Seam Position Research Articles

The THE EFFECT OF GEOLOGICAL STRUCTURES ON THE COAL QUALITY OF THE SEMBAKUNG FORMATION IN BERAU REGENCY, EAST KALIMANTAN

The Subsurface coal resources are 43.533 billion tons with total reserves of 173.51 million tons. The Berau Regency area has coal-bearing formations of the bituminous - sub bituminous type, namely the Latih Formation, Labanan Formation, Domaring Formation, Sajau Formation and Sembakung Formation. The research methods used are surface geological mapping, rock sampling and drilling. The analysis used is coal quality and geological structure. The results of analysis of changes in the dip of rock layers caused by geological structures in the form of folds and thrust faults can cause changes in coal quality. The dip of coal seam in the LP 54 outcrop sample is 24º, which has lower coal quality compared to LP 38 that has a seam dip of 65º. The quality of coal in LP 38 seam position N 203º E/65º has a calorific value of 6,393 Kcal/kg, while in LP 54 seam position N 30º E/24º has a calorific value of 4,888 Kcal/kg. At the drill point, location MMA_19-T has better coal quality compared to MMA_18-T and MMA_20-T, this is because the seam in MMA_19-T experienced folding accompanied by faulting with a calorific value of 7326 Kcal/kg.

Seam tracking and gap bridging during robotic laser beam welding via grayscale imaging and wobbling

The use of laser beam welding with robotic manipulators is expanding towards wider industrial applications as the system availability increases with reduced capital costs. Conventionally, laser welding requires high positioning and coupling accuracy. Due to the variability in the part geometry and positioning, as well as the thermal deformation that may occur during the process, joint position and fit-up are not always acceptable nor predictable a-priori if simple fixtures are used. This makes the passage from virtual CAD/CAM environment to real production site not trivial, limiting applications where short part preparations are a need like small-batch productions. Solutions that render the laser welding operations feasible for production series with non-stringent tolerances are required to serve a wider range of industrial applications. Such solutions should be able to track the seam as well as tolerating variable gaps formed between the parts to be joined. In this work, an online correction for robot trajectory based on a greyscale coaxial vision system with external illumination and an adaptive wobbling strategy are proposed as means to increase the overall flexibility of a manufacturing plant. The underlying vision algorithm and control architectures are presented; the robustness of the system to poor illumination conditions and variable reflection conditions is also discussed. The developed solution employed two control loops: the first is able to change the robot pose to follow varying trajectories; the second, able to vary the amplitude of circular wobbling as a function of the gap formed in butt-joint welds. Demonstrator cases on butt-joint welds with AISI 301 stainless steel with increased complexity were used to test the efficacy of the solution. The system was successfully tested on 2 mm thick, planar stainless-steel sheets at a maximum welding speed of 25 mm/s and yielded a maximum positioning and yaw-orientation errors of respectively 0.325 mm and 4.5°. Continuous welds could be achieved with up to 1 mm gaps and variable seam position with the developed control method. The acceptable weld quality could be maintained up to 0.6 mm gap in the employed autogenous welding configuration.

Feature Point Identification in Fillet Weld Joints Using an Improved CPDA Method

An intelligent, vision-guided welding robot is highly desired in machinery manufacturing, the ship industry, and vehicle engineering. The performance of the system greatly depends on the effective identification of weld seam features and the three-dimensional (3D) reconstruction of the weld seam position in a complex industrial environment. In this paper, a 3D visual sensing system with a structured laser projector and CCD camera is developed to obtain the geometry information of fillet weld seams in robot welding. By accounting for the inclination characteristics of the laser stripe in fillet welding, a Gaussian-weighted PCA-based laser center line extraction method is proposed. Smoother laser centerlines can be obtained at large, inclined angles. Furthermore, an improved chord-to-point distance accumulation (CPDA) method with polygon approximation is proposed to identify the feature corner location in center line images. The proposed method is validated numerically with simulated piece-wise linear laser stripes and experimentally with automated robot welding. By comparing this method with the grayscale gravity method, Hessian-matrix-based method, and conventional CPDA method, the proposed improved CPDA method with PCA center extraction is shown to have high accuracy and robustness in noisy welding environments. The proposed method meets the need for vision-aided automated welding robots by achieving greater than 95% accuracy in corner feature point identification in fillet welding.

Geologi dan Pola Sebaran Serta Kemenerusan Lapisan Batubara Menggunakan Metode Kontur Struktur di Desa Suo-Suo dan Sekitarnya Kecamatan Sumay Kabupaten Tebo Provinsi Jambi

Coal is a non-renewable natural resource. Coal has an important role in domestic and foreign energy resources. Coal is generally used in steam power plants (PLTU). In supporting the sustainability of coal energy, further exploration in coal development is needed. The research uses the required structural contour method with the aim of determining the distribution limits and direction of the continuity of the coal seam and only refers to the position of the coal seam and is applied to topographic maps. The observation location (LP) found in the research area contained 7 coal seams. Based on the criteria of the Indonesian National Standard (SNI), the number of outcrops that have a coal seam thickness above 1 meter are 2 outcrops with a thickness of 2.15 meters and 1.24 meters, respectively. The results of drawing the distribution pattern using the structural contour method based on the bottom of the coal seam, it is found that the direction of the distribution pattern is northwest-southeast. The continuity follows the direction of the hills and valleys based on a distribution pattern that is trending northwest-southeast with a developed control structure in the form of a pure rising fault. This is in accordance with the regional geological map of the study area with the direction of the continuity of the coal seam trending northwest-southeast. From the results of the distribution pattern and continuity, there are 2 coal outcrops that can be exploited with economic value.

Introduction of a New Test Methodology for Determining the Delayed Cracking Susceptibility

A missing test methodology that allows for the determination of delayed cracking susceptibility of laser welds of high-strength sheet steel is presented. Unlike other cold crack testing methods, this test is based on a self-restraint testing of specimens welded from thin sheet materials without welding consumables and external loading. The potential test procedure with sample geometry, clamping device and documentation of the cracks is described. It is shown that the position of the weld on the specimen is a critical parameter and the susceptibility to cold cracking increases with increasing edge distance. The test methodology in combination with the most critical seam position is successfully used to rank two different steels regarding their susceptibility to delayed cracking. Further investigations are conducted evaluating the cold cracking susceptibility at different energy levels and lubricating conditions. It is proven that the lubrication has a significant influence on the susceptibility to cold cracking. Nevertheless, a narrow but safe process window is found.

Research on 3D curved weld seam trajectory position and orientation detection method

The "teaching-reproduction" mode and off-line programming mode are widely used in traditional welding robots. For curved welding workpieces, the operators need to visually inspect the position and orientation of the weld seam during processing to select the teaching points or the starting point for programming. The welding accuracy depends on the operators’ experience. Re-teaching or re-programming is required after adjusting the welding position or orientation, resulting in poor welding accuracy and efficiency. To enhance the adaptability of the robot to various welding conditions, we propose a 3D curved weld seam trajectory position and orientation detection method. A local welding workpiece point cloud is obtained by laser vision sensor scanning. The point cloud is smoothed by using the moving least squares (MLS) method. The surface variation of each point is calculated, and the surface variation threshold is set to extract the weld seam feature points. The weld seam orientation is calculated by solving the basic vectors of the spatial curve according to the parametric equation of the weld seam trajectory. The rotation angles of the robot are calculated by the transformation matrix between the weld seam local coordinate system and the robot base coordinate system. According to the experiments, the proposed method can adapt to various orientations of the curved welding workpiece and improve the robot's adaptability to orientations, therefore, the accuracy and efficiency of welding will be enhanced.

Mechanical Action Mechanism and Crack Response Analysis of Slide Plate of No.12 Turnout

In the course of maintenance and repair, the railway workshops of the Lan-Qing railway found that the slide plate was prone to injury and damage. In order to study the actual stress state of the slide plate during the running of the train, a finite element model of the slide plate and related components was established based on ANSYS, Hypermesh was used for meshing and mesh quality control, and the Mooney-Rivlin constitutive intrinsic model was selected to simulate the rubber plate. The stress state of the slide plate when passing through the turnout in the straight and lateral directions is analyzed respectively. The results show that there is stress concentration at the root of the tongue depressor of the slide plate, and large deformation exists in the middle of the base plate, the front end of the platform plate and the position of the guide iron when passing through the turnout directly and laterally. The stress state of the slide plate is simulated when the rail brace fails, and the maximum stress is found to be 279.24 MPa, which exceeds the material yield limit. Therefore, we should pay attention to the inspection of the condition of the rail brace during the maintenance and repair. Presenting cracks at the weld seam location, it is found that the crack has a significant effect on the dewelding of the slide plate, and the heel position is the weakness of the slide plate. When the crack length reaches 1.2 mm, there is a tendency to crack at the weld seam position of the slide plate, and when the crack length continues to increase, the crack will further expand and jeopardizing structural safety. The research results can provide a theoretical basis for the improvement, disease prevention and maintenance of the slide plate.

Factors Influencing the Stability of a Slope Containing a Coal Seam in a Goaf

Our aim in this study was to analyze the major and minor factors affecting the stability of a slope containing a coal seam in a goaf. Based on engineering experience, we first identified nine factors that may affect slope stability, of which we determined eight that may substantially affect slope stability through a single-factor numerical simulation analysis. Then, we arranged 27 groups of numerical simulation tests with eight factors and three levels with the orthogonal test method, and we determined the ranking of the major and minor influencing factors through a range of variance analyses. The results showed that the influence of each factor was ranked as the roadway width > coal seam position > slope gradient > coal seam thickness > coal seam internal friction angle > coal seam cohesion > coal seam dip angle. Among these, the roadway width, coal seam position, and slope gradient were the major factors affecting slope stability; coal seam thickness, coal seam internal friction angle, coal seam cohesion, and coal seam dip angle were the minor factors. In this study, we combined the goaf and slope containing the coal seam, and we couple analyzed the factors influencing the stability of the slope containing the coal seam in the goaf. Our findings provide a scientific basis for the treatment and protection of slopes containing coal seams in goafs in the future and have a practical engineering importance for the analysis of the excavation stability of road-cutting slopes in goafs.

Seam detection method design and automatic calibration based on an active light coaxial visual sensor

Seam feature detection is one of the critical premises of automatic welding. However, the current seam detection method is vulnerable to complex laser environments, such as illumination interference and material defects, thereby limiting the application of active light coaxial visual sensors in seam tracking. This paper proposes a seam detection and calibration method based on an active light coaxial visual sensor in a laser welding digital twin environment. First, a vision sensor with an 808 nm auxiliary illumination and a customized lens are mounted on the laser welding head to filter out noises and capture the welding image, including the seam and melt pool. Next, a seam information extraction approach based on image processing algorithms is designed to extract seam position and seam width. In addition, an artificial neural network based Kalman filter is proposed to eliminate measurement errors and reduce measurement noise. The calibration of the seam detection parameters is a tedious process. Therefore, the calibration problem is transformed into an optimization problem. A genetic algorithm-based calibration method is proposed to automatically determine the optimum parameters of the seam detection algorithm. Finally, seam detection experiments on laser welding are conducted in both the digital twin environment and the laser welding system. The experimental results show that the proposed method is more efficient than the Hough transfer-based seam detection method. The calibration method can ensure the accuracy of the seam detection method, especially in complex and changeable welding conditions.

Repeated uniform blast loading on welded mild steel rectangular plates

The present research paper deals with a combined experimental and numerical assessment of the deformation mode and failure mechanism of welded rectangular plates exposed to three successive uniform blast loading. To perform the experimental study, three different categories of 0 W (a plate without any weld seam), 1 W (a plate with a single weld seam), and 2 W (a plate with double weld seams) were conducted and mounted onto the ballistic pendulum apparatus. The effect of the number of blasts, the number of weld seams, and the explosive charge mass was investigated on the structural dynamic response. Unlike 0 W plates, plates with double weld seams experienced large plastic deformation along with small thinning happening at the long side clamped boundary. Therefore, despite increasing the deformation resistance, the welding lines tended to mitigate the damage tolerance of the tested mild steel plates. To gain more insight into the influence of two other key parameters like the position of the weld seam and its orientation on the dynamic response and energy absorption capacity, numerical simulations were also performed using the ABAQUS/Explicit finite element solver in conjunction with the inbuilt ConWep blast function code. For modeling the amplified blast wave produced by the detonation of the explosive charge, the scaling factor of 4.1 was adopted. Due to the welding process on the tested plates, the material degradation at the Heat-Affected Zone (HAZ) was also incorporated into the simulation by obtaining the knockdown factor from quasi-static tensile tests on unwelded and welded specimens. An excellent agreement between experiments and simulation results was achieved in terms of the impulse of the applied load and central permanent deflection. Furthermore, the present paper provides the primary experimental data and numerical simulation support for the blast resistance design of metallic structures using welded specimens.

UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

This paper introduces an Unmanned Aerial Vehicle (UAV) image stitching method, based on the optimal seam algorithm and half-projective warp, that can effectively retain the original information of the image and obtain the ideal stitching effect. The existing seam stitching algorithms can eliminate the ghosting and blurring problems on the stitched images, but the deformation and angle distortion caused by image registration will remain in the stitching results. To overcome this situation, we propose a stitching strategy based on optimal seam and half-projective warp. Firstly, we define a new difference matrix in the overlapping region of the aligned image, which includes the color, structural and line difference information. Then, we constrain the search range of the seam by the minimum energy, and propose a seam search algorithm based on the global minimum energy to obtain the seam. Finally, combined with the seam position and half-projective warp, the shape of the stitched image is rectified to keep more regions in their original shape. The experimental results of several groups of UAV images show that our method has a superior stitching effect.

Image contrast enhancement and denoising in micro-gap weld seam detection by periodic wide-field illumination

Due to the speckle noise and welding light noise, it's still a challenge for the detection of tight weld joints by wide-field illumination. To circumvent this problem, we proposed a novel weld joint detection system by using periodic wide-field illumination. A specific frequency component can be encoded into the image sequence by modulating the intensity of the illumination. By leveraging high-speed light-emitting diode (LED) driver and high-speed camera, an image sequence can be obtained in milliseconds. Most of the light noises contain no modulation information. By extracting the modulation frequency from the image sequences, we achieved ~35% improvement in signal-to-noise ratio (SNR) of the seam image. In addition, the light noise can be removed effectively in the frequency domain. The comparisons under different welding conditions revealed that the proposed method could accurately locate the seam position in ultra-tight seam and under strong light noise while the method without modulation failed.

Application of Improved Multiple Linear Regression Model in the Tracking of Strip Welding Seam

In the process of hot dip galvanization. An improved multiple linear regression model is established to predict the welding seam position of strip steel when two continuous strip steels pass through the air knife. Firstly, a multiple linear regression model is established according to the production parameters of hot dip galvanizing process. Secondly, the least square method is used to improve the multiple linear regression model. Finally, in order to verify the effectiveness of the method, the model is simulated by SPSS software, and the experimental result show that the method achieves the purpose of quantification.

A study on the modified algorithm for image processing in Tracking Seam Welding

For robot path planning the weld seam positions need to be known in advance as the industrial robot generally work in teach and playback mode. Since the welding of the pipe is not done completely on the straight line (the nature of the pipe) and the test tube under the machine is moving, the symmetry of the two probes in relation to the welding site is very important during the test and quick tracking is required to set the probes. The use of image processing and machine vision techniques is very efficient in optimizing seamless welding radia. In designing the algorithms used, an attempt has been made to reduce the environmental conditions and unstable industrial situation well in order to track the weld seam with an acceptable speed. New approach has been used to access the central line of the weld seam area. The algorithm is designed to be implemented in a real environment and has very good results. One of the advantages of this method is the reduction of measurement error and the elimination of mechanical and electrical sensors in non-destructive tests.

Online porosity defect detection based on convolutional neural network for Al alloy laser welding

Porosity is one of the most serious defects in Al alloy laser welding. The online detection of the porosity can identify the weak position of weld seam and take remedial measures accordingly. In this paper, a convolutional neural network (CNN) model where the input is the signal spectrum graphs extracted by wavelet packet decomposition (WPD) is constructed to identify the porosity during Al alloy laser welding in real-time. The porosity monitoring platform is set up to obtain the keyhole opening area signal and the keyhole depth signal in the welding process. The sliding window scanning algorithm is used to scan the signals and the weld seam, and the time-frequency spectrum graphs are obtained by WPD processing on the signals in each sliding window. Through analysis, when there is porosity in a small weld seam section of the current sliding window, the signal spectrum graphs are messy at this moment, while when there is no porosity, the signal spectrum graphs are clean and the frequency bands are concentrated in low-frequency part. The CNN model is constructed to classify the signal spectrum graphs under different porosity status, thereby it can identify the porosity in the weld seam.

Weld Seam Detection, Finding, and Setting of Process Parameters for Varying Weld Gap by the Utilization of Laser and Vision Sensor in Robotic Arc Welding

The prime objective of this article is to obtain maximum weld seam positional accuracy and weld quality with varying weld gap values over weld cycles in laser and vision-sensor-assisted robotic arc welding. At first, a vision sensor is used to obtain the seam positions, so that there is no need to teach the robot manually. For a repeatable environment or a similarly designed weld path, the weld seam can then be found by a laser sensor; if there is any error position, it is then notified to a robot controller to move the robot tool center point to the actual position. After that, the output data of the laser sensor in terms of variation of weld gap is used to set the welding parameters at their optimal values by the application of fuzzy logic and NSGA-II algorithm to achieve maximum weld quality. A MOTOMAN MA1440A arc welding robot system is used for experimental investigation of the proposed approach. Furthermore, maximized weld quality in terms of the weld bead size, mechanical, and microstructure properties is achieved by the optimal setting of significant process parameters with respect to the weld gap value obtained as output from the laser sensor. The proposed approach adds more value to the automation process and offline quality improvement of robotic arc welding.

Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking.

Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using theρ-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.

Investigation of Reverse Swing and Magnus Effect on a Cricket Ball Using Particle Image Velocimetry

Lateral movement from the principal trajectory, or “swing”, can be generated on a cricket ball when its seam, which sits proud of the surface, is angled to the flow. The boundary layer on the two hemispheres divided by the seam is governed by the Reynolds number and the surface roughness; the swing is fundamentally caused by the pressure differences associated with asymmetric flow separation. Skillful bowlers impart a small backspin to create gyroscopic inertia and stabilize the seam position in flight. Under certain flow conditions, the resultant pressure asymmetry can reverse across the hemispheres and “reverse swing” will occur. In this paper, particle image velocimetry measurements of a scaled cricket ball are presented to interrogate the flow field and the physical mechanism for reverse swing. The results show that a laminar separation bubble forms on the non-seam side (hemisphere), causing the separation angle for the boundary layer to be increased relative to that on the seam side. For the first time, it is shown that the separation bubble is present even under large rates of backspin, suggesting that this flow feature is present under match conditions. The Magnus effect on a rotating ball is also demonstrated, with the position of flow separation on the upper (retreating) side delayed due to the reduced relative speed between the surface and the freestream.

The seismic performance of precast short-leg shear wall under cyclic loading

In order to study the seismic performance of precast short-leg shear wall connected by grouting sleeves (PSSW), the three-dimensional numerical model was established by using the experiment of PSSW subjected to low cyclic loading. Based on good agreement between numerical results and experimental results, the numerical analysis models with different structural parameters of axial compression ratio and splicing position were designed in detail, and the effects of various parameters on the seismic performance of PSSW were analyzed. The results show that the PSSW exhibits wide and stable hysteresis loops, indicating a satisfactory hysteretic performance and an excellent energy consumption capacity. With the increase of the axial compression ratio, the shear capacity of horizontal splice seam is improved, but the ductility coefficient and total energy consumption decrease obviously. The most disadvantageous position of PSSW can be effectively avoided by changing the position of the post pouring seam. The bearing capacity of the specimens is basically stable, and the energy consumption increases significantly, so the post pouring seam of precast wall is recommended to be far away from the bottom section of the wall. In addition, the failure mechanism of different splicing positions was analyzed in detail.

Seam Feature Point Acquisition Based on Efficient Convolution Operator and Particle Filter in GMAW

Seam feature point acquisition is the premise of the intelligent welding process such as initial point guiding and seam tracking. However, conventional seam feature point acquisition methods based on geometric feature have shortcomings of poor flexibility and robustness. In this article, a seam feature point acquisition method based on efficient convolution operator (ECO) and particle filter (PF) is proposed, which could be applied to different weld types and could achieve fast and accurate seam feature point acquisition even under the interference of welding arc light and spatter noises. First, a structured light vision sensor is developed to acquire welding image. Second, the ECO algorithm is adopted to track the seam region and acquire seam feature point during gas metal arc welding process. Third, the state and measurement equations of the weld seam position are established, and PF is applied to improve seam feature point acquisition accuracy. Finally, a welding experiment system is built and a series of seam feature point acquisition experiments of butt joint, lap joint, and fillet joint are carried out to validate the performance of the proposed method. The experiment results demonstrate that the processing speed of the proposed method could reach up 35 Hz, and the seam feature point acquisition errors are smaller than 0.15 mm, which could meet the real-time and accuracy requirement for subsequent initial point guiding and seam tracking.