A technical perspective on integrating artificial intelligence to solid-state welding

The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing their potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy

Monitoring and intelligent control for complex curvature friction stir welding

A multi-input multi-output system to implement on-line process monitoring and intelligent control of complex curvature friction stir welding was proposed. An extra rotation axis was added to the existing three translation axes to perform friction stir welding of complex curvature other than straight welding line. A clamping system was designed for locating and holding the workpieces to bear the large force involved in the process between the welding tool and workpieces. Process parameters (feed rate, spindle speed, tilt angle and plunge depth), and process conditions (parent material and curvature), were used as factors for the orthogonal array experiments to collect sensor data of force, torque and tool temperature using multiple sensors and telemetry system. Using statistic analysis of the experimental data, sensitive signal features were selected to train the feed-forward neural networks, which were used for mapping the relationships between process parameters, process conditions and sensor data. A fuzzy controller with initial input/output membership functions and fuzzy rules generated on-line from the trained neural network was applied to perceive process condition changes and make adjustment of process parameters to maintain tool/workpiece contact and energy input. Input/output scaling factors of the fuzzy controller were tuned on-line to improve output response to the amount and trend of control variable deviation from the reference value. Simulation results showed that the presented neuro-fuzzy control scheme has adaptability to process conditions such as parent material and curvature changes, and that the control variables were well regulated. The presented neuro-fuzzy control scheme can be also expected to be applied in other multi-input multi-output machining processes

Friction Stir Welding Manufacturing Advancement by On-Line High Temperature Phased Array Ultrasonic Testing and Correlation of Process Parameters to Joint Quality

Welding, a manufacturing process for joining, is widely employed in aerospace, aeronautical, maritime, nuclear, and automotive industries. Optimizing these techniques are paramount to continue the development of technologically advanced structures and vehicles. In this work, the manufacturing technique of friction stir welding (FSW) with aluminum alloy (AA) 2219-T87 is investigated to improve understanding of the process and advance manufacturing efficiency. AAs are widely employed in aerospace applications due to their notable strength and ductility. The extension of good strength and ductility to cryogenic temperatures make AAs suitable for rocket oxidizer and fuel tankage. AA-2219, a descendent of the original duralumin used to make Zeppelin frames, is currently in wide use in the aerospace industry. FSW, a solid-state process, joins the surfaces of a seam by stirring the surfaces together with a pin while the metal is held in place by a shoulder. The strength and ductility of friction stir (FS) welds depends upon the weld parameters, chiefly spindle rotational speed, feedrate, and plunge force (pinch force for self-reacting welds). Between conditions that produce defects, it appears in this study as well as those studies of which we are aware that FS welds show little variation in strength; however, outside this process parameter “window” the weld strength drops markedly. Manufacturers operate within this process parameter window, and the parameter establishment phase of welding operations constitutes the establishment of this process parameter window. The work herein aims to improve the manufacturing process of FSW by creating a new process parameter window selection methodology, creation of a weld quality prediction model, developing an analytical defect suppression model, and constructing a high temperature on-line phased array ultrasonic testing system for quality inspection

OPTIMIZED FATIGUE AND FRACTURE PERFORMANCE OF FRICTION STIR WELDED ALUMINIUM PLATE: A STUDY OF THE INTER-RELATIONSHIP BETWEEN PROCESS PARAMETERS, TMAZ, MICROSTRUCTURE, DEFECT POPULATION AND PERFORMANCE

