Repair of metallic components using hybrid manufacturing

Many high-performance metal parts users extend the service of these damaged parts by employing repair technology. Hybrid manufacturing, which includes additive manufacturing (AM) and subtractive manufacturing, provides greater build capability, better accuracy, and surface finish for component repair. However, most repair processes still rely on manual operations, which are not satisfactory in terms of time, cost, reliability, and accuracy. This dissertation aims to improve the application of hybrid manufacturing for repairing metallic components by addressing the following three research topics. The first research topic is to investigate and develop an efficient best-fit and shape adaption algorithm for automating 3D models\u27 the alignment and defect reconstruction. A multi-feature fitting algorithm and cross-section comparison method are developed. The second research topic is to develop a smooth toolpath generation method for laser metal deposition to improve the deposition quality for metallic component fabrication and repair. Smooth connections or transitions in toolpath planning are achieved to provide a constant feedrate and controllable deposition idle time for each single deposition pass. The third research topic is to develop an automated repair process could efficiently obtain the spatial information of a worn component for defect detection, alignment, and 3D scanning with the integration of stereo vision and laser displacement sensor. This dissertation investigated and developed key technologies to improve the efficiency, repair quality, precision, and automation for the repair of metallic components using hybrid manufacturing. Moreover, the research results of this dissertation can benefit a wide range of industries, such as additive manufacturing, manufacturing and measurement automation, and part inspection --Abstract, page iv

Stereo Vision Based Hybrid Manufacturing of Ti-6Al-4V in Component Repair Process

Parts or products from high performance metal are very expensive, partly due to the processing complexities during manufacturing. Recent studies have indicated that hybrid processes of additive manufacturing and machining process can be used to repair titanium parts, thus extending the service life. In order to implement these methods automatically, it is necessary to obtain the spatial geometry information of component with defects to generate the tool path. The purpose of this paper is to summarize the research on hybrid manufacturing with stereo vision function which can be applied to the component repair process. Stereo vision is adopted to detect the location and the size of the defect area which is marked by color marker. And then laser displacement sensor is applied to scan the defect area. Therefore, automated alignment, reconstruction of the defect area and tool path planning could be implemented based on the spatial geometry information. Finally, a Ti64 part repair experiment is done to verify the method. This work provides an automatic method for repairing damaged parts by hybrid manufacturing.Mechanical Engineerin

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors

Industrial Robot Trajectory Accuracy Evaluation Maps for Hybrid Manufacturing Process based on Joint Angle Error Analysis

Industrial robots have been widely used in various fields. The joint angle error is the main factor that affects the accuracy performance of the robot. It is important to notice that these kinematic parameters error cannot be eliminated from the robot system completely. Even after calibration, these errors still exist and will be fluctuated during the robot system running. This paper proposed a new method of finding the best position and orientation to perform a specific working path based on the current accuracy capacity of the robot system. By analyzing the robot forward/inverse kinematic and the angle error sensitivity of different joint in the serial manipulator system, a new evaluation formulation is established for mapping the trajectory accuracy within the robot’s working volume. The influence of different position and orientation on the movement accuracy of the end effector has been verified by experiments and discussed thoroughly. Finally, a visualized evaluation map can be obtained to describe the accuracy difference of a robotic laser deposition working path at different positions and orientations. This method is helpful for making the maximum usage of the robot’s current accuracy ability rather than blindly pursuing the higher accuracy of the robot system

Detection Improvement of Unburned Carbon Content in Fly Ash Flow Using LIBS with a Two-Stage Cyclone Measurement System

Fly ash contents can be considered as a basis for optimal and stable boiler combustion control and fly ash quality control in power plant, especially the unburned carbon in fly ash. The real-time and quantitative measurement of contents in fly ash was studied using a constructed two-stage cyclone measurement system and detected using laser-induced breakdown spectroscopy(LIBS) technique. The surrounding gas effect, such as CO2 effect on unburned carbon content, was studied comprehensively in this paper. The CO2 effect was eliminated using this proposed combination method of two-stage cyclone measurement system and LIBS with 1ns pulse-width laser according to the efficient gas-particle separation and the controlled laser-induced plasma processes of particle flow. The quantitative analysis was improved using the plasma temperature correction method with the intensity ratio of the emission pair from magnesium as a plasma temperature indicator. The measurement of unburned carbon content in fly ash with temperature correction method presented the concordant results analyzed by chemical analysis method. It is demonstrated the feasibility and improved detection ability for the real-time measurement of fly ash contents in power plant

