Journal of Manufacturing Systems最新文献

Real-time optimization of machining processes for aerospace structural components is imperative due to the difficult-to-cut materials and complex structures. Effective feed rate control in CNC machining plays a key role in achieving high-quality results. While current research trends in mass production emphasize the utilization of adaptive control algorithms and controllers within machining systems, there remains a need to enhance the adaptability of these control systems. This study introduces an active-passive hybrid feed rate control system designed to maintain consistently stable cutting conditions and extend tool life. The hybrid feed rate control system combines offline active pre-compensating, a scheduled pre-compensating feed rate profile, and an online feed rate passive fine-tuning with a real-time adaptive control loop in computer numerical control (CNC) machining. The response speed is enhanced by offline active pre-compensation, whereas the control precision is improved by online passive fine-tuning with a fuzzy controller. Four control cases were tested separately throughout the tool lifespan, including the conventional and adaptive control methods. The proposed adaptive control method reduced the maximum slope from 3.6 to 1.2, demonstrating superior performance compared to both its individual components and other case studies. The results showed a significant 25 % increase in tool life, with a slight decrease in machining efficiency of 7.35 % during the entire tool lifespan.

The emergence of cyber or platform-based manufacturing as-a-service is rapidly disrupting the way discrete parts are sourced and manufactured. However, the centralized business model of cyber manufacturing as-a-service platforms raises concerns about data ownership and access control of independent manufacturing suppliers. Contrary to centralized platforms, cyber manufacturing as-a-service aims to connect designers with geographically distributed manufacturers by serving as a broker who matches the query part design requirements with the manufacturing capabilities of candidate suppliers in its network. One of the key challenges in realizing the vision of cyber manufacturing as-a-service is the lack of a computationally efficient method for manufacturing capability search while maintaining data security of the proprietary datasets of the suppliers in the network. In this paper, we propose a federated learning approach that utilizes a deep unsupervised part retrieval model (FL-DUPR) to learn a federated embedding of suppliers’ manufacturing capabilities without directly accessing their proprietary datasets. We demonstrate through two case studies that this approach yields a supplier selection accuracy of 89 % when the manufacturing capabilities of the suppliers do not overlap, and a multi-label supplier selection accuracy of 87 % when there are significant overlaps in the suppliers’ manufacturing capabilities. We also show that our unsupervised learning approach outperforms the baseline supervised learning classification model trained under the same federated learning framework. The results demonstrate the promise of the proposed federated embedding approach for automated identification of the required manufacturing capabilities offered by various suppliers without directly accessing their proprietary data, thus paving the way for a more secure cyber manufacturing as-a-service business model.

As the production orders are becoming multi-category and small-batch in the era of product personalization, these require frequent reconfiguration of reconfigurable flexible assembly system for cross-category products (RFAS-CCP). However, there is no suitable theoretical assembly model and systematic implementation framework. We first propose a five-element assembly model (FAM) for RFAS-CCP, i.e. product, process, resource, knowledge, and decision. The product, process, and resource element describe the objects, steps to be assembled, and the tools, fixtures, and other equipment used for assembly, respectively. The knowledge element is a form representation of various heterogeneous data, such as a knowledge graph. The decision element includes various assembly methods to achieve assembly automation, flexibility, and intelligence. Then, in order to standardize and easy the frequent reconfiguration process, we reorganize various decision methods into a three-phase systematic implementation framework according to which stage they are used: design, configuration, and operation phases. The design phase methods primarily design various assembly modules for a product family, forming an assembly resource library. The configuration phase methods primarily configure suitable assembly lines for a specific product in the product family. The operation phase methods monitor the status of the assembly line and ensures its stable operation through health management. Finally, the effectiveness and practicality of the proposed five-element assembly model and three-phase systematic implementation framework are experimented with a pressure reducing valve product.

Advanced planning and scheduling (APS) addresses the complex and uncertain nature of production control. A digital twin (DT), which incorporates simulations through cyber-physical integration, provides an advanced functionality for APS. To facilitate efficient design and implementation, a DT-based APS must satisfy three requirements: technical functionalities for resilience, robust models for diverse operational constraints, and efficient interoperability through cyber-physical integration. Although several studies have proposed the use of DT as a primary technology for APS, proposals that address the process, functionality, integration, and information models are lacking. Additionally, the existing asset descriptions cannot adequately capture the sophisticated characteristics of DT and necessary informational elements for APS. Thus, this study designed a process model, functionalities, and integration models for the DT-based APS and asset descriptions for snapshot synchronization. Crucial service-compositions and functionalities were defined using work-center-level lifecycles. Consequently, a process model was developed, which focused on core activities for resilience. Moreover, horizontal integration between DT and control functionalities and vertical integration between DT and standards, were proposed to enhance the DT-based APS. The proposed method effectively managed the product, process, plan, plant, and resource classes by ensuring adherence to asset administration shell principles. To validate the effectiveness of the proposed methods, two work centers with distinctly different characteristics were employed and demonstrated dominant preventive measures compared to static functionality-based methods. The primary contributions encompass the facilitation of integration and interoperability within a DT-based APS. The proposed methods support the advanced characteristics of DT, ensuring robustness and neutrality across heterogeneous operational contexts.

Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. However, existing traditional technology which is borescope inspection is labor-intensive, time-consuming, and experience-dependent. To endow this technology with intelligence, a novel superpixel perception graph neural network (SPGNN) is proposed by utilizing a multi-stage graph convolutional network (MSGCN) for feature extraction and superpixel perception region proposal network (SPRPN) for region proposal. First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations. Then, MSGCN composed of several GCN blocks extracts graph structure features and performs graph information processing at graph level. Last but not least, the SPRPN is proposed to generate perceptual bounding boxes by fusing graph representation features and superpixel perception features. Therefore, the proposed SPGNN always implements feature extraction and information transmission at the graph level in the whole SPGNN pipeline, to alleviate the reduction of receptive field and information loss. To verify the effectiveness of SPGNN, we construct a simulated blade dataset with 3000 images. A public aluminum dataset is also used to validate the performances of different methods. The experimental results demonstrate that the proposed SPGNN has superior performance compared with the state-of-the-art methods.

In aircraft manufacturing, where diverse materials, including Carbon Fiber-Reinforced Plastics (CFRP), aluminum, and titanium alloys, are employed, the assembly process heavily relies on creating thousands of holes. These holes accommodate bolts and rivets, facilitating the secure interlocking of structural components within the aircraft fuselage. The proliferation of sensor systems in this domain has led to a substantial increase in data generation during the hole-making process, offering a compelling opportunity to optimize the production system. In this context, this article is dedicated to harnessing the data collected from the production system of a commercial aircraft to refine the assembly process, with a specific focus on reducing consumable costs. The primary approach involves developing a real-time Tool Wear Monitoring System by comparing the performance of Linear Regression, Lasso Regression, Ridge Regression, k-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting Machine Learning models. Using a scale of the general drill condition as an outcome, the Gradient Boosting Regressor has shown outstanding results. Notably, the residuals consistently exhibited zero-centered errors in training and test sets. However, it suggests that further enhancements are needed to surpass human-level performance in predicting tool conditions because of the quality and quantity of available data.