Multirobot systems have shown great potential in dealing with complicated tasks that are impossible for a single robot to achieve. One essential problem encountered in cooperatively working of the multirobot systems is the unknown initial transformation relationships from hand to eye, base to base, and flange to tool. In this article, the problem of multicoordinates calibration for a dual-robot system is formulated to a matrix equation AXB = YCZ. A novel approach for simultaneously solving the unknowns in equation AXB = YCZ is proposed, which is composed of a closed form method based on the Kronecker product and an iterative method which converts the calculation of a nonlinear problem to an optimization problem of a strictly convex function. The closed form method is used to quickly obtain an initial estimation for the iterative method to improve the efficiency and accuracy of iteration. In addition, a series of conditions on the solvability of the problem are proposed to guide the operators to select appropriate robot attitudes during the calibration process. To show the feasibility and superiority of the proposed iterative method, two other calibration methods are chosen to be compared to the proposed method through simulation and practical experiments. The comparison results verify the superiority of the proposed method in accuracy, efficiency, and stability.

Lack of monitoring of the in situ process signatures is one of the challenges that has been restricting the improvement of Powder-Bed-Fusion Additive Manufacturing (PBF AM). Among various process signatures, the monitoring of the geometric signatures is of high importance. This paper presents the use of vision sensing methods as a non-destructive in situ 3D measurement technique to monitor two main categories of geometric signatures: 3D surface topography and 3D contour data of the fusion area. To increase the efficiency and accuracy, an enhanced phase measuring profilometry (EPMP) is proposed to monitor the 3D surface topography of the powder bed and the fusion area reliably and rapidly. A slice model assisted contour detection method is developed to extract the contours of fusion area. The performance of the techniques is demonstrated with some selected measurements. Experimental results indicate that the proposed method can reveal irregularities caused by various defects and inspect the contour accuracy and surface quality. It holds the potential to be a powerful in situ 3D monitoring tool for manufacturing process optimization, close-loop control, and data visualization.

Three-dimensional structured light measurement of highly reflective surface is a challenge faced in industrial metrology. The high dynamic range (HDR) technique provides a solution by fusing images under multiple exposures; however, the process is highly time-consuming. This paper reports a new SL-based method to measure parts with highly reflective surfaces from only a single exposure. A new quantitative metric was defined to optimally select camera exposure for capturing input single-exposure images. Different from existing image gradient or entropy-based metrics, the new metric incorporates both intensity modulation and overexposure. A skip pyramid context aggregation network (SP-CAN) was proposed to enhance the single exposure-captured images. Compared with existing image enhancement methods, SP-CAN effectively preserves detailed encoded phase information near edges and corners during enhancement. Experiments with various industrial parts demonstrate that the average time cost of the proposed method was 0.6 second, which was only one tenth of the HDR method (ten exposures), and the two methods achieved similar coverage rates (97.6% vs. 98.0%) and measurement accuracy (0.040 mm vs. 0.038 mm).

Cyclic loading leads to interfacial fatigue of a graphene/polymer contact and multimode fracture of the graphene.

This article proposes a new eye-in-hand 3-D scanner-robot calibration approach to realize low data stitching errors during long-term continuous measurement. Eye-in-hand 3-D scanner–robot systems are commonly used for the complete measurement of an object under test (OUT) from multiple fields of view (FOVs). To align the multiple FOVs into a single coordinate system, marker-free stitching assisted by robot's positioning is attractive since it bypasses the cumbersome traditional fiducial marker-based method. Based on periodically capturing calibration images from a 2-D calibration target, scanner's and robot kinematic model's parameters are optimized. The challenges overcome in this article include: how to compensate for the center-detection error in scanner calibration; how to avoid the dependence of hand-eye calibration on DH parameters; and how to calculate an accurate world-to-robot transformation. These challenges were tackled by several new techniques including accurate scanner calibration with iterative refinement of control points, virtual arm-based scanner-robot kinematic modeling, and trajectory-based world-to-robot transformation calculation. Experimental results demonstrated a low-initial stitching error similar to the fiducial marker-based method (0.0446 mm versus 0.0542 mm) was achieved. The mean stitching error was effectively maintained to be $< $ 0.1 mm during the continuous measurement with an average intermittent downtime of 78 s for recalibration.

Structured light scanning has gained widespread applications in industrial metrology; however, measuring objects that have large surface-reflectivity variations is challenging. For this purpose, the high dynamic range (HDR) technique has become a standard method to fuse features under multiple exposures. Presently, in HDR measurement, manual or cumbersome exposure selection strategies are used. This article reports a new exposure selection approach. An image quality metric is designed to evaluate captured images and suppress regional overexposure, based on which a multiple-exposures selection strategy is developed. Unlike existing methods for single-optimal exposure selection, in our approach, pixel filtering is introduced to locate eligible pixels and candidate pixels in next exposures. Experimental results with industrial parts having large surface-reflectivity variations demonstrated that compared with HDR with 15 exposures (equal time intervals), our proposed approach achieved a similar surface coverage rates (97.4% versus 98.1%) and measurement accuracy (0.043 versus 0.041 mm), but only took 3–4 exposures averagely.