IET Collaborative Intelligent Manufacturing最新文献

Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real-time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty-seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.

The medium-thick plate is an important type of steel product widely used in construction and engineering machinery. The orders are usually characterised by multiple specifications and small quantities. The plate design is an important part in the production process of medium-thick plate, which includes the combination of sub-plates and the size design of the motherboard. A multi-objective model for medium-thick plate design is proposed based on the 2D bin packing model, comprehensively considering spatial and size constraints of the plate production. A two-stage genetic algorithm (TSGA) is developed to solve the proposed model. In the first stage, an improved GA is used to optimise the corresponding relationship between the sub-plates and the slab, as well as the size of the motherboard. In the second stage, an exact algorithm based on the integer programming model is applied to calculate the order layout to minimise the surplus materials. To validate the proposed method, computational experiments are conducted based on actual production data from a steel plant. The experimental results show the effectiveness of the TSGA algorithm in solving the plate design problem.

Industrial defect detection is an important part of intelligent manufacturing, and Internet of things (IoT)-based defect detection is receiving more and more attention. Although deep learning (DL) can help defect detection reduce the cost and improve the accuracy of traditional manual quality inspection, DL requires huge computational resources and is difficult to be simply deployed on IoT devices with limited computational power and memory resources. Digital signal processor (DSP) is an important IoT device with small size, high performance and low energy consumption, which has been widely used in intelligent manufacturing. In order to perform accurate defect detection on DSP, the authors proposed various optimisation strategies and then used a parallel scheme to scale the model to execute on multiple cores. The authors’ method evaluated it on Northeastern University Surface Defect Dataset, Magnetic Tile Defect Dataset, Rail Surface Defect Dataset and Silk Cylinder Defect Dataset, and the experimental results showed that the authors’ method obtains faster speeds without accuracy loss compared to running the same Convolutional Neural Networks model on a mainstream desktop CPU. This means that the authors’ method can realise efficient and accurate defect detection on IoT devices with limited computational power and memory resources, which opens up new possibilities for future development in the field of smart manufacturing.

3D meshes simplification plays an important role in many industrial domains. The two goals of Delaunay mesh simplification are maintaining high geometric fidelity and reducing mesh complexity. However, they are conflicting and cannot solved by gradient. Such limitation prevents existing Delaunay mesh simplification to obtain a small enough number of vertices and promising fidelity at the same time. To address these issues, this paper proposes an evolutionary multi-objective approach for Delaunay mesh simplification. Firstly, the authors replace the previous fixed error-bound threshold by the designed adaptive segment-specific thresholds. Secondly, a constrained simplification is performed through a series of edge collapses that satisfy both Delaunay and error constraints. Next, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the multi-objective problem to search for the optimal trade-off threshold sequences. Finally, a fine-tuning method is designed to further enhance the geometric fidelity of the simplified mesh. Experimental results demonstrate that the authors’ method consistently achieves a satisfactory balance between the approximation error and number of vertices, outperforming existing state-of-the-art methods.

Nowadays, multi unmanned aerial vehicle (multi-UAV) systems have been widely used in battlefield. The rationality of mission plan can directly affect the effectiveness of multi-UAV system. The existing multi-UAV task allocation model lack a comprehensive modelling of task pre-allocation and task reallocation issues. However, in actual task execution, task pre-allocation and task reallocation are a holistic problem. Therefore, based on the background of multi-UAV cooperative reconnaissance, the authors establish a multi-UAV cooperative reconnaissance task pre-allocation and reallocation model (MCRTPR). There are two kinds of task allocation in MCRTPR model. One is task pre-allocation, which is a static task allocation before the mission begin. Another is task reallocation, that is a dynamic task allocation during the mission. For task pre-allocation, a particle swarm optimisation algorithm based on experience pool (EPPSO) is proposed. And for task reallocation, the authors design a partial task reallocation algorithm based on contract network protocol (CNP-PTR). The experimental results show that, compared with some state-of-the-art algorithms, EPPSO can get the lowest fitness value under various experimental conditions, and CNP-PTR is able to handle task reallocation problem caused by multiple kinds of dynamic events.

