Journal of Manufacturing Systems最新文献

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.

Machining feature recognition serves as a foundational step in process planning, crucial for translating design information into manufacturing information. Traditional rule-based methods require extensive manual rule definition, prompting researchers to develop learning-based methods using data-driven algorithms. However, existing learning-based methods typically demand substantial data annotation and show limitations in machining feature segmentation. To address these issues, this paper introduces a novel learning-based machining feature recognition method. The proposed method leverages self-supervised learning to autonomously extract valuable intrinsic information from unlabeled data and incorporates a discriminative loss function to improve feature segmentation performance, thereby enhancing feature recognition results under conditions of limited labeled data. Specifically, the self-supervised learning network is first pre-trained on a large amount of unlabeled point cloud data representing CAD models and then fine-tuned with labeled data using the discriminative loss function. The fine-tuned network can be employed for recognizing machining features. Experimental results demonstrate that the proposed approach is effective during pre-training and improves feature recognition performance with limited amounts of labeled data, potentially reducing annotation efforts and associated costs.

Metal Additive Manufacturing (MAM) has seen significant growth in recent years, with sub-processes like Metal Material Extrusion (MEX) reaching industrial readiness. MEX, known for its cost-effectiveness and ease of integration, targets a distinct market segment compared to established high-end MAM processes. However, despite technological improvements, its overall integration into the industry as a viable manufacturing technology remains incomplete. This paper investigates the competitiveness of MEX, specifically its integration into the supply chain and the implications on cost and carbon emissions. Utilizing real-world data, the research develops a multi-objective optimization (MOO) model for a four-echelon supply chain including suppliers, airports, production facilities, and customers. The optimization model is combined with a previously developed cost model for MEX to optimize facility location in Norway using the NSGA-II algorithm. Employing a case study approach, the paper examines the production of an industrial part using stainless steel 17-4PH, detailing concrete process costs and system-level costs across four different production scenarios: 10, 100, 1,000, and 10,000 parts. The findings indicate MEX’s potential for cost-effective production at low and diversified volumes, supporting the trend towards customization and manufacturing flexibility. However, the study also identifies significant challenges in maintaining competitiveness at higher production volumes. These challenges underline the necessity for further advancements in MEX technology and process optimization to enhance its applicability and efficiency in larger-scale production settings.

Tool Wear Monitoring (TWM) plays a vital role in safeguarding product quality and enhancing machining efficiency. TWM technology mainly includes physics-based models and data-driven methods. However, physical models established under simplified or idealized conditions struggle to capture the complexity of machining processes. Moreover, the predictive efficacy of data-driven methods is heavily contingent upon the quantity of labeled data available. Addressing these issues, a hybrid-driven physics-informed Gaussian process regression model (PIGPR) is proposed. First, a health indicator construction strategy based on feature fitness analysis and Gaussian weighted moving average filtering is proposed to eliminate interference and redundancy in the measurement signal and improve monitoring efficiency. Second, a novel explicit physical model of tool wear was developed, with a determination coefficient of at least 0.98. On this basis, health indicator and proposed priori physical models are employed to constrain the mean function of the Gaussian process regression (GPR), combining data mining and physical models to provide prediction guidance for key physical domain knowledge for the hybrid model. Third, grid search algorithm is used to optimize the model parameters, adaptively identify tool wear conditions, and 95 % prediction confidence interval is given to provide more reliability. Finally, nine sets of experiments with varying cutting settings confirmed the PIGPR model's dependability. The findings demonstrate that the suggested hybrid approach significantly enhances the prediction precision of tool wear, achieving an accuracy of 0.997. Compared to the solely data-driven GPR model, the width and variance of the 95 % confidence interval decreased by 46.44 % and 60.80 %, respectively, which demonstrates that incorporating prior physical knowledge significantly enhances the smoothness and reliability of predictions.

Accurate anomaly detection (AD) of machine tools is crucial to ensure the quality and efficiency of the manufacturing processes. Due to the lack of tool anomaly information, it is difficult for AD model to precisely capture the distribution of health states and then obtain a discriminative decision boundary. Current methods try to reconstruct the normal data distribution without restricting the abnormal, resulting in the unacceptable overlap between normal and abnormal regions and finally leading to high false alarm rate. To tackle these issues, a hierarchical augmented autoencoder is proposed for AD of machine tools during manufacturing. First, a skip-connected autoencoder is built to basically learn the normal representations of multi-sensor data in an unsupervised manner. Then, to improve further emphasis the reconstruction on normality and suppress that on anomalies, we propose hierarchical memory modules to store multi-scale normal prototypical patterns, using them as a prior to guide the reconstruction with preference. Finally, A compound metric loss function is designed to measure data similarity considering both distance and angle perspectives, which can restrain noise interference and enhance model robustness. Extensive experiments are conducted on real-world CNC machine tool datasets, the proposed method achieves better performance for unsupervised AD compared with other typical methods.

Metadata are essential to the manufacturing sector. Encompassing a broad spectrum of information related to manufacturing processes, voluminous metadata are extensively obtained from sensing networks and experimentally validated finite element (FE) models. In the era of digital manufacturing, where metadata holds immense potential, its full value remains largely untapped without thorough analysis and characterisation. Yet, an overwhelming majority of the manufacturing metadata obtained during production lacks crucial information and is categorised as ‘fragmental data’, containing only a few (e.g., 1–2) essential pieces of information. Extremely sparse information within the fragmental data hinders the further analysis and characterisation of underlying scientific patterns. To address this challenge, two physics-informed filters, the probability density function filter (PDFF) and feature-driven neighbour filter (FDNF), were developed and embedded within the Evolutionary Binary (EB) algorithm. These filters enabled the alignment by identifying the origins of a set of naturally unattributed fragmental data, taking the digital characteristics space (DCS) of manufacturing processes as an alignment reference. This was realised by comparing the thermo-mechanical digital characteristics (DC), such as the temperature DC, to the counterparts stored in the DCS. An overall accuracy of 90 % was achieved when identifying the origins of unattributed fragmental hot stamping data using PDFF with a minimum length of 10 and FDNF with minimum length of 25. Results demonstrate a novel methodology to unlock the inherent values from unattributed fragmental data that contains extremely sparse information, thereby revolutionising insights into advanced manufacturing sciences.

Prognostic maintenance (PM) aims to monitor the running status and promptly detect potential failures to improve the availability and productivity of the equipment. The dimensional accuracy and surface integrity of the machined parts are directly influenced by the cutting tools. Thus, tool health monitoring (THM) is crucial to ensure the optimal in-service performance of the parts. Nevertheless, the variability of operating conditions, including milling parameters, workpiece materials, etc., typically results in insufficient fault data to train the model for new conditions, thus presenting a challenge in predicting the remaining useful life (RUL) of cutting tools. To address the above issue, this study proposes a multi-source online transfer learning framework for predicting the RUL of cutting tools cross various operating conditions. A source selection strategy is initially proposed to filter the source conditions that contribute to the target modeling from the numerous candidate operating conditions. Then, online transfer learning is employed to transfer valuable knowledge from source domains to target domains while updating the target data online to reflect the actual machining scene. In contrast to the traditional transfer learning approaches, this study utilizes a hybrid physics-data model as the base learner to improve the predictive precision of the RUL in the future scenarios. The results demonstrate its generalizability and flexibility in accurately tracking tool degradation status, and the prediction accuracy of the RUL reaches more than 93 % in various target operating conditions. This study provides reliable technical support for THM in machining actual complex components.