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

CONTROL OF METAL TRANSFER AT GIVEN ARC VARIABLES

Gas Metal Arc Welding (GMAW) is one of the most important welding processes in industrial application. To control metal transfer at given variables is a focus in the field of research and development in welding community. In this dissertation, laser enhanced GMAW is proposed and developed by adding a lower power laser onto the droplet to generate an auxiliary detaching force. The electromagnetic force needed to detach droplets, thus the current that determines this force, is reduced. Wire feed speed, arc voltage, and laser intensity were identified as three major parameters that affect the laser enhanced metal transfer process and a systematic series of experiments were designed and conducted to test these parameters. The behaviors of the laser enhanced metal transfer process observed from high speed images were analyzed using the established physics of metal transfer. In all experiments, the laser was found to affect the metal transfer process as an additional detaching force that tended to change a short-circuiting transfer to drop globular or drop spray, reduce the diameter of the droplet detached in drop globular transfer, or decrease the diameter of the droplet such that the transfer changed from drop globular to drop spray. The enhancement of the laser was found to increase as the laser intensity increased. The larger laser intensity tended to help reduce the size of the droplet detached. The arc voltage affected the metal transfer process through changing the current and changing the gap and possible time interval of the droplet development. A larger arc voltage helped reduce the size of the droplet detached through an increased electromagnetic force. Desired heat input and current/arc pressure waveforms may thus be both delivered and controlled by GMAW through laser enhancement. Laser recoil pressure force was estimated based on the difference of gravitational force with and without laser pulse, and the result was with an acceptable accuracy. Good formation of welds and full penetration of thin plate could be obtained using laser enhanced GMAW. A nonlinear model was established to simulate the dynamic metal transfer in laser enhanced GMAW, and the results agree with the experimental one

Process Modeling Optimization in Additive Manufacturing Using Artificial Neural Networks

The need for production has roots in human life and its history. This date back to primitive days of human life, where he or she had to apply surrounding materials in order to manufacture the tools necessary for survival and durability against any insecurity. This was legitimizing the use of any means in order to obtain the tools and reach the goals at any cost. However, with human development primarily within the knowledge and understanding domain and also with the desire of humanity for best, expectations have risen. This was the time not only the cost mattered but also the simplicity of design, massive production, and diversity, less waste, autonomy, and implementation within a shorter time gained a higher momentum. On the other hand, the conventional manufacturing method was based on subtractive manufacturing with cutting and eliminating the unwanted sections or parts of an object. The disadvantage of such a method is that it requires a complicated production process design and is accompanied by waste. However, with the rise of additive manufacturing and three-dimensional printing equipment back in the 1980s, it became possible to build parts which could have almost any shape or geometry. Moreover, this also empowered the possibility of using digital and 3D models built by computer-aided design software. Simultaneously, on the other side, the foundation and application of artificial intelligence were maturing. This was due to the demand for machines to assist human beings in the domain of knowledge reasoning, learning, and planning. These were the pillars for making machines autonomous and to benefit from such features. Accordingly, this research work studies and overviews the applications and techniques of machine learning and artificial intelligence in the domain of additive manufacturing. It aims to determine the interaction of influential parameters on the process and to find the best solutions for improving the quality and mechanical features of manufactured parts. Moreover, this research tends to enable the experts to grasp a better understanding of AM process during manufacturing and additionally intends to infuse the experts' knowledge in additive manufacturing field utilizing the artificial neural network and finally generate a model with the ability of prediction and selection for promising results

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Development of a Graph-based Metamodelling Framework for Additive Manufacturing and Its Simulation Using Machine Learning

