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Recognition and guidance of initial welding position (IWP) is one of the most important steps of automatic welding process, also a key technology of autonomous welding process. The purpose of this paper is to advance an improved Harris Algorithm and grey scale scanning method (GSCM) to raise the precision of image processing.

Through the configuration of “single camera and double positions,” a new set of image processing algorithms is adopted to extract feature points by using the pattern of rough location and subtle extraction, so as to restructure three‐dimensional information to guide robot move to IWP in the practical welding environment.

Experiments showed that mean square errors (MSEs) in X, Y, Z‐directions for both flat butt joint and flat flange are 0.4491, 0.8178, 1.4797, and 0.5398, 0.4861, 1.1071 mm, respectively.

It has a limitation in providing guidance for only one step, and would be more accurate if fractional steps are adopted.

Guidance experiments of IWPs on oxidant tank's simulating parts are carried out, whose success rate is up to 95 percent and MSEs are 0.7407, 0.7971, and 1.3429 mm. It meets the demands of continuous and automatic welding process.

Improved Harris Algorithm and GSCM are advanced to raise the precision of image processing which influenced guidance precision most.

This paper aims to develop a method to achieve automatic robotic welding and seam tracking so that three‐dimensional weld seam could be tracked without teaching and good welding formation could be accomplished.

Adaptive image processing method was used for various types of weld seam. Also the relationship between welding height and arc signal was calibrated. Through the decomposition and synthesis, three‐dimensional space type weld seam could be extracted and tracked well. The workpiece without teaching was finally tracked precisely and in a timely way with use of the fuzzy controller.

Composite sensing technology including arc and visual sensing had obvious advantages. Image processing method could be used for tracking plane weld seam efficiently while arc sensing could characterize welding height. Through the coupled controlling algorithm, arc sensing and visual sensing could be fused effectively.

How to couple information more accurately and quickly was still one of the most important problems in composite sensing technology.

Composite sensing technology could reduce costs to achieve weld seam instead such expensive device as laser sensor. The simulating parts of scalloped segment of bottom board for rockets were tracked in the project. Once more adaptive algorithms were developed, more complicated practical workpieces could be dealt with in robotic welding which promotes the application of industry robots.

A useful method for three‐dimensional space type weld seam tracking without teaching was developed. The whole procedure of adaptive image processing method was simple but efficient and robust. The coupled controlling strategy addressed could accomplish seam tracking by composite sensing technology.

The purpose of this paper is to develop a kind of low cost measuring system based on binocular vision sensor to detect both the weld pool geometry and root gap simultaneously for robot welding process.

Two normal charge coupled device cameras are used for capturing clear images from two directions; one of them is used to measure the root gap and another one is used to measure the geometric parameters of the weld pool. Efforts are made from both hardware and software aspects to decrease the strong interferences in pulsed gas tungsten arc welding process, so that clear and steady images can be obtained. The grey level distribution characteristics of root gap edge and weld pool edge in images are analyzed and utilized for developing the image processing algorithms.

A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.

The results show that the algorithms can extract the root gap edges and the contour of weld pool effectively, and then some geometric parameters can be calculated from the results.

The binocular vision system provides a new method for sensing of robot welding process.

This paper aims to examine and analyze the effects of government policy, quality of human resources and professional institutions on workforce competitiveness using welding technology variable as a mediating variable.

This study used quantitative research by using partial least square – structural equation modeling (PLS-SEM) to analyze the collected data.

Based on the results of the analysis, it was noted that there was a significant influence between government policy, quality of human resources and professional institutions on welding technology. The coefficients are characterized by a positive direct relationship, which means that the higher the quality of government policy and human resources professionals variables, the higher the value of the institute of welding technology.

This study recommends that government should create policies that have benefits to competitiveness of Indonesian workforce. Implications from this study support government to use the model to determine and initiate policies in the field of welding as well as establish clear and standardized operating standards and recruitment process (government apparatus) that can accommodate the competitiveness of welding workers in Indonesia.

The originality of this paper is that the participatory approach was adopted in this study using PLS-SEM. In addition, this study was one of the first studies to carry out research at the BNSP office, BLK-Bandung-Jakarta, Makassar, B4T and dismiss the Ministry of Manpower and the Ministry of Industry in Jakarta, Indonesia, where there was no research in this location. Previous studies conducted research in various case studies.

The purpose of this paper is to report an investigation into acoustic emission (AE) characteristics of the corrosion situation of the bottom of a large storage tank.

Guard sensors were applied in on‐line AE inspection of a tank bottom, and the AE signal characteristics of the corrosion areas of tank bottom were analyzed. The AE test results were compared with those from an internal tank internal test.

It was observed that guard sensors could shield effectively a large proportion of the extraneous noise signals inside the tank. The characteristics of AE signals from different types of corrosion were significantly different. A comparison of AE test results and tank internal inspection data showed a good agreement.

Characteristic AE signals from different types of corrosion were obtained for the first time, which assisted in the identification of the tank bottom corrosion situation.