As a better alternative to the energy-intensive process of recycling waste sheet steel (WSS) from the exterior components of end-of-life vehicles to produce new steel, the feasibility of remanufacturing WSS into angle mesh steel (AMS) for construction applications is evaluated. A remanufacturing unit with a capacity of 1278 m²/day of WSS (30,000 vehicle/year) was evaluated using a triple-bottom-line sustainability analysis of the technological, economic, and environmental feasibilities by hybrid defuzzification–curve-fitting, solid-waste recoverability management, and weighting methods. Based on the remanufacturing productivity, an economic feasibility index was calculated considering the sales potential and profit, while the energy and CO₂ emission savings were used to evaluate the environmental feasibility. The technical feasibility considered machine parameters and topological properties of the WSS. The Volkswagen Passat has the best remanufacturability of 200 analysed vehicle models. Remanufacturability indexes of 0.61 and 0.86 were calculated, giving remanufacturing efficiencies of 58%–82%. All feasibility indexes exceed literature thresholds, indicating that the proposed remanufacturing process is a sustainable business strategy and contributes to the United Nations Sustainability Goals of climate action; responsible consumption and production; no poverty; and industry, innovation, and infrastructure.

OpenAI's ChatGPT (GPT-4) ushers in a superior mode of computer interaction through natural language dialogues. Notably, it generates not only engaging dialogues but also codes aligned to queries and requirements. The potential of ChatGPT in hardware implementation via natural language is implemented and a strategy for “asking the right questions” is outlined. The versatility of ChatGPT is demonstrated through three mainstream hardware designs – systolic array, ResNet and MobileNet accelerators – comparing these with hand-coded designs. The evaluation metrics include design quality, design efforts, and limitations of code generated by ChatGPT/GPT-4/Cursor against prevalent High-Level Synthesis or hand-coded HDL designs. Consequently, a novel design workflow is proposed and the constraints of using GPT, particularly in AI accelerators, are highlighted.

A spectrum of control methods in human–robot interaction was investigated, ranging from direct control to telepresence with a virtual representation of the robot arm. A total of 24 participants used a setup that included a Franka Emika Panda robot arm, Varjo XR-3 head-mounted display, and Leap Motion Controller. Participants performed a box-and-block task using the bare hand (A), and under five gesture-controlled robotic operation methods: direct sight (B), sight via video-feedthrough (C), in a 3D telepresence environment with (D) and without (E) virtual representation of the robot arm, and using a 2D video feed (F). The number of grabbing attempts did not differ significantly between conditions, but local operation (B & C) yielded more transferred blocks than teleoperation (D–F). Teleoperation using a 3D presentation was advantageous compared to teleoperation using a 2D video feed, as demonstrated by lower peak forces and smaller range in gripper heights in conditions D and E compared to condition F, a finding supported by analyses of the head movement activity. Finally, the bare hand yielded the best performance and subjective ratings. In summary, teleoperation using a 3D presentation provided a smoother interaction than teleoperation with a 2D video feed. However, direct human interaction remains a benchmark yet to surpass.

The reliability of well-performing production processes not only depends on internal but also on external factors in the upstream supply chain. Therefore, companies require fast and reliable information exchange with cooperating organisations. Since existing researches usually put focus on reviewing intra-organisational solutions, the authors have conducted a survey to capture the current state and needs regarding the inter-organisational information exchange between German companies. The main outcomes of the authors’ survey are outlined. In a first step, the current status of digitalisation in the companies is analysed. The authors have found that the use and implementation of modern methods correlates positively with the company size. Taking a look on the current methods for inter-organisational information exchange in a second step, it is seen that only a few companies are completely satisfied with their current methods. Companies see the greatest potential for the improvement of information exchange in standardisation and the increase of required resources. This indicates that companies would be best supported by the development of solutions that can be implemented easily and help to form the heterogeneous landscape of data exchange strategies towards a structured, standardised way.

In the era of the fourth industrial revolution, many certain technologies such as Internet of Things (IoT) or Internet of Service (IoS) have been implemented for the improvement of current system. However, most of them did not ensure the real-time performance which is inevitable for modern motion systems. The implementation of Ethernet-based protocol, rapid data exchange and synchronised transmission are proper for the novelties of slave controller. An integration of the decentralised network into the motion control system is introduced firstly. To demonstrate the conceptual design, some descriptions of motivations and related definitions are explained. Then, the structure of hardware components and internal connections are mentioned. Later, the software architecture including the data exchange and flowchart of motion program, is described in detail. To verify the feasibility and effectiveness of the proposed approach, some technical analysis of timing synchronisation and tracking deviation between our method and the others are carried out. In addition, several practical validations are conducted in the same conditions. The contributions of the authors are (i) to firstly present the concept of controller design using the real-time technology, (ii) to illuminate both hardware design and software design of the proposed approach and (iii) to analyse the superior performance in delay time between our method and the others as well as validate the real-world results to prove the effectiveness and feasibility. From the authors’ achievement, it is observed that these results can be broadly purposeful to the sustainable development of Industry 4.0.