139 research outputs found
ESTABLISHING THE FOUNDATION TO ROBOTIZE COMPLEX WELDING PROCESSES THROUGH LEARNING FROM HUMAN WELDERS BASED ON DEEP LEARNING TECHNIQUES
As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF).
This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by robotize a complex welding process. By integrate process sensing, data visualization, deep learning-based modeling and optimization, a complex welding system is established, with the systematic solution to generalize domain-specific knowledge from experienced human welder. Such system can automatically perform complex welding processes that can only be handled by human in the past.
To enhance the system\u27s tracking capabilities, we trained an image segmentation network to offer precise position information. We incorporated a recurrent neural network structure to analyze dynamic variations during welding. Addressing the challenge of human heterogeneity in data collection, we conducted experiments illustrating that even inaccurate datasets can effectively train deep learning models with zero mean error. Fine-tuning the model with a small portion of accurate data further elevates its performance
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
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
Investigation of Laser Clad Bead Geometry to Process Parameter Settings for Effective Parameter Selection, Simulation, and Optimization
Laser cladding is an additive manufacturing technique involving deposition of powdered clad metal in successive 2D layers onto a substrate thereby creating surface coatings with enhanced material properties. Process and shape parameters contribute in defining the geometry of the clad bead; however, due to the highly coupled nature of the process, it is difficult to determine the relationship between parameters. This research predicts such parameters through development of a cognitive artificial intelligence system using artificial neural networks. A robust experimentation design process applying response surface methodology technique is adopted to collect the bead geometry data for various process configurations. Furthermore, the research identifies the extent of contribution of each factor and the impact of their interactions on the model output through ANOVA and sensitivity analysis. Lastly, a K-mean clustering algorithm is incorporated to identify optimal number of clusters present in the collected dataset on the basis of bead shape characteristics
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
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
Machine Learning for Camera-Based Monitoring of Laser Welding Processes
Der zunehmende Einsatz automatisierter Laserschweißprozesse stellt hohe Anforderungen an die Prozessüberwachung. Ziel ist es, eine hohe Fügequalität und eine frühestmögliche Fehlererkennung zu gewährleisten. Durch die Verwendung von Methoden des maschinellen Lernens können kostengünstigere und im Optimalfall bereits vorhandene Sensoren zur Überwachung des gesamten Prozesses eingesetzt werden. In dieser Arbeit werden Methoden aufgezeigt, die mit einer an der Fokussieroptik koaxial zum Laserstrahl integrierten Kamera eine Prozessüberwachung vor, während und nach dem Schweißprozess vornehmen. Zur Veranschaulichung der Methoden wird der Kontaktierungsprozess von Kupferdrähten zur Herstellung von Formspulenwicklungen verwendet. Die vorherige Prozessüberwachung umfasst eine durch ein faltendes neuronales Netz optimierte Bauteillagedetektion. Durch ei ne Formprüfung der detektierten Fügekomponenten können zudem vorverarbeitende Schritte überwacht und die Schweißung fehlerhafter Bauteile vermieden werden. Die prozessbegleitende Überwachung konzentriert sich auf die Erkennung von Spritzern, da diese als Indikator für einen instabilen Prozess dienen. Algorithmen des maschinellen Lernens führen eine semantische Segmentierung durch, die eine klare Unterscheidung zwischen Rauch, Prozesslicht und Materialauswurf ermöglicht. Die Qualitätsbewertung nach dem Prozess beinhaltet die Extraktion von Informationen über Größe und Form der Anbindungsfläche aus dem Kamerabild. Zudem wird ein Verfahren vorgeschlagen, welches anhand eines Kamerabildes mit Methoden des maschinellen Lernens die Höhendaten berechnet. Anhand der Höhenkarte wird eine regelbasierte Qualitätsbewertung der Schweißnähte durchgeführt. Bei allen Algorithmen wird die Integrierbarkeit in industrielle Prozesse berücksichtigt. Hierzu zählen unter anderem eine geringe Datengrundlage, eine begrenzte Inferenzhardware aus der industriellen Fertigung und die Akzeptanz beim Anwender
Towards the Fabrication Strategies for Intelligent Wire Arc Additive Manufacturing of Wire Structures from CAD Input to Finished Product
With the increasing demand for freedom of part design in the industry, additive manufacturing (AM) has become a vital fabrication process for manufacturing metallic workpieces with high geometrical complexity. Among all metal additive manufacturing technologies, wire arc additive manufacturing (WAAM), which uses gas metal arc welding (GMAW), is gaining popularity for rapid prototyping of sizeable metallic workpieces due to its high deposition rate, low processing conditions limit, and environmental friendliness. In recent years, WAAM has been developed synergistically with industrial robotic systems or CNC machining centers, enabling multi-axis free-form deposition in 3D space. On this basis, the current research of WAAM has gradually focused on fabricating strut-based wire structures to enhance its capability of producing low-fidelity workpieces with high spatial complexity. As a typical wire structure, the large-size free-form lattice structure, featuring lightweight, superior energy absorption, and a high strength-weight ratio, has received extensive attention in developing its WAAM fabrication process.
