Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Abstract

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs

“…Advanced technologies like AI and CV are also employed for inspection, such as: using machine vision for spur gears parameters measurement [11], using CV to detect gear tooth number [12], using artificial vision for quality control of spur gears [13], inspection of gear faults using support vector machines (SVMs) and artificial neural networks (ANNs) [14], determining fine-pitch gears centers using machine vision [15], gear faults with convolutional neural networks (CNNs) [16], gears diagnosis using CNNs [17] and inspection of plastic gears using ANN and SVM based method [18]. AI and CV are also used for other AI inspection related application like: dimensions inspection with machine vision [19], detection of defects in products [20], sugarcane varieties inspection [21], welding inspection [22] inspection of optical laser welding [23] and inspection of aerospace components [24]. Vibration signals were the source information in most of the gears related literature mentioned above.…”

Determination of Optimal Flat-End Head Geometries for Pressure Vessels Based on Numerical and Experimental Approaches

“…When the tubes are subjected to relatively high pressures, the calculated wall thickness becomes rather big, and the use of flat ends seems to be also more rational and justified, due to their relatively simple form. Independently from the shape, all these ends are joined with the tubular parts of tanks through welding, which is a well-recognized and automatized technology nowadays [ 24 , 25 , 26 ].…”

تأثير العوامل البيئية على دقة نظام فحص جودة مبني على التعلم بالنقل

“…The importance of using DL with CV on the other hand, has arisen due to several qualities possessed by DL, in which it upscaled traditional CV systems and made it more sophisticated and easier to implement and design. Machine vision with deep learning have been used for: defects detection in products [2], inspection of sugarcane varieties [3], process monitoring and quality control [4], welding inspection [5] optical laser welding inspection [6], laser welding defects detection [7] and gear faults diagnosis with convolutional neural networks (CNNs) [8]. As DL depends on deep neural networks to extract the necessary features from the data automatically, in contrast with traditional CV techniques where manual features extraction is mandatory [9].…”