Steel Quality Control Research Articles

Development of a non-destructive depth-selective quantification method for sub-percent carbon contents in steel using negative muon lifetime analysis

The amount of C in steel, which is critical in determining its properties, is strongly influenced by steel production technology. We propose a novel method of quantifying the bulk C content in steel non-destructively using muons. This revolutionary method may be used not only in the quality control of steel in production, but also in analyzing precious steel archaeological artifacts. A negatively charged muon forms an atomic system owing to its negative charge, and is finally absorbed into the nucleus or decays to an electron. The lifetimes of muons differ significantly, depending on whether they are trapped by Fe or C atoms, and identifying the elemental content at the muon stoppage position is possible via muon lifetime measurements. The relationship between the muon capture probabilities of C/Fe and the elemental content of C exhibits a good linearity, and the C content in the steel may be quantitatively determined via muon lifetime measurements. Furthermore, by controlling the incident energies of the muons, they may be stopped in each layer of a stacked sample consisting of three types of steel plates with thicknesses of 0.5 mm, and we successfully determined the C contents in the range 0.20–1.03 wt% depth-selectively, without sample destruction.

Quality Control of Steel in Steel Iron Product Factory System

Quality Control of Steel in Steel Iron Product Factory System

ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTION OF MECHANICAL PROPERTIES OF STEEL

The prediction of mechanical properties is very important task in the quality control of steel, which is one of the world's most important engineering and construction materials. To the main factors affecting the mechanical properties of steel belongs thermomechanical treatment and the chemical composition. The chemical composition may vary for a special high-strength low-alloy steel to meet certain mechanical property requirements. This paper presents a machine learning approach for prediction of tensile strength of structural steels. A single hidden layer Artificial Neural Network (ANN) model was developed and validated based on industrial data set. The ANN model inputs were parameters of interatomic interaction, characterizing the chemical and structural state of the melt. The modeling results showed that the ANN model might be an attractive and useful tool for forecasting the mechanical properties of steel. It offers also a potential for the further derivative free optimization of chemical composition for special quality criteria.

Digitization Possibilities for Steel Quality Control Using the Structure of Fracture

Digitization Possibilities for Steel Quality Control Using the Structure of Fracture

Steel quality of rolling contact bearings produced by different manufacturers, used for mining equipment instrumentation

Severe operating conditions of mining equipment have a negative impact on the operating life of the rolling contact bearings used in such equipment. The main reasons for the premature failure of rolling contact bearings include, first of all, poor quality of the bearing steel as delivered and the defects of their subsequent heat treatment. The abovementioned reasons result in splitting of bearings, metal shelling and appearance of potholes on their tracks, as well as intense abrasive wear. Nevertheless, despite the importance of this issue, the quality of bearing steel, either as delivered or in finished bearings, is usually not under control.To solve this problem, the quality of bearing steel was studied, exemplified by the bearings of the Russian, Swedish (SKF) and Japanese (NSK) manufacturers, i.e., main suppliers of rolling contact bearings for mining equipment in Russia. The problem of bearing steel quality control was solved by developing a quality control methodology that took into account the state of the metal after metallurgical treatment and at the stage of bearing production. Based on this methodology, the main types of bearing steel defects affecting the mechanism of bearing failure are identified.It is found that the bearing steel quality of domestic manufacturers is highly competitive with foreign counterparts. A method of nondestructive control is also proposed for detecting possible microstructure defects in bearings, either as delivered or while in operation.

Quality Control of Steel Deformed Bar Product using Statistical Quality Control (SQC) and Failure Mode and Effect Analysis (FMEA)

The Percentage of steel deformed bar product defect in an Indonesian factory was 0.064% by 2019, where the defect percentage exceeded a limit target set by the company, which is 0.050%. This research used a Statistical Quality Control (SQC) method by applying the Seven Tools to identify the problems that caused defects in the product. The author also applied Failure Mode and Effect Analysis (FMEA) method into the production process to generate solutions for the cause of dominant defect factors. According to the calculated RPN value, the cross defect was caused the most by uncentered between top roll and bottom roll. For scratch defect, the main factor carried by Spindle Carrier that is vibrating during the rolling process. The line defect caused by a worn caliber. By implementing the recommended solution, the percentage of product defects decreased from 0.064% to 0.0075% during March to June 2020.

One-Shot Recognition of Manufacturing Defects in Steel Surfaces

Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.

Quality control of steel for large-sized welded structures of Arctic shelf. Application of Russian and foreign requirements

Future large-scale development of Arctic inevitably requires marine structures both of Russian and foreign manufacturers and, correspondingly, applying of domestic and foreign steels. So, it is expedient to compare Russian and foreign systems of standard requirements for steel products and welded joints’ metal applied at low temperatures. The paper analyzes their theoretical and experimental grounding as it is extremely important because the difference in requirements serves an instrument to drive out Russian steel manufacturers from international projects.

A Semi‐parametric Model for Microstructure Analysis of Advanced High‐strength Dual‐phase Steels Considering Sample Variation

Microstructure analysis of advanced high‐strength dual‐phase steels has attracted increasing attention in recent years, as the microscopic properties of steels significantly affect their material properties and behaviors. As a result, the steel microstructure plays an important role of product quality. However, most of the existing methods only consider a single microstructure sample, while the unit‐to‐unit variability among different samples is ignored. As a result, they cannot be applied for steel quality control. In this study, we propose a semi‐parametric microstructure modeling method that can capture the variation across different microstructure samples. The proposed model can be used for both isotropic materials and anisotropic materials in which the microstructure properties in the vertical direction and those in the horizontal direction are different. The proposed model is applied for both quality control of dual‐phase steels and microstructure reconstruction without having to conduct physical experiments. A case study is conducted by applying the developed methods to advanced high‐strength steels. Copyright © 2016 John Wiley & Sons, Ltd.

Scrap management by statistical evaluation of EAF process data

The electric arc furnace (EAF) is a process for melting steel scrap with electricity. In this paper a method for estimating scrap properties based on the evaluation of historical process data is discussed and a series of scrap management strategies based on the estimated properties are suggested. Data from four Swedish EAFs have been analysed and on-line software applications have been developed and installed at one steel plant. The results from this study show that it is possible to use partial least squares to provide accurate estimates of the levels of impurity (Cu, Sn, As) and alloy content (Cr, Ni, Mo) in scrap grades. The degree of explained variation ( R 2) obtained in this study ranges between 40% and 70% for impurity elements and 70% and 100% for alloy elements. The mean prediction errors (RMSEE) are in some cases small enough to improve steel quality control in terms of chemical analysis. To ensure that the estimates remain consistent with scrap quality, it is suggested that the prediction models be updated on a regular basis.

Determination of Urinary Beryllium, Arsenic, and Selenium in Steel Production Workers

Some of the most pernicious dangers of pollution arise from the presence of traces of toxic elements in the environment. In this work, we report on the determination of beryllium, arsenic, and selenium in the urine of steel production and steel quality control (QC) workers, in comparison to healthy control subjects. The urine samples were digested by a microwave system. Graphite furnace and hydride atomic absorption was used for the quantitative measurements of Be and As and Se, respectively. A quality control method for these procedures was established with concurrent analysis of Standard Trace Metals 7879 Level II and NIST SRM 2670 (Toxic Elements in Freeze Dried Urine). The results show that the urinary levels of these elements in steel production (As, 38.1 +/- 28.7 microg/L; Be, 1.58 +/- 0.46 microg/L, and Se, 69.2 +/- 28.8 +/- g/L) and in quality control workers (As, 23.9 +/- 18.1 microg/L; Be, 1.58 +/- 0.46 microg/L, and Se, 54.8 +/- 25.1 microg/L) are significantly higher than in the controls (As, 10.3 +/- 8.7 microg/L; Be, 0.83 +/- 0.46 microg/L; and Se, 32.3 +/- 13.5 microg/L). The possible connection of these elements with the etiology of disease and the possible role of selenium as a protective agent against the oncogenic and teratogenic action of other substances is discussed. We suggest the need for improvement of environmental conditions in the workplace through better ventilation and industrial hygiene practices.

Infrared analysis of thin inorganic and organic films on metals

Infrared spectroscopy is shown to be an adequate method for qualitative and quantitative characterization of technical surface layers for corrosion protection. The measuring conditions for each type of sample have to be selected carefully. For the sake of reliability a certain redundancy in the evaluated data might be useful. It is the aim of this work to gather experience for routine quality control of steel and aluminium surfaces.

Quality Control of Steel Using Clock and Chart Recording Photoelectric Spectrometers

A check analysis program initiated over twenty-five years ago is used to evaluate the performance of emission photoelectric spectrometers in five production control laboratories of the Armco Steel Corporation. For comparison, evaluation of analyses using conventional chemical methods by these same laboratories prior to instrumental analysis is also made The paper describes the check analysis program and presents analytical data used to evaluate the accuracies of the two types of photoelectric spectrometers and the conventional chemical methods used by the production laboratories.

Steel quality control using the ARL quantometer

During the past few years, the use of recording photoelectric spectrometers for the production control of electric and open-hearth furnaces has become increasingly prominent throughout the steel industry. This method of analysis now supplements, and in some cases has replaced, wet chemical and photographic spectrochemical methods.