However, there is currently no sophisticated WAAM system commercially available in the industry to implement an automated fabrication process of wire or lattice structures. The challenges faced in depositing wire structures include the lack of methods to effectively identify individual struts in wire structures, 3D slicing algorithms for the whole wire structures, and path planning algorithms to establish reasonable deposition paths for these generated discrete sliced layers. Moreover, the welded area of the struts within the wire structure is relatively small, so the strut forming is more sensitive and more easily affected by the interlayer temperature. Therefore, the control and prediction of strut formation during the fabricating process is still another industry challenge. Simultaneously, there is also an urgent need to improve the processing efficiency of these structures while ensuring the reliability of their forming result
Methods for Quality Monitoring in Ultrasonic Welding of Carbon Fiber Reinforced Polymer Composites
Carbon fiber reinforced composites have been increasingly used in various industrial sectors, especially in the automotive industry. Ultrasonic welding is considered as an effective approach to joining such composites. Reliable weld quality classification and prediction methods are needed to ensure quality and reduce manufacturing costs. However, existing methods have two weaknesses. The first one is that the majority of the existing methods are based on signal feature data extracted from the original experimental time-series data. Feature-based models may not take full advantage of the information contained in the large amounts of time-series data available, even though the models are simple and easy to program. On the other hand, when using experimental time-series data to conduct weld quality monitoring, the data size may be insufficient for training neural network-based methods for quality monitoring or classification. Therefore, a method is needed to augment experimental data while preserving the statistical characteristics of the experimental data. To find reliable quality monitoring models in various situations, this dissertation proposes two neural network models that are respectively applied to feature-based data and full time-series-based data and compares their performances.
The dissertation first investigates the relationship between weld energy and joint performance in ultrasonic welding of carbon fiber reinforced polymer (CFRP) sheets through weld experiments. The weld quality classes for training quality monitoring algorithms are determined from welded joint lap-shear strength and the microstructure of the weld zone. These pre-defined weld quality classes are the output criteria for weld quality monitoring on feature-based models and time-series-based models. For feature- based weld quality monitoring, a simple and efficient feature selection method is first developed to screen the most significant features for classification from multiple weld quality classes. A Bayesian regularized neural network (BRNN) is then demonstrated to be more accurate and robust when classifying weld quality classes in ultrasonic composite welding when using feature-based data as the input than the previously proposed methods of support vector machine (SVM), k-nearest neighbors (kNN), and linear discriminant analysis (LDA).
To address the limited size of experimental data, a Multivariate Monte Carlo (MMC) simulation with copulas approach is proposed to reasonably generate large amounts of time-series process signals for ultrasonic composite welding. With both experimental data and a large quantity of simulated data, a deep convolutional neural network (CNN) is applied to weld quality classification. The CNN model is found to be more accurate and robust, not only under small training data set sizes, but also under large training data set sizes when compared with previously researched classification methods applied in ultrasonic welding.
In conclusion, neural network-based models could achieve high accuracy using feature signals and the full time-series process signals.Ph.D.Manufacturing EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/168232/1/Dissertation_Lei Sun.pd
Real-Time Monitoring of Thermal Processes
In this research, a monitoring system for thermal processes was developed which measures the most critical process phenomena, such as thermal dynamics (peak temperature, heating rate, and cooling rate) and geometric features, in real-time, which can be used for quality assurance and real-time feedback control. Thermal processes are a subset of manufacturing processes that are characterized by heating materials with a concentrated heat source to alter the properties of the materials or join them. Metal additive manufacturing and arc welding processes are considered thermal processes, where the concentrated energy source may be in the form of a laser, electron beam, electric arc, etc. While thermal processes can be used to create complex components without the limitations of traditional manufacturing, process disturbances may cause deviations from expected results.
During thermal processing, geometry and thermal dynamics of the heat affected zone (HAZ) directly influence the quality of the produced products. Therefore, it is critical to have an accurate tool to monitor the geometry and thermal dynamics in real time to better assure the quality of the part. Various sensors are available to measure these properties, though imaging is a common theme among thermal process monitoring. Imaging is an effective technique since it allows for non-contact in-situ measurements. Imaging in different wavelengths can provide different information regarding the HAZ, such as the temperature distribution from infrared (IR) light. While high resolution, and high frame rate geometry measurements from visible light can be monitored directly. Moreover, processing images with machine learning algorithms has also been shown to be capable of predicting porosity and detecting defects in the part being manufactured. Therefore, the monitoring system designed in this research features high dynamic range (HDR) visible light and IR dual camera sensors with a common optical path to monitor the geometry and thermal dynamics, with the potential to implement machine learning to monitor other features in the future. An enclosure was designed to house both sensors with a common optical setup for the sensors to have a similar field of view (FOV).
In this work, the IR sensor was used to create a dataset to predict the temperature distribution of the HAZ with the HDR sensor. From the temperature distribution, thermal dynamics such as peak temperature, cooling rate, heating rate, solidification time, and melting time were calculated in real-time to estimate the material properties of the final part. The HDR sensor was also used to predict the geometry of the deposited material (clad). Using the same sensors, the height and width of the deposition are estimated from the captured images in real-time which are used for deposition geometry control. The geometry prediction algorithm evolved during this work with different algorithms and features used in the measurements to improve the robustness and accuracy of geometry measurements.
To test the effectiveness of the monitoring system, laser heat treatment (LHT) experiments were conducted to initially validate the thermal dynamics measurements. Thermal dynamics were then further validated during laser directed energy deposition (LDED), which was additionally used to validate the geometry measurements of the clad. Moreover, gas metal arc welding (GMAW) experiments were conducted as well to demonstrate the potential for using this system for different energy sources and materials.
The developed dual sensor camera was shown to be capable of capturing images in real-time during thermal processes. Processing the visible-light images allows the geometry of the HAZ to be monitored, while the IR sensor provides its temperature distribution. The system was shown to be robust enough to capture data with multiple materials (stainless steel and nickel-based alloys) and with different energy sources (laser and electric arc). The thermal dynamics measured with this tool have been shown to correlate to the material properties of the produced parts, thus demonstrating the potential to infer the material properties from these measurements. It has also been shown that a cost-effective alternative design using the visible light sensor to predict the temperature distribution with calibrated measurements from a pyrometer may be used for temperature measurements in thermal processes. Therefore, the developed monitoring system is shown to be an effective monitoring and control tool for various thermal processes
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