Vision-based Monitoring System for High Quality TIG Welding

The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of images’ aspect over the categories considered

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

Temperature Monitoring in the Refractory Lining of a Continuous Casting Tundish using Distributed Optical Fiber Sensors

This Article Explores the Prospects of using Spatially Distributed Optical Fiber Temperature Sensors based on Rayleigh Optical Frequency Domain Reflectometry (OFDR) Technology in the Continuous Casting of Molten Steel. the Measurement Capability of the Optical Fiber Sensors in a Simulated Steelmaking Environment Was Demonstrated using a Mock Refractory-Lined Tundish, Which Was Fabricated In-House. Single-Mode Optical Fibers, Contained in Protective Stainless-Steel Tubing, Were Embedded in the Refractory Lining of the Mock Tundish. the Instrumented Tundish Was Preheated Up to a Temperature of 960 °C (Recorded at the Surface of the Working Lining) Before the Molten Steel Pour. a Low-Alloy Steel (AISI 4140 Grade) Was Melted in a 200 Lb (90.7 Kg) Coreless Induction Furnace and Was Poured into the Instrumented Preheated Tundish. the Embedded Optical Fiber Sensors Were Used to Measure the Temperature Distribution in the Castable Lining during the Preheating Process of the Tundish and during its Contact with Molten Steel. Temperatures Were Metered with a Spatial Resolution of 0.65 Mm Along the Embedded Optical Fibers. the Measurements Were Recorded with an Update Rate of 1 Hz. the Maximum Temperature Recorded by the Optical Fiber Sensors in the Castable Lining after the Steel Pour Was 591°C. the Spatially Continuous Optical Fiber Sensors Provided Useful Information on Thermal Gradients in the Castable Lining. Real-Time Monitoring of Spatially Distributed Thermal Profiles in the Refractory Lining Can Improve Superheat Control, Reduce Energy Losses, Detect Refractory Wear, and Improve the Quality of the Cast

Nondestructive evaluation and in-situ monitoring for metal additive manufacturing

Powder-based additive manufacturing (AM) technologies are seeing increased use, particularly because they give greatly enhanced design flexibility and can be used to form components that cannot be formed using subtractive manufacturing. There are fundamental differences in the morphology of additively manufactured materials, when compared with, for example castings or forgings. In all cases it is necessary to ensure that parts meet required quality standards and that “allowable” anomalies can be detected and characterized. It is necessary to understanding the various types of manufacturing defects and their potential effects on the quality and performance of AM, and this is a topic of much study. In addition, it is necessary to investigate quality from powder throughout the manufacturing process from powder to the finished part. In doing so it is essential to have metrology tools for mechanical property evaluation and for appropriate anomaly detection, quality control, and monitoring. Knowledge of how and when the various types of defects appear will increase the potential for early detection of significant flaws in additively manufactured parts and offers the potential opportunity for in-process intervention and to hence decrease the time and cost of repair or rework. Because the AM process involves incremental deposition of material, it gives unique opportunities to investigate the material quality as it is deposited. Due to the AM processes sensitivity to different factors such as laser power and material properties, any changes in aspects of the process can potentially have an impact on the part quality. As a result, in-process monitoring of additive manufacturing (AM) is crucial to assure the quality, integrity, and safety of AM parts. To meet this need there are a variety of sensing methods and signals which can be measured. Among the available measurement modalities, acoustic-based methods have the advantage of potentially providing real-time, continuous in-service monitoring of manufacturing processes at relatively low cost. In this research, the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capabilities of existing characterization methodologies for powder-based AM parts are discussed and methods for in-situ non-destructive evaluation are reviewed. A proof-of-concept demonstration for acoustic measurements used for monitoring both machine and material state is demonstrated. The analyses have been performed on temporal and spectral features extracted from the acoustic signals. These features are commonly related to defect formation, and acoustic noise that is generated and can potentially characterize the process. A novel application of signal processing tools is used for identification of temporal and spectral features in the acoustic signals. A new approach for a K-means statistical classification algorithm is used for classification of different process conditions, and quantitative evaluation of the classification performance in terms of cohesion and isolation of the clusters. The identified acoustic signatures demonstrate potential for in-situ monitoring and quality control of the additive manufacturing process and parts. A numerical model of the temperature field and the ultrasonic wave displacement field induced by an incident pulsed laser on additively manufactured stainless steel 17 4 PH is established which is based on thermoelastic theory. The numerical results indicate that the thermoelastic source and the ultrasonic wave features are strongly affected by the characteristics of the laser source and the thermal and mechanical properties of the material. The magnitude and temporal-spatial distributions of the pulsed laser source energy are very important factors which determine not only the wave generation mechanisms, but also the amplitude and characteristics of the resulting elastic wave signals

