Identification of key GMAW fillet weld parameters and interactions using artificial neural networks

Fillet welds are one of the most commonly used weld joints but one of the most difficult to weld consistently. This paper presents a technique using Artificial Neural Networks (ANN) to identify the key Gas Metal Arc Welding (GMAW) fillet weld parameters and interactions that impact on the resultant geometry, when using a metal cored wire. The input parameters to the model were current, voltage, travel speed; gun angle and travel angle and the outputs of the model were penetration and leg length. The model was in good agreement with experimental data collected and the subsequent sensitivity analysis showed that current was the most influential parameter in determining penetration and that travel speed, followed closely by current and voltage were most influential in determining the leg length. The paper also concludes that a ‘pushing’ travel angle is preferred when trying to control the resultant geometry mainly because both the resultant leg length and penetration appear to be less sensitive to changes in heat input

Using artificial neural networks to identify and optimise the key parameters affecting geometry of a GMAW fillet weld

Control of Gas Metal Arc Welding (GMAW) parameters is key to maintaining good quality and consistent fillet weld geometry. The external geometry of the fillet weld can be easily measured, however the internal geometry (i.e. penetration), which is critical in determining the structural integrity of the joint, is difficult to measure without destructively testing the workpiece. Consequently the most cost effective way to ensure adequate penetration is to maintain close control of the input parameters. Furthermore if we can demonstrate tight control of the parameters and interactions that affect the joint penetration then we can increase the confidence that sufficient penetration is being achieved.Previous studies have shown that the variation in set up parameters between welders and the guidance given by industry/suppliers can vary widely and in some cases be contradictory. Also in practice there are several characteristics of the manual/semi-automatic GMAW fillet weld process that are difficult to control (e.g. gun angle, travel angle and gap) but yet have an impact on the resultant geometry.This paper will document a programme of work which has used an Artificial Neural Network (ANN) to identify the parameters, and specific interactions that have an impact on the resultant fillet weld geometry. The variables that will be assessed in this paper will include current, voltage, travel speed, gun angle, travel angle. Further follow on studies will take place to understand the impact of gap, gas flow & nozzle diameters

A Neuro-Expert Approach for Decision -Making in Welding Environment.

Decision making in welding is very important for achieving a good quality welded joint for the least possible cost. Of particular interest is decision making involving the selection of process, parameters, weld procedure specification, defect analysis and trouble shooting. This research has provided a means of capturing the planning knowledge in a Neuro-Expert System in a form that is capable of learning new information, correcting old information and automating the decision-making process in a welding environment. A strategy is formulated for the representation of knowledge in the form of a neural links and the translation of rules into neural link weights. After training those weights were converted back into rules to find out the inconsistent rules and capture new rules using a new approach. The various job variables affecting the process of welding are identified in detail and a Neuro-Expert system for the selection of process, parameters and weld procedure specification is developed. The neural networks are integrated with an expert system for decision making in welding environment. Apart from providing the initial parameters of welding, the expert system is used to validate the output of the neural network and served as a user-friendly interface for the neural network. Defect Analysis is performed in welding domain by mapping the welding parameters and defect patterns in a neural network. A neural network based approach for representing the knowledge in expert system is utilized for this purpose as the modification and updating of the knowledge was easier

Two factor authentication by using SMS for web based application

Two factor authentications had two entry levels for authentication’s mechanism. This study had used Short Messaging Service (SMS) technology as a second factor in doing authentication on the web application. It can minimize the problems that occur regarding to the illegal access over the user privacy information on the Internet. The system that uses two factor authentications is capable to give the priority and safety in aspect of user’s privacy information from the web based applications

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

E-design tools for friction stir welding: cost estimation tool

A cost model is an important tool for product design and material selection. An efficient and effective cost estimation tool is necessary for early design evaluation. In this paper, cost estimation models that estimate the production cost for Metal Inert Gas (MIG), Friction Stir (FS), and Friction Stir Spot (FSS) welded joints are presented. These models determine the cost incurred to fabricate each joint along with a detailed explanation of each cost component. Each cost component has been closely analyzed and major cost components have been included in the cost model --Abstract, page iv

Novel Optimization Methodology for Welding Process/Consumable Integration

Advanced materials are being developed to improve the energy efficiency of many industries of future including steel, mining, and chemical, as well as, US infrastructures including bridges, pipelines and buildings. Effective deployment of these materials is highly dependent upon the development of arc welding technology. Traditional welding technology development is slow and often involves expensive and time-consuming trial and error experimentation. The reason for this is the lack of useful predictive tools that enable welding technology development to keep pace with the deployment of new materials in various industrial sectors. Literature reviews showed two kinds of modeling activities. Academic and national laboratory efforts focus on developing integrated weld process models by employing the detailed scientific methodologies. However, these models are cumbersome and not easy to use. Therefore, these scientific models have limited application in real-world industrial conditions. On the other hand, industrial users have relied on simple predictive models based on analytical and empirical equations to drive their product development. The scopes of these simple models are limited. In this research, attempts were made to bridge this gap and provide the industry with a computational tool that combines the advantages of both approaches. This research resulted in the development of predictive tools which facilitate the development of optimized welding processes and consumables. The work demonstrated that it is possible to develop hybrid integrated models for relating the weld metal composition and process parameters to the performance of welds. In addition, these tools can be deployed for industrial users through user friendly graphical interface. In principle, the welding industry users can use these modular tools to guide their welding process parameter and consumable composition selection. It is hypothesized that by expanding these tools throughout welding industry, substantial energy savings can be made. Savings are expected to be even greater in the case of new steels, which will require extensive mapping over large experimental ranges of parameters such as voltage, current, speed, heat input and pre-heat

Intelligent 3D seam tracking and adaptable weld process control for robotic TIG welding

Tungsten Inert Gas (TIG) welding is extensively used in aerospace applications, due to its unique ability to produce higher quality welds compared to other shielded arc welding types. However, most TIG welding is performed manually and has not achieved the levels of automation that other welding techniques have. This is mostly attributed to the lack of process knowledge and adaptability to complexities, such as mismatches due to part fit-up. Recent advances in automation have enabled the use of industrial robots for complex tasks that require intelligent decision making, predominantly through sensors. Applications such as TIG welding of aerospace components require tight tolerances and need intelligent decision making capability to accommodate any unexpected variation and to carry out welding of complex geometries. Such decision making procedures must be based on the feedback about the weld profile geometry. In this thesis, a real-time position based closed loop system was developed with a six axis industrial robot (KUKA KR 16) and a laser triangulation based sensor (Micro-Epsilon Scan control 2900-25). [Continues.