A systematic review of additive manufacturing-based remanufacturing techniques for component repair and restoration

A review of vision-aided robotic welding

Welding is a major manufacturing process that joins two or more pieces of materials together through heating/mixing them followed by cooling/solidification. The goal of welding manufacturing is to join materials together to meet service requirements at lowest costs. Advanced welding manufacturing is to use scientific methods to realize this goal. This paper views advanced welding manufacturing as a three step approach: (1) pre-design that selects process and joint design based on available processes (properties, capabilities, and costs); (2) design that uses models to predict the result from a given set of welding parameters and minimizes a cost function for optimizing the welding parameters; and (3) real-time sensing and control that overcome the deviations of welding conditions from their nominal ones used in optimizing the welding parameters by adjusting the welding parameters based on such real-time sensing and feedback control. The paper analyzes how these three steps depend on process properties/capabilities, process innovations, predictive models, numerical models for fluid dynamics, numerical models for structures, real-time sensing, and dynamic control. The paper also identifies the challenges in obtaining ideal solutions and reviews/analyzes the existing efforts toward better solutions. Special attention and analysis have been given to (1) gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) as benchmark processes for penetration and materials filling; (2) keyhole plasma arc welding (PAW), keyhole-tungsten inert gas (K-TIG), and keyhole laser welding as improved/capable penetrative processes; (3) friction stir welding (FSW) as a special penetrative low heat input process; (4) alternating current (AC) GMAW and double-electrode GMAW as improved materials filling processes; (5) efforts in numerical modeling for fluid dynamics; (6) efforts in numerical modeling for structures; (7) challenges and efforts in seam tracking and weld pool monitoring; (8) challenges and efforts in monitoring of keyhole laser welding and FSW; and (9) efforts in advanced sensing, data fusion/sensor fusion, and process control using machine learning/deep learning, model predictive control (MPC), and adaptive control.

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Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Collaborative manufacturing technologies of structure shape and surface integrity for complex thin-walled components of aero-engine: Status, challenge and tendency

Seam tracking with structured light vision has been widely applied into the robot welding. And the precise laser stripe extraction is the premise of automatic laser vision seam tracking. However, conventional laser stripe extraction methods based on image processing have the shortcomings of poor flexibility and robustness, which are easily affected by considerable image noises in the welding processing, such as arc light, smoke, and splash. To address this issue, inspired by image segmentation, with the strong contextual feature expression ability of deep convolutional neural network (DCNN), a novel image denoising method of seam images is proposed in this paper for automatic laser stripe extraction to serve intelligent robot welding applications, such as seam tracking, seam type detection, weld bead detection, etc. With the deep encoder-decoder network framework, aimed at the information loss issue by multiple convolutional and pooling operations in DCNNs, an attention dense convolutional block is proposed to extract and accumulate multi-scale feature maps. Meanwhile, a residual bi-directional ConvLSTM block (BiConvLSTM) is proposed to effectively learn multi-scale and long-range spatial contexts from local feature maps. Finally, a weighted loss function is proposed for model training to address the class unbalanced issue. Combined with the seam image set, the experimental results show that the proposed image denoising network could correctly extract the laser stripes from seam images which could demonstrate that the proposed method shows a high detection precision and good robustness against the strong image noise interference from welding process.

Image Denoising of Seam Images With Deep Learning for Laser Vision Seam Tracking

In this paper we aim to solve the problems of laser cladding remanufacturing of damaged complex components. First, a model of a workpiece to be repaired was pre-sliced finely by using the minimum layer thickness. On this basis, an algorithm for calculating step width was proposed. When a workpiece side angle meets |a − 180∣ not more than 40°, the distance from the projection point to the left or right lines of the closest contour point is calculated, and the distance closest to that of the previous contour point as the step width of the current point is noted. When a workpiece side angle meets |a − 180∣ more than 40°, the corner feature of the contour point is obvious, and the distance from the projection point to the corresponding point is taken directly as the step width of the current point. After which, the slices with the minimum layer thickness are merged layer by layer, according to the maximum step width required by the process and the maximum acceptable layer thickness. Finally, the algorithm was verified by using a laser repair example. The method of calculating the step width accurately reflected variations of the curvatures between adjacent layers, or the whole contour profile. Also, the adaptive slicing algorithm balanced the build time and repair qualities, further, which addressed the loss of curvature mutation in conventional stepwise uniform refinement and improved the slicing efficiency.

An adaptive slicing algorithm for laser cladding remanufacturing of complex components

Adaptive control for laser welding with filler wire of marine high strength steel with tight butt joints for large structures

It is worthwhile to efficiently and cost-effectively capture the vibration energy in the operation of rotating machinery and convert it from mechanical energy to electrical power for a specific application. Currently, triboelectric nanogenerators as vibration energy harvesters are mainly studied for the start and stop of mechanical equipment but less for its resonance. In this paper, a self-powered circular-honeycomb triboelectric nanogenerator (CH-TENG) is proposed as a vibration energy harvester. Firstly, tests are carried out on a Vibration Platform to obtain the output performance of CH-TENG at different excitation amplitudes, frequencies, and accelerations. The factors affecting the output of CH-TENG are then summarized. Stability experiments are also carried out to verify the stability of CH-TENG. Secondly, the CH-TENG is applied on a 7.5 kW convex pole synchronous machine to verify its practicality. Finally, the homemade CH-TENG is improved with springs installed at the bottom to amplify the vibration energy amplitude. The experimental results show that the synchronous machine will have apparent voltage and current output pulses when it reaches the resonance frequency. It indicates that the CH-TENG can be used not only as a vibration energy harvester but also for detecting the resonance state of a synchronous machine, which has significant potential for monitoring resonance faults of the machine when it is at service. 

Self-powered circular-honeycomb triboelectric nanogenerator for vibration energy harvesting and resonance detection of synchronous machine

Laser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.,First, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.,The experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.,A linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

Matching for navigation map building for automated guided robot based on laser navigation without a reflector

Adaptive tool path generation for flank milling of thin-walled parts based on force-induced deformation constraints

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