Emission characteristics of laser-induced plasma using collinear long and short dual-pulse LIBS

The collinear long and short dual-pulse LIBS (DP-LIBS) was employed to clarify the emission characteristics from laser-induced plasma. The plasma was sustained and became stable by the long pulse-width laser with the pulse width of 60 μs under FR (free running) condition as an external energy source. Comparing the measurement results of stainless steel in air using SP-LIBS and DP-LIBS, the emission intensity was enhanced using DP-LIBS markedly. The temperature of plasma induced by DP-LIBS was maintained at higher temperature under different gate delay time and short pulse-width laser power conditions compared with these measured using SP-LIBS of short pulse width. Moreover, the variation rates of plasma temperature measured using DP-LIBS were also lower. The superior detection ability was verified by the measurement of aluminum sample in water. The spectra were clearly detected using DP-LIBS, whereas it cannot be identified using SP-LIBS of short pulse width and long pulse width. The effects of gate delay time and short pulse-width laser power were also discussed. These results demonstrate the feasibility and enhanced detection ability of the proposed collinear long and short DP-LIBS method

Comparison Study on the Performances of Finite Volume Method and Finite Difference Method

Vorticity-stream function method and MAC algorithm are adopted to systemically compare the finite volume method (FVM) and finite difference method (FDM) in this paper. Two typical problems—lid-driven flow and natural convection flow in a square cavity—are taken as examples to compare and analyze the calculation performances of FVM and FDM with variant mesh densities, discrete forms, and treatments of boundary condition. It is indicated that FVM is superior to FDM from the perspective of accuracy, stability of convection term, robustness, and calculation efficiency. Particularly ,when the mesh is coarse and taken as 20×20, the results of FDM suffer severe oscillation and even lose physical meaning

Unburned carbon measurement in fly ash using laser-induced breakdown spectroscopy with short nanosecond pulse width laser

The unburned carbon in fly ash is one of the important factors for the boiler combustion condition. Controlling the unburned carbon in fly ash is beneficial for fly ash recycle and to improve the combustion efficiency of the coal. Laser-induced breakdown spectroscopy (LIBS) technology has been applied to measure the fly ash contents due to its merits of non-contact, fast response, high sensitivity, and real-time measurement. In this study, experimental measurements have been adopted for fly ash flows with the surrounding gases of N2 and CO2, while the CO2 concentration varified to evaluate the CO2 effect on the unburned carbon signal from fly ash powder. Two kinds of pulse width lasers, 6ns and 1ns, were separately adopted to compare the influence of laser pulse width. Results showed that compared with 6ns pulse width laser, plasma temperature was lower and had less dependence on delay time when using 1ns pulse width laser, and spectra had more stable background. By using 1ns pulse width laser, the emission signal from surrounding CO2 also decreased because of the less surrounding gas breakdown. The solid powder breakdown signals also became more stable when using 1ns pulse width laser. So it is demonstrated that 1ns pulse width laser has the merits for fly ash flow measurement using LIBS

Fault2SeisGAN: A method for the expansion of fault datasets based on generative adversarial networks

The development of supervised deep learning technology in seismology and related fields has been restricted due to the lack of training sets. A large amount of unlabeled data is recorded in seismic exploration, and their application to network training is difficult, e.g., fault identification. To solve this problem, herein, we propose an end-to-end training data set generative adversarial network Fault2SeisGAN. This network can expand limited labeled datasets to improve the performance of other neural networks. In the proposed method, the Seis-Loss is used to constrain horizon and amplitude information, Fault-Loss is used to constrain fault location information, and the Wasserstein distance is added to stabilize the network training to generate seismic amplitude data with fault location labels. A new fault identification network model was trained with a combination of expansion and original data, and the model was tested using actual seismic data. The results show that the use of the expanded dataset generated in this study improves the performance of the deep neural network with respect to seismic data prediction. Our method solves the shortage of training data set problem caused by the application of deep learning technology in seismology to a certain extent, improves the performance of neural networks, and promotes the development of deep learning technology in seismology