Knowledge discovery for friction stir welding via data driven approaches: Part 1 – correlation analyses of internal process variables and weld quality

For a comprehensive understanding towards Friction Stir Welding (FSW) which would lead to a unified approach that embodies materials other than aluminium, such as titanium and steel, it is crucial to identify the intricate correlations between the controllable process conditions, the observable internal process variables, and the characterisations of the post-weld materials. In Part I of this paper, multiple correlation analyses techniques have been developed to detect new and previously unknown correlations between the internal process variables and weld quality of aluminium alloy AA5083. Furthermore, a new exploitable weld quality indicator has, for the first time, been successfully extracted, which can provide an accurate and reliable indication of the ‘as-welded’ defects. All results relating to this work have been validated using real data obtained from a series of welding trials that utilised a new revolutionary sensory platform called ARTEMIS developed by TWI Ltd., the original inventors of the FSW process

Artificial Neural Network Prediction of Aluminium Metal Matrix Composite with Silicon Carbide Particles Developed Using Stir Casting Method

Aluminium matrix composites (AMCs) are range of advanced engineering materials used for a wide range of applications. AMCs consist of a non-metallic reinforcement incorporated into Aluminium matrix providing advantageous properties over base metal alloys.In this paper, artificial neural network (ANN) is used to predict the micro-hardness, yield strength, tensile extension, modulus, ultimate tensile strength and stress, time to fracture, load at maximum extension, tenacity, electrical resistivity and conductivity. Information obtained from ANN model predictions can be used as guidelines during the conceptual design and optimisation of manufacturing processes; thus, reducing time and costs

Artificial Neural Network Prediction of Aluminium Metal Matrix Composite with Silicon Carbide Particles Developed Using Stir Casting Method

Aluminium matrix composites (AMCs) are range of advanced engineering materials used for a wide range of applications. AMCs consist of a non-metallic reinforcement incorporated into Aluminium matrix providing advantageous properties over base metal alloys. In this paper, artificial neural network (ANN) is used to predict the micro-hardness, yield strength, tensile extension, modulus, ultimate tensile strength and stress, time to fracture, load at maximum extension, tenacity, electrical resistivity and conductivity. Information obtained from ANN model predictions can be used as guidelines during the conceptual design and optimisation of manufacturing processes; thus, reducing time and costs

Monitoramento do processo FSW empregando espectrogramas para os sinais das forças e do torque da ferramenta para a liga AA5052-H32

Orientadores: Alberto Luiz Serpa, Antonio Jose RamirezTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Este trabalho procura identificar imperfeções durante um processo de soldagem por fricção (FSW) usando o espectrograma da Transformada de Fourier de Curto Tempo (STFT) dos sinais da forças da ferramenta e do torque da ferramenta. Esta técnica foi considerada também uma fonte de informação sobre a estabilidade da soldagem durante o processo, especialmente levando em consideração as forças transversais e laterais. Considerando a importância das forças da ferramenta e do torque da ferramenta, um experimento foi executado usando duas chapas AA5052-H32 de ligas alumínio em uma soldagem de topo. O experimento considerou a variação dos três parâmetros operacionais: velocidade de rotação, velocidade transversal e profundidade de penetração. As respostas foram analisadas utilizando os espectrogramas dos sinais e uma análise estatística que mostra os parâmetros operacionais de influência sobre as ocorrências de falhas usando a técnica STFT. O espectrograma foi capaz de reconhecer o instante em que as falhas ocorreram em uma janela de 2 segundos a 100 Hz da taxa de amostragem. Os resultados mostram que não só as forças laterais e transversais possuem informações relevantes no espectro, mas também a força axial e o torque podem contribuir para o reconhecimento de imperfeções. No entanto, analisando o espectro de um único sinal, as falhas podem não ser detectadas. Usando os padrão dos espectogramas, um sistema de lógica difusa foi projetado para indicar a maior probabilidade de falhaAbstract: This research aims to detect voids and cavities defects during a friction stir welding process using the Short Time Fourier Transform (STFT) spectrogram of the tool forces and the spindle torque signals. This technique was considered also a source of information on the welding stability during the process, especially taking into account the traverse and lateral forces. Considering the importance of the tool forces and the spindle torque during the process, an experiment was run by welding two aluminum AA5052-H32 plates nearly in parallel position and not overlapped (butt welding). The experiment considered the variation of the three operational parameters: rotation speed, traverse speed, and penetration depth. The responses were analyzed using the spectrograms of the signals and a statistical analysis showing the influence of the operational parameters over the fault occurrences using the STFT. The spectrogram was able to recognize the instant when the discontinuities occurred in a window of 2 seconds at 100 Hz of the sample rate. The results show that not only the lateral and traverse forces have relevant information in the spectrum, but also the axial force and the spindle torque can contribute for the recognition of imperfections. However, analyzing the spectrum of one single signal, faults may be undetected. A fuzzy logic system was designed to show the major fault probability using the spectogram patternDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Method and Apparatus for In-Process Sensing of Manufacturing Quality

A method for determining the quality of an examined weld joint comprising the steps of providing acoustical data from the examined weld joint, and performing a neural network operation on the acoustical data determine the quality of the examined weld joint produced by a friction weld process. The neural network may be trained by the steps of providing acoustical data and observable data from at least one test weld joint, and training the neural network based on the acoustical data and observable data to form a trained neural network so that the trained neural network is capable of determining the quality of a examined weld joint based on acoustical data from the examined weld joint. In addition, an apparatus having a housing, acoustical sensors mounted therein, and means for mounting the housing on a friction weld device so that the acoustical sensors do not contact the weld joint. The apparatus may sample the acoustical data necessary for the neural network to determine the quality of a weld joint

ANN Modelling to Optimize Manufacturing Process

Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted

Machine Learning and System Identification for Estimation in Physical Systems

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical.The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field.Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research.Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining.In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost.In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and make sure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms

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