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Wire arc additive manufacturing (WAAM) is an emerging technology in the manufacturing industry, which uses a welding arc as an energy source to fuse metal wire and deposit layer by layer. In order to promote its manufacture precision, stability, and repeatability, it’s crucial to develop a feedback control strategy for WAAM. This research implements vision-based feedback control for the layer width during the WAAM process. A WAAM system is developed using a robot and CMT welder with a visual sensing system. The dynamics of the layer width in WAAM process is modeled experimentally. An ARX dynamic model is built. Based on this model, a model predictive control (MPC) strategy is derived to regulate the WAAM process. Feedback control experiments were conducted to verify the tracking and robustness performance of the proposed MPC algorithm.

Wire arc additive manufacturing technology (WAAM) has become a very promising alternative to high-value large metal components in many manufacturing industries. Due to its long process cycle time and arc-based deposition, defect monitoring, process stability and control are critical for the WAAM system to be used in the industry. Although major progress has been made in process development, path slicing and programming, and material analysis, a comprehensive process monitoring, and control system are yet to be developed. This paper aims to provide an in-depth review of sensing and control design suitable for a WAAM system, including technologies developed for the generic Arc Welding process, the Wire Arc Additive Manufacturing process and laser Additive Manufacturing. Particular focus is given to the integration of sensor-based feedback control, and how they could be implemented into the WAAM process to improve its accuracy, reliability, and efficiency. The paper concludes by proposing a framework for sensor-based monitoring and control system for the GMAW based WAAM process. This framework provides a blueprint for the monitoring and control strategies during the WAAM process and aims to identify and reduce defects using information fusion techniques.

Geometric properties of material deposited by the wire arc additive manufacturing (WAAM) process often deviates from desired setpoints. To improve the accuracy and repeatability of the WAAM process, an effective control strategy to maintain desired deposition geometry that operates robustly under various welding conditions is required. In this work, a control strategy utilizing multi-input multi-output (MIMO) model-predictive control (MPC) is presented. This approach, based on linear autoregressive (ARX) modelling, aims to improve the accuracy and flexibility of deposited bead geometry in the WAAM process. The MPC controller updates welding parameters between successive layers by minimizing a cost function based on sequences of input variables. Measurements of deposited bead geometry are made by laser scanner and input to the linear ARX model, which then makes future bead geometry predictions. Weighting coefficients of the ARX model are trained iteratively throughout the manufacturing process. Experimental results show that the derived control strategy can reduce fluctuations in a part's height by 400% and maintain the fluctuation within an acceptable range. In addition, the fluctuations in bead width along a single weld seam was also improved by more than 50%.

Wire Arc Additive Manufacturing (WAAM) is well suited for the manufacture of sizeable metallic workpieces featuring medium-to-high geometrical complexity due to its high deposition rate, low processing conditions limit, and environmental friendliness. To enhance the current capability of the WAAM process for fabricating structures with complex geometry, this paper proposes a robot-based WAAM strategy adapted specifically for fabricating free-form parts with wire structures composed of multiple struts. Contributions in this work include: (i) The study of bead modelling, which establishes optimal welding parameter selection for the process; (ii) The novel manufacturing strategy, including the adaptive slicing methodology and height control system for accurately depositing every single strut; (iii) Detailed manufacturing procedures for multi-strut branch intersections as well as the collision-free path planning to control the overall fabrication process. To verify the effectiveness of this proposed WAAM approach, two complex wire structures were fabricated successfully, indicating the feasibility of the proposed fabrication strategy.

In the offshore oil and gas industry, the repair of damaged drilling riser buoyancy modules (DRBMs) currently involves the removal of defective regions using hand-held abrasive tools. This operation is not only time consuming, but also exposes the worker to hazardous materials generated by the grinding process. In order to increase productivity and minimize occupational health hazards, an automated robotic repair system is developed. The DRBM has a large and almost featureless surface making it particularly challenging to accurately and efficiently detect and localize all the defective regions on the surface. To address these challenges, an eye-in-hand monocular vision system is used to generate the DRBM's global surface map. The defective regions can then be identified from the map using ellipse detection algorithms, and subsequently removed with a robotic manipulator. Experimental results have shown that the proposed solution is able to accurately and efficiently remove defective regions on the surface of DRBMs.