Friction stir welding (FSW) is an exciting new solid-state welding process with the potential to advantageously impact many fabrication industries. Current take-up of the process by industry is hindered by lack of knowledge of suitable welding parameters for any particular alloy and sheet thickness. The FSW process parameters are usually chosen empirically and their success is evaluated via simple mechanical property testing. There are severe drawbacks with such methods of determining manufacturing conditions. These include indirect relationships between tensile and fatigue properties, particularly for welds, and a high probability of totally missing real optimized conditions. This research is therefore undertaken as a first step in providing information that will assist manufacturing industry to make sound decisions with respect to selecting FSW parameters for weldable structural alloys. Some of the key issues driving material selection for manufacturing are weld quality in terms of defects, fatigue strength and crack growth, and fracture toughness. Currently a very limited amount of data exists regarding these mechanical properties of FSW welds, and even less information exists regarding process parameter optimization. This is due to the mechanical microstructural complexity of the process and the relatively large number of process parameters (feed, speed, force and temperature) that could influence weld properties. In order to advance predictive understanding and modeling for FS welds, it is necessary to develop force and energy based models that reflect the underlying nature of the thermo-mechanical processes that the material experiences during welding. This project aims at determining the influence and effect of Friction Stir Welding process control parameters on the microstructure of the thermo-mechanically affected zone, the defect population in the weld nugget, hardness, residual stresses, tensile and fatigue performance of 6 mm plate of 5083-H321 aluminium alloy, which is known to be susceptible to planar defect formation. Welds were made with a variety of process parameters (that is feed rate and rotational speed) to create different rates of heat input. Forces on the FSW tool (horizontal and vertical), torque and tool temperature were measured continuously during welding from an instrumented FSW tool. Detailed information on fatigue performance, residual stress states, microstructure, defect occurrence, energy input and weld process conditions, were investigated using regression models and contour maps which offer a unique opportunity to gain fundamental insight into the process-structure-property relationships for FS welds. Weld residual strains have been extensively measured using synchrotron X-ray diffraction strain scanning to relate peak residual stresses and the widths of the peak profiles, taken from a single line scan from the mid depth of the FS welds, with the weld process conditions and energy input into the welds. Several residual stress maps were also investigated. The optical and scanning electron microscope were used to determine the type of intrinsic defects present in the FSW fatigue and tensile specimens. Vickers hardness measurements were taken from the mid depth of the welds and were compared with the weld input parameters. The main contribution of this thesis is as follow: (i) the relationship between input parameters and process parameters; (ii) the relationship between input weld parameters (that is feed rate and rotational speed) and process parameters (that is vertical downwards force Fz, tool temperature, tool torque and the force footprint data), energy input and tensile strength, fatigue life and residual stresses to obtain regions of optimum weld conditions; (iii) identification of the defects present in FSW, their relationship with process parameters and their effect on tensile strength and fatigue life; and (iv) the usefulness of the real time process parameter monitoring automated instrumented FSW tool to predict the mechanical properties of the welds.Nelson Mandela Metropolitan University, South Afric

Engineering Principles

Over the last decade, there has been substantial development of welding technologies for joining advanced alloys and composites demanded by the evolving global manufacturing sector. The evolution of these welding technologies has been substantial and finds numerous applications in engineering industries. It is driven by our desire to reverse the impact of climate change and fuel consumption in several vital sectors. This book reviews the most recent developments in welding. It is organized into three sections: “Principles of Welding and Joining Technology,” “Microstructural Evolution and Residual Stress,” and “Applications of Welding and Joining.” Chapters address such topics as stresses in welding, tribology, thin-film metallurgical manufacturing processes, and mechanical manufacturing processes, as well as recent advances in welding and novel applications of these technologies for joining different materials such as titanium, aluminum, and magnesium alloys, ceramics, and plastics

Bibliography of Lewis Research Center technical publications announced in 1993

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.

Special Issue of the Manufacturing Engineering Society (MES)

This book derives from the Special Issue of the Manufacturing Engineering Society (MES) that was launched as a Special Issue of the journal Materials. The 48 contributions, published in this book, explore the evolution of traditional manufacturing models toward the new requirements of the Manufacturing Industry 4.0 and present cutting-edge advances in the field of Manufacturing Engineering focusing on additive manufacturing and 3D printing, advances and innovations in manufacturing processes, sustainable and green manufacturing, manufacturing systems (machines, equipment and tooling), metrology and quality in manufacturing, Industry 4.0, product lifecycle management (PLM) technologies, and production planning and risks