NONLINEAR SYSTEM MODELING UTILIZING NEURAL NETWORKS: AN APPLICATION TO THE DOUBLE SIDED ARC WELDING PROCESS

The need and desire to create robust and accurate joining of materials has been one of up most importance throughout the course of history. Many forms have often been employed, but none exhibit the strength or durability as the weld. This study endeavors to explore some of the aspects of welding, more specifically relating to the Double Sided Arc Welding process and how best to model the dynamic non-linear response of such a system. Concepts of the Volterra series, NARMAX approximation and neural networks are explored. Fundamental methods of the neural network, including radial basis functions, and Back-propagation are investigated

WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK

Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms. A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern is captured which contains all the information about the penetration status. An experienced welder is able to determine weld penetration status just based on the reflected laser pattern. However, it is difficult to characterize the images to extract key information that used to determine penetration status. To overcome the challenges in finding right features and accurately processing images to extract key features using conventional machine vision algorithms, we propose using convolutional neural network (CNN) to automatically extract key features and determine penetration status. Data-label pairs are needed to train a CNN. Therefore, an image acquiring system is designed to collect reflected laser pattern and the image of work-piece backside. Data augmentation is performed to enlarge the training data size, which resulting in 270,000 training data, 45,000 validation data and 45,000 test data. A six-layer convolutional neural network (CNN) has been designed and trained using a revised mini-batch gradient descent optimizer. Final test accuracy is 90.7% and using a voting mechanism based on three consequent images further improve the prediction accuracy

Friction modeling of Al-Mg alloy sheets based on multiple regression analysis and neural networks

This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables the determination of the friction coefficient value under a wide range of friction conditions, without performing time-consuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors that affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks, the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.publishedVersio

Process control for WAAM using computer vision

This study is mainly about the vision system and control algorithm programming for wire arc additive manufacturing (WAAM). Arc additive manufacturing technology is formed by the principle of heat source cladding produced by welders using molten inert gas shielded welding (MIG), tungsten inert gas shielded welding (TIG) and layered plasma welding power supply (PA). It has high deposition efficiency, short manufacturing cycle, low cost, and easy maintenance. Although WAAM has very good uses in various fields, the inability to control the adding process in real time has led to defects in the weld and reduced quality. Therefore, it is necessary to develop the real-time feedback through computer vision and algorithms for WAAM to ensure that the thickness and the width of each layer during the addition process are the same

An investigation on relationship between process control parameters and weld penetration in robotic CO2 arc welding using factorial design approach

Weld penetration is an important physical characteristic of a weldment that affects the stress carrying capacity of the weld joint. Several welding parameters seem to influence weld penetration. This paper presents the relationship between weld penetration and four direct welding process parameters of robotic CO2 arc welding process on structural carbon steel. Two level, full factorial design was applied to investigate and quantify the direct and interactive effects of four process parameters on weld penetration. The upper and lower limits of the process control variables were identified through trial and error methodology, and the experiments were conducted using ‘bead on plate’ mode. The performance of the model was then tested by using analysis of variance technique and the significance of the coefficients was tested by applying student’s‘t’ test. Commercial computer programs were used for statistical analysis. The main and interactive effects of different welding parameters are studied by presenting it in graphical form

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

In Situ Process Monitoring and Machine Learning Based Modeling of Defects and Anomalies in Wire-Arc Additive Manufacturing

Wire Arc Additive Manufacturing (WAAM) has made great strides in recent years however, there remain numerous persistent challenges still hindering more widespread adoption. Defects in the parts produced degrade their mechanical performance. Inconsistency in the geometry of the weld beads or undesirable anomalies such as waviness, or humps can lead to loss of geometric accuracy and in extreme cases, when anomalies propagate to subsequent layers, build failure. Such defects can be mitigated by a controls framework, which would require a model that maps undesirable outcomes to information about the process that can be obtained in real time. This thesis explores the development of a multi-sensor framework for real time data acquisition and several approaches for arriving at such a model, employing well known machine learning methodologies including Random Forests, Artificial Neural Networks and Long Short Term Memory. The merits and drawbacks of these methods is discussed, and a physics based approach intended to mitigate some of the drawbacks is explored. The models are trained first on data obtained on a single build layer, and subsequently on a multi-layer wall

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