As a new emerging technology, wire arc additive manufacturing (WAAM) has attracted extensive interests from both academia and industry during recent years. WAAM uses welding arc as an energy source to fuse metal wire and deposit layer by layer, which provides the advantages of freeform deposition. In order to improve its manufacture precision, stability and repeatability, it is necessary to develop sensing and control strategy for WAAM process. This research develops a passive visual sensing system for a robotic WAAM system. A new deep learning technique (Mask R-CNN) is proposed to detect and segment the melt pool area, and the width of melt pool can be measured based on the coordinate of the bounding rectangle. The pseudo-random ternary (PRT) signals were used to stimulate the WAAM process, and the corresponding width can be measured by the Mask R-CNN. Based on the width data and corresponding PRT input, a dynamic model of adaptive neuro-fuzzy inference system was built for the WAAM process.

In this paper, an automatic rebar counting system based on image processing and machine learning techniques is proposed. The system makes use of several image processing techniques including Canny edge detection, Circle Hough Transform (CHT) calculation and a machine learning system to accurately identify the number of individual rebar in a given bundle under various lighting conditions. This work includes a study of a number of different machine learning algorithms including decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), traditional neural network and Convolutional Neural Network (CNN). The proposed system is able to transfer the original object detection problem into a more easily solvable image classification problem and is hence achieve an overall accuracy of 95.99% in the presence of reasonable lighting conditions.

Wire arc additive manufacturing (WAAM) is an emerging process for the fabrication of complex metallic components due to its high deposition rate and low equipment costs. This paper presents a WAAM strategy adapted especially for producing freeform wire-structured parts. Contributions in this work include a bead modelling study to establish optimal welding parameters and a novel deposition strategy, which includes an adaptive slicing methodology and height control system to control the overall process. The effectiveness of this approach, termed skeleton arc additive manufacturing (SAAM), was demonstrated with the manufacture of a complex wire-structured part.

SUMMARYVariable stiffness mechanisms are able to mechanically reconfigure themselves in order to adjust their system stiffness. It is generally accepted that only antagonistic designs, featuring quadratic springs, can produce linear spring-like behaviour (i.e., a linear relationship between the displacement and its resultant force). However, these antagonistic designs typically are not as energy efficient as series-based designs. In this work, we propose a novel variable stiffness mechanism that can achieve both linear-spring behaviour whilst maintaining an energy efficient characteristic. This paper will present the working principle, mechanical design and characterization of the joints stiffness properties (verified via experimental procedure). The pros and cons of this novel design with reference to the other Variable Stiffness Actuator (VSA) designs will be discussed based on experimental results and in the context of general machining tasks.

This paper presents a novel sampling-based motion planning method for shape inspection applications with a redundant robotic system. In this paper, a 7-Degree-of-Freedom (DOF) redundant robotic system consisting of a 6-DOF manipulator and a 1-DOF turntable is used for the industrial inspection problem. A Set Covering Problem (SCP) is formulated to select suitable viewpoints that satisfy the inspection requirements, and a Generalized Travelling Salesman Problem (GTSP) is formulated to determine both the robot poses and the visiting sequences. While previous studies solve the two problems separately, we formulate the SCP and GTSP problems as a combined sequencing SC-GTSP problem. A Random-Key Genetic Algorithm (RKGA) is then used to solve the combined SC-GTSP problem in a one-step optimization process. To validate the effectiveness of our method, we applied the proposed method to several motion planning cases. The results show that the proposed method outperforms the previous approaches by requiring up to 28.1% less total inspection time.

In this paper, we propose a novel planning method for the shape inspection applications with a sensor-equipped industrial manipulator. In the proposed approach, the view planning problem is formulated as a Set Covering Problem (SCP) and the path planning problem is formulated as a Travelling Salesman Problem (TSP). The combined SCP and TSP problems are then formulated as a sequencing problem, and solved simultaneously via Random-Key Genetic Algorithm (RKGA) in a one-step optimization process. With the proposed method, time-efficient and collision-free motion plan that satisfies the requirement of the robotic inspection tasks can be generated automatically. Computational studies were performed to verify the effectiveness of the proposed planning method. The method was found to outperform other approaches by generating robot paths that complete the inspection task with shorter execution time. The Overall time reductions were found up to 28.4% compared to the previous approaches. To further demonstrate the effectiveness of the proposed method in a real-world inspection task, an experimental test was also performed using an ABB industrial robotic system equipped with an on-board 3D scanner.

