OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair.

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Dynamic Modeling of Weld Bead Geometry Features in Thick Plate GMAW Based on Machine Vision and Learning.

Weld bead geometry features (WBGFs) such as the bead width, height, area, and center of gravity are the common factors for weighing welding quality control. The effective modeling of these WBGFs contributes to implementing timely decision making of welding process parameters to improve welding quality and enhance automatic levels. In this work, a dynamic modeling method of WBGFs is presented based on machine vision and learning in multipass gas metal arc welding (GMAW) with typical joints. A laser vision sensing system is used to detect weld seam profiles (WSPs) during the GMAW process. A novel WSP extraction method is proposed using scale-invariant feature transform and machine learning. The feature points of the extracted WSP, namely the boundary points of the weld beads, are identified with slope mutation detection and number supervision. In order to stabilize the modeling process, a fault detection and diagnosis method is implemented with cubic exponential smoothing, and the diagnostic accuracy is within 1.50 pixels. A linear interpolation method is presented to implement sub pixel discrimination of the weld bead before modeling WBGFs. With the effective feature points and the extracted WSP, a scheme of modeling the area, center of gravity, and all-position width and height of the weld bead is presented. Experimental results show that the proposed method in this work adapts to the variable features of the weld beads in thick plate GMAW with T-joints and butt/lap joints. This work can provide more evidence to control the weld formation in a thick plate GMAW in real time.

A Combination of Keyhole GTAW with a Trapezoidal Interlayer: A New Insight into Armour Steel Welding.

The ballistic performance of armour steel welds using austenitic filler materials is poor on account of the disparity in the mechanical properties of the weld and base metals. Consequently, a novel Keyhole Gas Tungsten Arc Welding process with a trapezoidal AISI309 austenitic stainless steel interlayer was developed to tailor chemical composition and microstructure by controlling the solidification sequence. Results show that the dilution rate in the weld metal region can reach up to 43.5% by placing a specially designed interlayer in between the base metal, providing a major scope for microstructure modification. Detailed weld analysis was undertaken by X-ray diffraction, optical and secondary and transmission electron microscopy, energy dispersive spectroscopy and electron back-scattering diffraction. The results from Vickers hardness indents and Charpy impact toughness testing at -40 °C show that the properties of the weld metal region are comparable to that of the base metal. This is ascribed to the weld metal comprising a two phase microstructure of martensite and retained austenite, which contribute to improvements in strength and toughness, respectively. Furthermore, the tailored chemical composition, microstructure and low temperature phase transformation in the weld metal may reduce the tendency toward both solidification cracking and hydrogen assisted cold cracking.

Fabricating Superior NiAl Bronze Components through Wire Arc Additive Manufacturing.

Cast nickel aluminum bronze (NAB) alloy is widely used for large engineering components in marine applications due to its excellent mechanical properties and corrosion resistance. Casting porosity, as well as coarse microstructure, however, are accompanied by a decrease in mechanical properties of cast NAB components. Although heat treatment, friction stir processing, and fusion welding were implemented to eliminate porosity, improve mechanical properties, and refine the microstructure of as-cast metal, their applications are limited to either surface modification or component repair. Instead of traditional casting techniques, this study focuses on developing NAB components using recently expanded wire arc additive manufacturing (WAAM). Consumable welding wire is melted and deposited layer-by-layer on substrates producing near-net shaped NAB components. Additively-manufactured NAB components without post-processing are fully dense, and exhibit fine microstructure, as well as comparable mechanical properties, to as-cast NAB alloy. The effects of heat input from the welding process and post-weld-heat-treatment (PWHT) are shown to give uniform NAB alloys with superior mechanical properties revealing potential marine applications of the WAAM technique in NAB production.

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers.

This paper presents an approach for learning robust models of humanoid robot trajectories from demonstration. In this formulation, a model of the joint space trajectory is represented as a sequence of motion primitives where a nonlinear dynamical system is learned by constructing a hidden Markov model (HMM) predicting the probability of residing in each motion primitive. With a coordinated mixture of factor analyzers as the emission probability density of the HMM, we are able to synthesize motion from a dynamic system acting along a manifold shared by both demonstrator and robot. This provides significant advantages in model complexity for kinematically redundant robots and can reduce the number of corresponding observations required for further learning. A stability analysis shows that the system is robust to deviations from the expected trajectory as well as transitional motion between manifolds. This approach is demonstrated experimentally by recording human motion with inertial sensors, learning a motion primitive model and correspondence map between the human and robot, and synthesizing motion from the manifold to control a 19 degree-of-freedom humanoid robot.