Mallinnuksella ja simuloinnilla voi olla merkittävä rooli monimutkaisen valmistusjärjestelmän ja sen toiminnan ymmärtämisen lisäämisessä. Teollisuus 4.0:n myötä on tarpeen integroida kehittyneitä valmistusprosesseja, esimerkiksi käyttämällä esineiden internetin (IIoT) -teknologioita, jotta voidaan luoda valmistusjärjestelmiä, jotka eivät ole vain yhteydessä toisiinsa, mutta kommunikoivat keskenään paremmin sekä pystyvät analysoimaan ja käyttämään informaatiota fyysisen maailmaan sijoittuviin älykkäisiin ohjaus menetelmiin. Tällainen edistys edellyttää perinteisten massatuotannon valmistus paradigmojen siirtymistä monimutkaisempiin ja monipuolisempiin tuotantoteknologia-alueisiin, jotka koskevat massaräätälöintiä ja tehostettua tuotteiden eriyttämistä, modifiointia ja innovaatioita. Ainetta lisäävät valmistus menetelmät ovat nousseet esiin luotettavana vaihtoehtona tavanomaisille tuotantomenetelmille, mukaan lukien ainetta vähentävät menetelmät, johtuen usein kyseisten menetelmien ennennäkemättömistä muotoilun vapauksista ja monipuolisuudesta pitkälle räätälöityjen tuotteiden valmistuksessa. Lisäainevalmistusteknologioiden onnistunut ottaminen käyttöön valtavirran tuotannossa edellyttää kuitenkin lisäaine valmistusjärjestelmän mallintamista ja simulointia kokonaisuudessaan sen muotoilun, toiminnan ja käytön simuloimiseksi ja optimoimiseksi, samalla kun saavutetaan halutut tuotantotulokset. Tällä hetkellä mallit, jotka on kehitetty karakterisoimaan eri toimintoja additiivisessa valmistusprosessissa, saavat eri muotoja (esim. analyyttisiä, empiirisiä, fysiikkapohjaisia ja koneoppimismalleja) vaihtelevalla tarkkuudella. Täten, kokonaisvaltainen järjestelmän mallinnus vaatii joukon mallinnuksia yksittäisistä lisäainevalmistus teknologioiden karakterisoinneista. Erilaisten prosessitoimintojen, geometrioiden ja materiaalien yhdistäminen tekee kuitenkin haasteelliseksi tarvittavien osajärjestelmätasojen heterogeenisien mallien kokoamisen kokonaisvaltaiseksi järjestelmämalliksi. Tämän puutteen korjaamiseksi, tämän tutkimuksen tavoitteena on kehittää graafipohjainen metamallinnuskehys tuotesuunnittelun ja valmistusstrategioiden integroimiseksi digitaalisesti kokonaisvaltaisten ja simuloitavien monialaisten metamallien kehittämiseksi. Kehitetty viitekehys tukee 1) eri tiedon muotojen integrointia monialaisten metamallien kehittämiseen, 2) determinististen ja todennäköisyyspohjaisten koneoppimislähestymistapojen soveltamista kehitettyjen metamallien simuloinnin mahdollistamiseksi ja 3) ennustavaa analyysiä ja optimointia simuloimalla kehitettyjä metamalleja, jotta voidaan mahdollistaa suunnittelun ja valmistuksen päätöksenteko. Tämä tutkimus mahdollistaa lisäainevalmistusprosessin syöttö arvojen ja tulosten systeemisen karakterisoinnin olemassa olevan tiedon ja kokeellisen tiedon avulla. Lisäainevalmistusprosessin mallintamista ohjasivat tuotesuunnittelu- ja prosessitiedot, ja sitä tuettiin simulaatiolla päätöksentekoa varten. Tutkimuksen taustalla olevat mallit kattavat kaksi kaupallisesti saatavilla olevaa lisäainevalmistusprosessia. Tämä tutkimus osoittaa, että datalähtöisten ja muiden lähestymistapojen käyttö, joissa hyödynnetään sekä kerättyä dataa että olemassa olevaa tietoa, voi mahdollistaa tarkkojen ja selitettävissä olevien metamallien kehittämisen lisäainevalmistuksen tarkkaan seurantaan ja valvontaan halutun tuotteen laadun varmistamiseksi.Modelling and simulation can play a significant role in enhancing the understanding of a complex manufacturing system and its operation. With the advent of Industry 4.0, there is a need to integrate advanced manufacturing processes, e.g., using industrial internet of things (IIoT) technologies, to create manufacturing systems that are not only interconnected, but communicate better and can analyse and use information to drive intelligent action into the physical world. Such progress requires traditional manufacturing paradigms of mass production to move into more complex and diverse production technology domains of mass customization and enhanced product differentiation, modification, and innovation. Additive manufacturing has emerged as a reliable alternative to conventional manufacturing, including subtractive processes, often attributed to its claim for unprecedented design freedom and versatility for the production of highly customized products. However, for successful adoption of additive technologies into mainstream production, modelling and simulation of the manufacturing system in the entirety of its complexity is required to simulate and optimize its design, operation, and use, while achieving desirable production outcomes. At present, models developed to characterize the various activities in an additive manufacturing process take different forms (e.g., analytical, empirical, physics-based, and machine learning models) at varying levels of granularity. Thus, holistic system modelling requires an array of heterogenous models for characterizing a single additive manufacturing technology. However, the inclusion of different process activities, geometries, and materials makes it a challenge to compose the necessary subsystem-level heterogenous models into a holistic system model. To address this gap, this research aims to develop a graph-based metamodelling framework for digitally integrating the product design and manufacturing strategies to develop holistic and simulatable multi-domain metamodels. The developed framework supports 1) integration of different forms of knowledge to develop multi-domain metamodels, 2) application of deterministic and probabilistic machine learning approaches to enable simulation of developed metamodels, and 3) predictive analysis and optimization through simulation of the developed metamodels to enable design and manufacturing decision making. This research enables systemic characterization of additive manufacturing process inputs and outputs using pre-existing knowledge and experimental data. Additive manufacturing process modelling was driven by product design and process data/information, and supported by simulation for decision making. Underpinning models within the research encompass two commercially available additive manufacturing processes. This research demonstrates that the use of data-driven and other approaches that utilize both collected data and pre-existing knowledge can enable the development of accurate and explainable metamodels for close monitoring and control of additive manufacturing to ensure desirable product quality

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Welding Processes

Despite the wide availability of literature on welding processes, a need exists to regularly update the engineering community on advancements in joining techniques of similar and dissimilar materials, in their numerical modeling, as well as in their sensing and control. In response to InTech's request to provide undergraduate and graduate students, welding engineers, and researchers with updates on recent achievements in welding, a group of 34 authors and co-authors from 14 countries representing five continents have joined to co-author this book on welding processes, free of charge to the reader. This book is divided into four sections: Laser Welding; Numerical Modeling of Welding Processes; Sensing of Welding Processes; and General Topics in Welding

Argonne National Laboratory annual report of laboratory directed research and development program activities for FY 1995.

The purposes of Argonne's Laboratory Directed Research and Development (LDRD) Program are to encourage the development of novel concepts, enhance the Laboratory's R&D capabilities, and further the development of its strategic initiatives

Current Air Quality Issues

Air pollution is thus far one of the key environmental issues in urban areas. Comprehensive air quality plans are required to manage air pollution for a particular area. Consequently, air should be continuously sampled, monitored, and modeled to examine different action plans. Reviews and research papers describe air pollution in five main contexts: Monitoring, Modeling, Risk Assessment, Health, and Indoor Air Pollution. The book is recommended to experts interested in health and air pollution issues