Robotic friction stir processing (FSP) has not been widely researched to date. This is perhaps due to the limited force capabilities of industrial robots in comparison with dedicated commercial FSP equipment. When operating a FSP machine, the force used to plunge the tools may range from 5000 to 8000 N which is currently beyond the capability of most robots. However, the capacity of robotic manipulators is increasing, so low force friction stir processing is becoming feasible. The ability of the robot arm to apply a controlled force that is normal to a 3-dimensional surface without the need to reorient the workpiece makes it a very useful tool for FSP of complex components. In this analysis, a robot arm with a capacity of 2500 N is used to improve the surface properties of nickel aluminum bronze (NAB) using low force FSP. Multiple passes were applied to the surface of the test sample for a more consistent spread of the stir zone. The sample was then microhardness tested and demonstrated a 62 pct increase in surface hardness. Cavitation erosion testing of the original and processed surfaces was also performed as per ASTM G-32. The erosion rate of the processed NAB sample was 44 pct of the rate experienced by the original cast NAB sample. Finally, the corrosion potentials of FSP NAB were measured at 45 mV less anodic than the unprocessed material, indicating that the processed material is more noble relative to the cast NAB sample.

This paper presents a novel view planning method to generate suitable viewpoints for the reconstruction of the 3D shape of buildings, based on publicly available 2D map data. The proposed method first makes use of 2D map data, along with estimated height information, to generate a rough 3D model of the target building. Randomized sampling procedures are then employed to generate a set of initial candidate viewpoints for the reconstruction process. The most suitable viewpoints are selected from the candidate viewpoint set by first formulating a modified Set Covering Problem (SCP) which considers image registration constraints, as well as uncertainties present in the rough 3D model. A neighborhood greedy search algorithm is proposed to solve this SCP problem and select a series of individual viewpoints deemed most suitable for the 3D reconstruction task. The paper concludes with both computational and real-world field tests to demonstrate the overall effectiveness of the proposed method.

The view planning problem is the problem that involves finding suitable viewpoints for vision-related tasks such as inspection or reconstruction. In this paper, we propose a novel view planning algorithm for a camera-equipped Unmanned Aerial Vehicle (UAV) acquiring visual geometric information of target objects in its surrounding environment. The proposed model-based approach makes use of iterative random sampling and a probabilistic potential-field method to generate candidate viewpoints in a non-deterministic manner. Combinatorial optimization is then applied to select the most suitable subset of these candidate viewpoints to complete the given visual inspection or shape reconstruction task. The effectiveness of the proposed method is demonstrated through a number of computational tests that compare its overall performance against two previous methods. A field-test is also performed to demonstrate the method's applicability in a real world UAV-based shape reconstruction task of an outdoor statue.

In order to achieve higher productivity and flexibility, manufacturing industry is turning increasingly to robotics based lean automation systems. This lean approach presents a series of new challenges for the control, operation and programming of robotic hardware implemented to carry out a range of manufacturing processes. This paper reviews relevant path planning methodologies alongside a specific set of requirements for a manipulator operating in a lean automation workcell. Then, new challenges to path planning for a lean automation system are presented. Finally, a framework for a new path planner is developed and its performance is compared to existing methods.

Although robotics based flexible automation is an intriguing prospect for small to median enterprises in the era of the global competition, the complexity of programming remains one of the major hurdles limiting its applications. This paper presents an automated offline programming (AOLP) method to address this issue. AOLP is software that automatically plans and programs for a robotic welding system with high Degree of Freedoms (DOFs). It takes CAD model as input, and is able to generate the complete robotic welding code without any further programming effort.

This paper presents an offline programming (OLP) system for a complex robotic welding cell using DELMIA Automation. The goals of this research are aimed at investigating the feasibility of taking a commercially available robotic simulation package, DELMIA, and to use a Visual Basic Automation interface to reduce the programming time by creating automated ‘modules’ to carry out some of the tasks in the OLP process. The paper first investigates and presents the structure of OLP as a discreet method of individual steps. These steps are then evaluated for their potential as an automation candidate. The methods in which these steps are automated are then presented. A general analysis of the developed OLP system was carried out, providing a scope for future research and development.

Although an automated flexible production cell is an intriguing prospect for small to median enterprises (SMEs) in current global market conditions, the complexity of programming remains one of the major hurdles preventing automation using industrial robots for SMEs. This paper provides a comprehensive review of the recent research progresses on the programming methods for industrial robots, including online programming, offline programming (OLP), and programming using Augmented Reality (AR). With the development of more powerful 3D CAD/PLM software, computer vision, sensor technology, etc. new programming methods suitable for SMEs are expected to grow in years to come.