Optimisation of welding parameters to mitigate the effect of residual stress on the fatigue life of nozzle–shell welded joints in cylindrical pressure vessels.

Doctoral Degree. University of KwaZulu-Natal, Durban.The process of welding steel structures inadvertently causes residual stress as a result of thermal cycles that the material is subjected to. These welding-induced residual stresses have been shown to be responsible for a number of catastrophic failures in critical infrastructure installations such as pressure vessels, ship’s hulls, steel roof structures, and others. The present study examines the relationship between welding input parameters and the resultant residual stress, fatigue properties, weld bead geometry and mechanical properties of welded carbon steel pressure vessels. The study focuses on circumferential nozzle-to-shell welds, which have not been studied to this extent until now. A hybrid methodology including experimentation, numerical analysis, and mathematical modelling is employed to map out the relationship between welding input parameters and the output weld characteristics in order to further optimize the input parameters to produce an optimal welded joint whose stress and fatigue characteristics enhance service life of the welded structure. The results of a series of experiments performed show that the mechanical properties such as hardness are significantly affected by the welding process parameters and thereby affect the service life of a welded pressure vessel. The weld geometry is also affected by the input parameters of the welding process such that bead width and bead depth will vary depending on the parametric combination of input variables. The fatigue properties of a welded pressure vessel structure are affected by the residual stress conditions of the structure. The fractional factorial design technique shows that the welding current (I) and voltage (V) are statistically significant controlling parameters in the welding process. The results of the neutron diffraction (ND) tests reveal that there is a high concentration of residual stresses close to the weld centre-line. These stresses subside with increasing distance from the centre-line. The resultant hoop residual stress distribution shows that the hoop stresses are highly tensile close to the weld centre-line, decrease in magnitude as the distance from the weld centre-line increases, then decrease back to zero before changing direction to compressive further away from the weld centre-line. The hoop stress distribution profile on the flange side is similar to that of the pipe side around the circumferential weld, and the residual stress peak values are equal to or higher than the yield strength of the filler material. The weld specimens failed at the weld toe where the hoop stress was generally highly tensile in most of the welded specimens. The multiobjective genetic algorithm is successfully used to produce a set of optimal solutions that are in agreement with values obtained during experiments. The 3D finite element model produced using MSC Marc software is generally comparable to physical experimentation. The results obtained in the present study are in agreement with similar studies reported in the literature

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

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

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

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

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations