Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding

Elektrisches Widerstandsschweißen (Englisch: Electric Resistance Welding, ERW) ist eine Gruppe von vollautomatisierten Fertigungsprozessen, bei denen metallische Werkstoffe durch Wärme verbunden werden, die von elektrischem Strom und Widerstand erzeugt wird. Eine genaue Qualitätsüberwachung von ERW kann oft nur teilweise mit destruktiven Methoden durchgeführt werden. Es besteht ein großes industrielles und wirtschaftliches Potenzial, datengetriebene Ansätze für die Qualitätsüberwachung in ERW zu entwickeln, um die Wartungskosten zu senken und die Qualitätskontrolle zu verbessern. Datengetriebene Ansätze wie maschinelles Lernen (ML) haben aufgrund der enormen Menge verfügbarer Daten, die von Technologien der Industrie 4.0 bereitgestellt werden, viel Aufmerksamkeit auf sich gezogen. Datengetriebene Ansätze ermöglichen eine zerstörungsfreie, umfassende und präzise Qualitätsüberwachung, wenn eine bestimmte Menge präziser Daten verfügbar ist. Dies kann eine umfassende Online-Qualitätsüberwachung ermöglichen, die ansonsten mit herkömmlichen empirischen Methoden äußerst schwierig ist. Es gibt jedoch noch viele Herausforderungen bei der Adoption solcher Ansätze in der Fertigungsindustrie. Zu diesen Herausforderungen gehören: effiziente Datensammlung, die dasWissen von erforderlichen Datenmengen und relevanten Sensoren für erfolgreiches maschinelles Lernen verlangt; das anspruchsvolle Verstehen von komplexen Prozessen und facettenreichen Daten; eine geschickte Selektion geeigneter ML-Methoden und die Integration von Domänenwissen für die prädiktive Qualitätsüberwachung mit inhomogenen Datenstrukturen, usw. Bestehende ML-Lösungen für ERW liefern keine systematische Vorgehensweise für die Methodenauswahl. Jeder Prozess der ML-Entwicklung erfordert ein umfassendes Prozess- und Datenverständnis und ist auf ein bestimmtes Szenario zugeschnitten, das schwer zu verallgemeinern ist. Es existieren semantische Lösungen für das Prozess- und Datenverständnis und Datenmanagement. Diese betrachten die Datenanalyse als eine isolierte Phase. Sie liefern keine Systemlösungen für das Prozess- und Datenverständnis, die Datenaufbereitung und die ML-Verbesserung, die konfigurierbare und verallgemeinerbare Lösungen für maschinelles Lernen ermöglichen. Diese Arbeit versucht, die obengenannten Herausforderungen zu adressieren, indem ein Framework für maschinelles Lernen für ERW vorgeschlagen wird, und demonstriert fünf industrielle Anwendungsfälle, die das Framework anwenden und validieren. Das Framework überprüft die Fragen und Datenspezifitäten, schlägt eine simulationsunterstützte Datenerfassung vor und erörtert Methoden des maschinellen Lernens, die in zwei Gruppen unterteilt sind: Feature Engineering und Feature Learning. Das Framework basiert auf semantischen Technologien, die eine standardisierte Prozess- und Datenbeschreibung, eine Ontologie-bewusste Datenaufbereitung sowie halbautomatisierte und Nutzer-konfigurierbare ML-Lösungen ermöglichen. Diese Arbeit demonstriert außerdem die Übertragbarkeit des Frameworks auf einen hochpräzisen Laserprozess. Diese Arbeit ist ein Beginn des Wegs zur intelligenten Fertigung von ERW, der mit dem Trend der vierten industriellen Revolution korrespondiert

Development of an Intelligent On-Line Monitoring System Based on ANFIS Algorithm for Resistance Spot Welding Process

This paper presents an on-line quality assessment model based on Adaptive Neuro-Fuzzy Inference System (ANFIS).The ANFIS model is realized for identifying the RSW dynamical system based on given input output data. As a special neural network, ANFIS can approximate all nonlinear systems with less training data, quicker learning speed and higher precision.In this study, a system for monitoring various signals which provide real-time information of nugget formation and growth for RSW is established, and a series of experiments are conducted to research the correlation between these signals and weld quality. These signals include welding current, welding time and dynamic resistance. A set of dynamic resistance patterns are grouped based on their corresponding weld nugget quality, and were selected as the input data to train the proposed ANFIS model. Once the monitoring system had been trained, it was then tested to evaluate its efficiency and validity.The classifier based on ANFIS algorithm indicates the fast classification, showing a total success rate of 82.1 per cent for test data. Keywords: ANFIS, dynamic resistance, resistance spot welding

Real-time Automated Weld Quality Analysis From Ultrasonic B-scan Using Deep Learning

Resistance spot welding is a widely used process for joining metals using electrically generated heat or Joule heating. It is one of the most commonly used techniques in automotive industry to weld sheet metals in order to form a car body. Although, industrial robots are used as automated spot welders in massive scale in the industries, the weld quality inspection process still requires human involvement to decide if a weld should be passed as acceptable or not. Not only it is a tedious and error- prone job, but also it costs industries lots of time and money. Therefore, making this process automated and real-time will have high significance in spot welding as well as the field of Non-destructive Testing (NDT). Research team in Institute of Diagnostic Imaging Research (IDIR) have developed technology to obtain grey-scale 2D images called ultrasonic b-scans in real-time during production in order to visualize the weld development with respect to time. They have demonstrated that by extracting and interpreting relevant patterns from these b-scans, weld quality can be determined accurately. However, current works combining conventional image and signal processing techniques are unable to extract those patterns from a wide variety of weld shapes with production-level satisfaction. Therefore, in this thesis, we propose to apply SSD, a single-shot multi-box detection based deep convolutional neural network framework for real-time embedded detection of components of cross-sectional weld shape from ultrasonic b-scans and interpret them to numeric parameters which are used as features to classify welds as good, bad or acceptable in real-time. Our proposed model has showed significant improvement in deciding weld quality compared to existing methods when tested on real industry facility

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

Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art

Information contributes to the improvement of decision-making, process improvement, error detection, and prevention. The new requirements of the coming Industry 4.0 will make these new information technologies help in the improvement and decision-making of industrial processes. In case of the welding processes, several techniques have been used. Welding processes can be analyzed as a stochastic system with several inputs and outputs. This allows a study with a data analysis perspective. Data mining processes, machine learning, deep learning, and reinforcement learning techniques have had good results in the analysis and control of systems as complex as the welding process. The increase of information acquisition and information quality by sensors developed at present, allows a large volume of data that benefits the analysis of these techniques. This research aims to make a bibliographic analysis of the techniques used in the welding area, the advantages that these new techniques can provide, and how some researchers are already using them. The chapter is organized according to some stages of the data mining process. This was defined with the objective of highlighting evolution and potential for each stage for welding processes

INTELLIGENT SPOT WELDING QUALITY MONITORING USING ADVANCED SIGNAL PROCESSING TECHNIQUES

Master'sMASTER OF ENGINEERIN

Algorithms on determining the correlation laws between ultrasonic images and quality of spot welds.

Conventional quality control devices for spot welding cannot perform on-line inspection and provide feedback to the welding control system. In this way, the traditional quality control systems are similar to statistical welding parameters monitoring systems. It is imperative to combine the idea of on-line quality inspection with closed-loop feedback control in a robust control system. However, there is no single acoustic method to date capable of manipulating real-time control and on-line quality inspection, concurrently, since specific procedures (e.g. scanning time and adjustment time) need to be adopted by traditional acoustic microscopes to retrieve proper information, and these procedures tend to disable the real-time and on-line capability of acoustic microscopy. With recent hardware improvements, the novel portable acoustic device is able to reduce the scanning time to real-time fashion without losing any significant data. On the other hand, the adjustment time of the portable acoustic device can be reduced noticeably by employing intelligent control software instead of human operators. This new hardware-software configuration will be an ideal approach to the on-line, real-time nondestructive inspection of spot welds. The primary goal of this research is to develop an intelligent system to accomplish the on-line, real-time nondestructive inspection for spot welds. The following objectives were fulfilled to reach the final goal. (1) Classification of the acoustic images of spot welds. (2) Quantification of acoustic information as parameters. (3) The study of the influence of each parameter on the strength of spot welds. (4) Identification of important and significant parameters. (5) Integration of these parameters into the knowledge base of the software. The system developed can be an on-line advisor that is capable of providing critical information about the quality of spot welds during the process. Furthermore, this system is able to render warning signals to the process control unit to prevent further mistakes.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .L33. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1132. Advisers: Roman Maev; Michael Wang. Thesis (Ph.D.)--University of Windsor (Canada), 1999

Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis

In this work, several of the most popular and state-of-the-art classification methods are compared as pattern recognition tools for classification of resistance spot welding joints. Instead of using the result of a non-destructive testing technique as input variables, classifiers are trained directly with the relevant welding parameters, i.e. welding current, welding time and the type of electrode (electrode material and treatment). The algorithms are compared in terms of accuracy and area under the receiver operating characteristic (ROC) curve metrics, using nested cross-validation. Results show that although there is not a dominant classifier for every specificity/sensitivity requirement, support vector machines using radial kernel, boosting and random forest techniques obtain the best performance overallSpanish MICINN Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and by the Junta de Castilla y León GREX251-200

Quality analysis modelling for development of a process controller in resistance spot welding using neural networks techniques

Student Number : 9811923K - PhD thesis - School of Mechanical Engineering - Faculty of Engineering and the Built EnvironmentMethods are presented for obtaining models used for predicting welded sample resistance and effective weld current (RMS) for desired weld diameter (weld quality) in the resistance spot welding process. These models were used to design predictive controllers for the welding process. A suitable process model forms an important step in the development and design of process controllers for achieving good weld quality with good reproducibility. Effective current, dynamic resistance and applied electrode force are identified as important input parameters necessary to predict the output weld diameter. These input parameters are used for the process model and design of a predictive controller. A three parameter empirical model with dependent and independent variables was used for curve fitting the nonlinear halfwave dynamic resistance. The estimates of the parameters were used to develop charts for determining overall resistance of samples for any desired weld diameter. Estimating resistance for samples welded in the machines from which dataset obtained were used to plot the chart yielded accurate results. However using these charts to estimate sample resistance for new and unknown machines yielded high estimation error. To improve the prediction accuracy the same set of data generated from the model were used to train four different neural network types. These were the Generalised Feed Forward (GFF) neural network, Multilayer Perceptron (MLP) network, Radial Basis Function (RBF) and Recurrent neural network (RNN). Of the four network types trained, the MLP had the least mean square error for training and cross validation of 0.00037 and 0.00039 respectively with linear correlation coefficient in testing of 0.999 and maximum estimation error range from 0.1% to 3%. A prediction accuracy of about 97% to 99.9%. This model was selected for the design and implementation of the controller for predicting overall sample resistance. Using this predicted overall sample resistance, and applied electrode force, a second model was developed for predicting required effective weld current for any desired weld diameter. The prediction accuracy of this model was in the range of 94% to 99%. The neural network predictive controller was designed using the MLP neural network models. The controller outputs effective current for any desired weld diameter and is observed to track the desired output accurately with same prediction accuracy of the model used which was about 94% to 99%. The controller works by utilizing the neural network output embedded in Microsoft Excel as a digital link library and is able to generate outputs for given inputs on activating the process by the push of a command button