Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. In this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.     

A novel vision-based method for 3D profile extraction of wire harness in robotized assembly process

Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments.     

基于计算机视觉的油封尺寸检测系统设计

针对油封的大批量、多品种、流水线作业的生产特点以及"零废品率"的检测要求,利用机器视觉检测和计算机图象处理技术,研究开发了适应大批量生产和在线实时检测的油封尺寸机器视觉检测系统;建立了系统软硬件结构,分析了系统的组成和工作原理及工作过程,并对图像处理算法进行了设计;系统标定后的实验检测结果证明,该检测系统能实现产品100%检测的生产目标,具有非接触、在线实时、精度适中、成本低等特点,具有较好的生产通用性。     

Theory of edge perception for computer vision feedback control

In this paper, the authors address the problem of edge-perception for its applications to vision-feedback control in robotic systems.In natural vision, the recognition of objects takes place through the process consisting of eye system, neural networks and cognition. The cognitive process, in turn yields a phenomenon known as perception. This is the phenomenon of perception of physical attributes, such as edges, color and texture, etc., which is responsible for the recognition of objects through the natural vision processes.In this paper, we make an attempt to postulate the theory of perception for gray-level images. The gray-level images, when going through the cognitive and perception processes, are contaminated by the uncertainty; here we call it cognitive uncertainty.The studies in this paper are confined to the phenomenon of edge-perception for two-dimensional gray-level images, however, these studies can be extended to other types of visual attributes both in two-dimensional and three-dimensional spaces. Indeed, the perception of these attributes, which attempts to emulate the human vision system, may help in the design of a truly robust computer vision-feedback control system for robotic applications.     

Semantic analysis in XTRA,an English — Chinese machine translation system

This paper concerns the resolution of lexical ambiguity in a machine translation environment. We describe the integration of principles of selection restrictions. Preference Semantics, and intelligent relaxation of constraints in handling lexical ambiguity. The approach differs from many previous MT systems in that it is more powerful than brute force systems, while more realistic than systems that assume a large degree of coded encyclopedia information for full understanding.     

基于机器视觉的隔热条尺寸测量系统

针对当前隔热条的尺寸测量仍停留在产品离线后,工作人员用卡尺测量,劳动强度大,工作效率低、测量精度易受人为因素影响的现状,本文提出将机器视觉技术引入到隔热条自动化生产线的在线尺寸检测系统中,这种非接触式测量既可以避免在测量过程中对隔热条的损坏,又可以提高测量精度和测量速度,从而达到提高测量效能的目的。     

激光焊接对接拼缝测量的微景深方法

对于复杂微细、错边量小的激光焊接拼缝测量,现有的视觉传感器方法不能稳定、可靠地提取出拼缝的中心位置、拼缝宽度等信息。为此,提出了基于微景深的激光焊接对接拼缝测量方法,该方法采用高倍光学放大镜头、工业CCD(Charge Coupled Device)/CMOS(Complementary Metal-Oxide-Semiconductor Transistor)相机和LED(Light-Emitting Diode)外部照明装置,以光学成像光轴与被测表面法矢呈45°的方式,组成的对接拼缝的非接触测量系统,既解决了显微放大时景深很小、测量范围小的问题,又解决了狭窄拼缝高光学放大倍数拼缝检测的难题。实验结果表明,该系统可快速准确地提取出拼缝中心坐标、拼缝宽度以及拼缝局部表面法矢。拼缝宽度测量精度优于5 μm,拼缝中心测量精度优于6 μm,可识别的最小拼缝宽度为0.02 mm,完全满足激光焊接对接狭窄拼缝的检测与自动跟踪的要求。     

Particular behavior of spindle thermal deformation by thermal bending

Thermally induced errors reduce the accuracy in precision machining, and a great deal of research has been presented on compensation for these errors in machine tools. However, during the transition period after commencing or stopping spindle rotation, thermal deformation behavior is very complex. In particular, the y-directional movement of the vertical machining center cannot be explained by thermal expansion alone because of the relationship between deformation and temperature. Thermal bending that is generated from the thermal gradient in the structure causes this movement. In the research described in this paper, a theoretical explanation and an experimental verification is given for the particular behavior of spindle thermal deformation. As it is not easy to map the relationship of the compensation model, separation of the steady from the non-steady state in the mapping process is strongly recommended.     

基于机器视觉的加工刀具磨损监测方法

为了对加工过程中刀具的磨损状态进行监测,针对麻花钻的磨损形式,提出基于机器视觉的加工刀具磨损监测方法. 根据磨损刀具图像的灰度分布特点,提出基于积分图加速和Turky bi-weight核函数的非局部均值去噪方法;采用单、双阈值大津法获取磨损区域的灰度区间,实现对图像的自适应对比度增强;提出基于形态学重构方法的磨损区域局部极值点提取方法,有效完成对磨损区域的检测和边界提取. 该刀具磨损检测方法成功应用于麻花钻头磨损状态的监测过程,实验结果表明,相较于目前已有的机器视觉监测方法,所提出的方法具有更高的检测精度和效率,准确地提取磨损轮廓,从而有效实现对刀具磨损状态的监测和自动化监控加工过程,达到降低人工成本和产品不合格率的目的.