Model based design (MBD) is a systems engineering approach wherein multi-dimensional models are designed to simulate, analyze and test actual systems. It is a mathematical and intuitive methodology that helps to solve complex problems related to design, communication, signal processing, control systems, software development and more. It is used to model embedded systems in industrial equipment, motion control in autonomous systems, aerospace navigation and automotive, medical systems among many more. MBD is a process that enables rapid and more financially viable development of multi-modal frameworks, including control, signal management, and embedded architectures. In model-driven planning, the framework model is at the heart of progressive interaction by improving compliance to the prerequisites of planning, execution, and testing. The current study presents a brief outlook of the important features, benefits, applications and implementation practices of MBD.

Model Based Design in Industrial Automation

Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

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A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

Model based design (MBD) is a systems engineering approach wherein multi-dimensional models are designed to simulate, analyze and test actual systems. It is a mathematical and intuitive methodology that helps to solve complex problems related to design, communication, signal processing, control systems, software development and more. It is used to model embedded systems in industrial equipment, motion control in autonomous systems, aerospace navigation and automotive, medical systems among many more. MBD is a process that enables rapid and more financially viable development of multi-modal frameworks, including control, signal management, and embedded architectures. In model-driven planning, the framework model is at the heart of progressive interaction by improving compliance to the prerequisites of planning, execution, and testing. The current study presents a brief outlook of the important features, benefits, applications and implementation practices of MBD. 

Technology is advancing day by day and with its relentless progress, the industry is also constantly evolving to meet new-age customer expectations with higher efficiency and sustainability. Automation and robots existed during industry 3.0 as well but now they are being connected in networks for the ease of managing, monitoring and controlling the manufacturing plants. Online / remote location based control and machine to machine communications are but few of the infinite possibilities opened up in the fourth industrial revolution. Industry 4.0 doesn't refer to a single technology/process, rather it is a seamless physical-digital integration of multiple cutting edge technologies. This paper presents a short survey covering important industry 4.0 technologies such as autonomous mobile robots (AMR), internet of robotic things (IoRT), block chain, artificial intelligence (AI), cyber security, big data analytics and machine learning (ML). This paper also presents important research scopes and directions identified for each of these technologies towards wider implementations of industry 4.0 architectures. 

Survey of Technologies for Industry 4.0

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated. 

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Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Automated and intelligent classification of defects can improve productivity, quality, and safety of various welded components used in industries. This study presents a transfer learning approach for accurate classification of tungsten inert gas (TIG) welding defects while joining stainless steel parts. In this approach, eight pre-trained deep learning models (VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and DenseNet169) were explored to classify welding images into two-class (good weld/bad weld) and multi-class (good weld/burn through/contamination/lack of fusion/lack of shielding gas/high travel speed) classifications. Moreover, four optimizers (SGD, Adam, Adagrad, and Rmsprop) were applied separately to each of the deep learning models to maximize prediction accuracies. All models were evaluated based on testing accuracy, precision, recall, F1 scores, training/validation losses, and accuracies over successive training epochs. Primary results show that the VGG19-SGD and DenseNet169-SGD architectures attained the best testing accuracies for two-class (99.69%) and multi-class (97.28%) defects classifications, respectively. For “burn through,” “contamination,” and “high travel speed” defects, most deep learning models ensured productivity over quality assurance of TIG welded joints. On the other hand, the weld quality was promoted over productivity during classification of “lack of fusion” and “lack of shielding gas” defects. Thus, transfer learning methodology can help boost productivity and quality of welded joints by accurate classification of good and bad welds.

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Deepak Sharma

Papers

Intelligent Classification of Tungsten Inert Gas Welding Defects: A Transfer Learning Approach