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Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

In most developing countries, the delivery of construction project is still characterised by inefficiencies resulting from the use of outdated methods and techniques, which retards project performance. Hence, the call for the implementation of innovative technologies such as humanoids in the execution of construction projects as it has been proven to be very effective in other sectors while improving productivity and quality of work. Consequently, this study looks at how humanoids can be used in the construction industry and what benefits they can bring.

The study employed a quantitative approach underpinned in post-positivist philosophical view using questionnaire as the instrument for data collection. The target respondents were construction professionals, and purposive sampling was used, while a response rate of 62.5% was gotten. The methods of data analysis were mean item score, standard deviation and one-sample t-test.

The findings revealed that humanoids can be used in progress tracking, auto-documentation and inspection and surveillance of tasks in construction activities. Also, the most important benefits of using humanoids in construction work were found to be shorter delivery times, fewer injuries and more accurate work.

The outcome of the study gives professionals and relevant stakeholders in construction and other interested parties' information about the areas where humanoids can be used and their benefits in construction.

The novelty of this study is that it is a pioneering study in South Africa on humanoids' usage in the construction industry. Also, it expands the existing borderline of the conservation of construction digitalisation for enhanced project execution.

This paper aims to develop a specific type of mobile nonrigid support friction stir welding (FSW) robot, which can adapt to aluminum alloy trucks for rapid online repair.

The friction stir welding robot is designed to complete online repair according to the surface damage of large aluminum alloy trucks. A rotatable telescopic arm unit and a structure for a cutting board in the shape of a petal that was optimized by finite element analysis are designed to give enough top forging force for welding to address the issues of inadequate support and significant deformation in the repair process.

The experimental results indicate that the welding robot is capable of performing online surface repairs for large aluminum alloy trucks without rigid support on the backside, and the welding joint exhibits satisfactory performance.

Compared with other heavy-duty robotic arms and gantry-type friction stir welding robots, this robot can achieve online welding without disassembling the vehicle body, and it requires less axial force. This lays the foundation for the future promotion of lightweight equipment.

The designed friction stir welding robot is capable of performing online repairs without dismantling the aluminum alloy truck body, even in situations where sufficient upset force is unavailable. It ensures welding quality and exhibits high efficiency. This approach is considered novel in the field of lightweight online welding repairs, both domestically and internationally.

This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.

In this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.

Numerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.

A new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.

The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.

It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.

Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.

The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

This paper aims to examine how collaborative supplier development (SD) activities, supplier capabilities and buyer–supplier relationship interrelate in technology-based, luxury product business contexts characterized by small volumes, difficult targets and resource constraints relative to those targets.

Using inductive case research method, the authors investigate multiple embedded cases involving six dyadic buyer–supplier relationships of two luxury product manufacturers in the motorcycle and automotive industries. Each dyad represents an important sub-system for which the buying firm committed significant SD efforts to help the supplier successfully achieve difficult targets.

The analysis reveals how paradoxical tensions might emerge as the firms engage in successful SD activities, which could lead to decreasing relationship commitment ultimately resulting in the termination of the relationship. The authors utilize the “value co-creation and value capture” paradox framework to understand the SD and relationship dynamic and characterize it as developing-leveraging paradox to explain its dualities, i.e. commitment-based SD efforts (increasing value co-creation), and unilateral leveraging of the newly acquired capabilities (increasing value capture) by both the buyer and the supplier. Overemphasis on value capture by one of the exchange partners spurs a detrimental vicious cycle leading to the decline of the relationship.

The study explains the paradoxical dynamics that may emerge in SD activities of innovative, technologically complex, luxury product firms. The findings contribute to the SD literature by highlighting how learnings from SD activities could contribute to the dark sides of buyer–supplier relationship. The technologically complex, luxury product contextual characteristics of the study may limit the generalizability of the study findings.

The study provides novel insights into the emergence and management of paradoxes in buyer–supplier relationships, in terms of virtuous and vicious dynamics of developing-leveraging.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.

First, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.

The study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.

The proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.

The purpose of this paper is to demonstrate the impact of the welding sequence on the substrate plate distortion during the wire and arc additive manufacturing (WAAM) process. This paper also aims to show the capability of finite element simulations in the prediction of those thermally induced distortions.

An experiment was conducted in which solid aluminum blocks were manufactured using two different welding sequences. The distortion of the substrates was measured at predefined positions and converted into bending and torsion values. Subsequently, a weakly coupled thermo-mechanical finite element model was created using the Abaqus simulation software. The model was calibrated and validated with data gathered from the experiments.

The results of this paper showed that the welding sequence of a part significantly affects the formation of thermally induced distortions of the final part. The calibrated simulation model was able to capture the different distortion behavior attributed to the welding sequences.

Within this work, a simulation model was developed capable of predicting the distortion of WAAM parts in advance. The findings of this paper can be used to improve the design of WAAM welding sequences while avoiding high experimental efforts.