address this challenge is to develop a robotic bridge painting system. The development of the robotic system can be justified by the potential improvements in safety and productivity. This paper presents the development and testing of an Intelligent Painting Process Planner. The Planner, built based on bridge feature scheme, is the key component for the robotic bridge painting system that integrates the painting process planning, robot path planning, cost optimization, and quality control functions. During the development process, lab experiments were conducted to determine the values of painting process planning parameters and coating thickness distribution functions. Field tests demonstrated that the prototype robotic bridge painting system achieved the specified painting quality using the parameter values provided by the Planner. Areas that need to be improved in the future were also identified