DISTRIBUTED FIBER-OPTIC TEMPERATURE SENSORS FOR APPLICATIONS IN THE STEEL INDUSTRY

Steelmaking facilities require continuous temperature measurements throughout the manufacturing process to ensure consistent product quality and high productivity. Motivated by the limitations of conventional temperature sensors, distributed fiber-optic sensors (DFOS) were developed and deployed for various applications in the steel industry. Fiber-optic sensors offer various advantages over conventional sensors, such as the miniaturized size of the optical fiber, immunity to electromagnetic interferences, capability for multiplexing and distributed sensing, and the ability to withstand harsh environments. Firstly, high-resolution Rayleigh backscattering based DFOS were demonstrated as potential solutions for temperature measurements in steelmaking processes by performing experimental simulations. Additionally, aluminum casting experiments were conducted to demonstrate the measurement capability of DFOS in solidifying metal alloys. Temperatures exceeding 700 ℃ were measured at sub-millimeter spatial resolution (~ 0.65 mm) and at milliseconds sampling speeds. Moreover, a novel dip testing paddle was developed employing a copper mold instrumented with optical fiber. The instrumented mold was used to perform steel dip tests in a 200 lb induction furnace in a foundry laboratory. The results obtained from temperature measurements provided strong evidence that the dip testing paddle can be a useful apparatus for the investigation of the fundamental reactions occurring in a continuous casting mold. The present study demonstrated that DFOS can be transformative to the steel industry by enabling efficient process control, reducing energy and maintenance costs, improving the safety of equipment and workers, and enhancing the quality and yield of metal products --Abstract, p. i

Accelerating Process Development for 3D Printing of New Metal Alloys

Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties

Monitoring and characterization of abnormal process conditions in resistance spot welding

Resistance spot welding (RSW) is extensively used for sheet metal joining of body-in-white (BIW) structure in the automobile industry. Key parameters, such as welding current, electrode force and welding time, are involved in the RSW process. Appropriate welding parameters are vital for producing good welds; otherwise, undersized weld and expulsion are likely to be caused. For a specific type of sheet metal, an acceptable nugget is produced when an appropriate combination of welding parameters is used. However, undersized welds and expulsion are still commonly seen in the plant environment, where some abnormal process conditions could account for the production of the poor quality welds. Understanding the influence of abnormal process conditions on spot weld quality and other RSW related issues is crucial. A range of online signals, strongly related to the nugget development history, have attracted keen interest from the research community. Recent monitoring systems established the applied dynamic resistance (DR) signal, and good prediction of nugget diameter was made based on signal values. However, the DR curves with abnormal process conditions did not agree well with those under normal condition, making them less useful in detecting abnormal process conditions. More importantly, none of the existing monitoring systems have taken these abnormal process conditions into account. In addition, electrode degradation is one of the most important issues in the plant environment. Two major electrode degradation mechanisms, softening and intermetallic compound (IMC) formation, are strongly related to the characteristics of welding parameters and sheet metals. Electrode misalignment creates a very distinct temperature history of the electrode tip face, and is believed to affect the electrode degradation mechanism. Though previous studies have shown that electrode misalignment can shorten electrode life, the detailed mechanism is still not understood. In this study, an online-monitoring system based on DR curve was first established via a random forest (RF) model. The samples included individual welds on the tensile shear test sample and welds on the same sheet, considering the airgap and shunting effect. It was found that the RF model achieved a high classification accuracy between good and poor welds. However, the DR signals were affected by the shunting distance, and they displayed opposite trends against individual welds made without any shunting effect. Furthermore, a suitable online signal, electrode displacement (ED), was proposed for monitoring abnormal process conditions such as shunting, air gap and close edged welds. Related to the thermal expansion of sheet metal, ED showed good consistency of profile features and actual nugget diameters between abnormal and normal welds. Next, the influence of electrode misalignment on electrode degradation of galvannealed steel was qualitatively and quantitatively investigated. A much-reduced electrode life was found under the angular misalignment of 5°. Pitting and electrode softening were accelerated on the misaligned electrodes. δ Fe-Zn phase from the galvannealed layer that extends electrodes was found non-uniformly distributed on the worn electrode. Furthermore, electron backscatter diffraction (EBSD) analysis was implemented on the worn electrode, showing marked reduction in grain diameter and aspect ratio. The grain deformation capacity was estimated by the distribution of the Taylor factor, where the portion of pore grain was substantially weakened in the recrystallized region compared to the base metal region

ESTABLISHING THE FOUNDATION TO ROBOTIZE COMPLEX WELDING PROCESSES THROUGH LEARNING FROM HUMAN WELDERS BASED ON DEEP LEARNING TECHNIQUES

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF). This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by robotize a complex welding process. By integrate process sensing, data visualization, deep learning-based modeling and optimization, a complex welding system is established, with the systematic solution to generalize domain-specific knowledge from experienced human welder. Such system can automatically perform complex welding processes that can only be handled by human in the past. To enhance the system\u27s tracking capabilities, we trained an image segmentation network to offer precise position information. We incorporated a recurrent neural network structure to analyze dynamic variations during welding. Addressing the challenge of human heterogeneity in data collection, we conducted experiments illustrating that even inaccurate datasets can effectively train deep learning models with zero mean error. Fine-tuning the model with a small portion of accurate data further elevates its performance

Additive Manufacturing Research and Applications

This Special Issue book covers a wide scope in the research field of 3D-printing, including: the use of 3D printing in system design; AM with binding jetting; powder manufacturing technologies in 3D printing; fatigue performance of additively manufactured metals, such as the Ti-6Al-4V alloy; 3D-printing methods with metallic powder and a laser-based 3D printer; 3D-printed custom-made implants; laser-directed energy deposition (LDED) process of TiC-TMC coatings; Wire Arc Additive Manufacturing; cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing; the influence of material properties and characteristics in laser powder bed fusion; Design For Additive Manufacturing (DFAM); porosity evaluation of additively manufactured parts; fabrication of coatings by laser additive manufacturing; laser powder bed fusion additive manufacturing; plasma metal deposition (PMD); as-metal-arc (GMA) additive manufacturing process; and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning

Process Modeling in Pyrometallurgical Engineering

The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions