Search Results (175)

In the interior decoration panel industry, automated production lines have become the standard configuration for large-scale enterprises. However, during the panel processing procedures such as sanding and painting, the loss of traditional identification markers like QR codes or barcodes is inevitable. This creates a critical technical bottleneck in the assembly stage of customized or multi-model parallel production lines, where identifying individual panels significantly limits production efficiency. To address this issue, this paper proposes a high-precision measurement method based on close-range photogrammetry for capturing panel dimensions and hole position features, enabling accurate extraction of identification markers. Building on this foundation, an identity discrimination method that integrates weighted dimension and hole position IDs has been developed, making it feasible to efficiently and automatically identify panels without physical identification markers. Experimental results demonstrate that the proposed method exhibits significant advantages in both recognition accuracy and production adaptability, providing an effective solution for intelligent manufacturing in the home decoration panel industry. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

In response to the growing complexity of modern process manufacturing systems, this paper proposes a novel simulation framework named the Process–Equipment–In-Process State (PEI) simulation method, which introduces a unified and structured approach to modeling multi-stage industrial processes. Unlike conventional simulation approaches that rely on ad hoc or loosely organized modules, the PEI method decomposes the simulation system into three core and interoperable modules: Process Structure (P), Equipment Behavior (E), and In-Process State (I). This modular abstraction facilitates the decoupling of model logic. It also enables a structure-driven simulation execution mechanism. In this structure, the process topology governs task scheduling; equipment models translate control inputs into physical conditions; and state models simulate material evolution accordingly. A complete simulation case involving water mixing, heat exchange, and slurry transformation demonstrates the method’s capability to support traceable state evolution, logical task flow, and extensible model binding. The results demonstrate that the proposed method enables module decoupling, clear simulation pathways, and traceable state changes, providing effective support for structured modeling and behavioral evolution analysis in process manufacturing. Full article

In the context of carbon neutrality and smart manufacturing, balancing the challenge of carbon and operational efficiency has become a hotspot issue. However, within the specific stage of multi-equipment collaborative manufacturing operational coupling in the production process, multi-state characteristics of equipment operation, multidependencies among operational states, the multi-source of carbon emissions, and spatiotemporal sequence coupling raise the dynamics and complexity of carbon emission modeling and carbon efficiency evaluation. Therefore, a novel methodology to integrate carbon efficiency into a multi-equipment collaboration manufacturing service cell (MECMfg-SC) is proposed in this paper. The stage of multi-equipment collaboration manufacturing operational coupling (MECMfg-OC) in the process of multi-equipment collaboration manufacturing is presented and explained. Then, the operational coupling energy consumption model is constructed based on the MECMfg-OC. The environmental cost performance indicators for smart manufacturing systems, including energy efficiency evaluation (EEe) indicators and carbon efficiency evaluation (CEe) indicators, are proposed. At last, a ball screw smart workshop in a leading Chinese NEV enterprise is introduced to verify the proposed approach. Empirical results confirm the approach’s effectiveness and practical viability. Full article

With the upgrading of consumption driving the transformation of the home furnishing industry towards personalized customization, panel furniture enterprises are confronted with a core contradiction between large-scale production and individualized demands: The traditional production management model is unable to cope with the chaos in production scheduling, resource waste, and low collaborative efficiency caused by small-batch and multi-variety orders. This paper proposes an intelligent production scheduling system that integrates Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), and Warehouse Management System (WMS), and elaborates on its data processing methods and specific application processes in each production stage. Compared with the traditional model, it effectively overcomes limitations such as coarse-grained planning, delayed execution, and information islands in middle-level systems, achieving deep collaboration between planning, workshop execution, and warehouse logistics. Empirical studies show that this system not only can effectively reduce the production costs of customized panel furniture manufacturers, enhance their market competitiveness, but also provides a digital transformation framework for the entire customized panel furniture manufacturing industry, with significant theoretical and practical value. Full article

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

Anomaly detection in industrial imaging is critical for ensuring quality and reliability in automated manufacturing processes. While recently several methods have been reported in the literature that have demonstrated impressive detection performance on standard benchmarks, they necessarily rely on computationally intensive CNN architectures and post-processing techniques, necessitating access to high-end GPU hardware and limiting practical deployment in resource-constrained settings. In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency. The core of our method consists of two main components. First is a PCA-based anomaly detection module that specifically exploits near-zero variance features. Contrary to traditional PCA methods, which tend to focus on the high-variance directions that encapsulate the dominant patterns in normal data, our approach demonstrates that the lower variance directions (which are typically ignored) form an approximate null space where normal samples project near zero. However, the anomalous samples, due to their inherent deviations from the norm, lead to projections with significantly higher magnitudes in this space. This insight not only enhances sensitivity to true anomalies but also reduces computational complexity by eliminating the need for operations such as matrix inversion or the calculation of Mahalanobis distances for correlated features otherwise needed when normal behavior is modeled as Gaussian distribution. Second, our framework consists of a cascaded multi-stage decision process. Instead of combining features across layers, we treat the local features extracted from each layer as independent stages within a cascade. This cascading mechanism not only simplifies the computations at each stage by quickly eliminating clear cases but also progressively refines the anomaly decision, leading to enhanced overall accuracy. Experimental evaluations on MVTec and VisA benchmark datasets demonstrate that our proposed approach achieves superior anomaly detection performance (99.4% and 91.7% AUROC respectively) while maintaining a lower computational overhead compared to other methods. This framework provides a compelling solution for practical anomaly detection challenges in diverse application domains where competitive accuracy is needed at the expense of minimal hardware resources. Full article

This study proposes a continuous preparation strategy for poly (l-lactic acid) (PLLA) scaffolds with oriented hierarchical microporous structures for bone repair. A PLLA-oriented multi-stage microporous bone repair scaffold (hereafter referred to as the oriented multi-stage microporous scaffold) was designed using a novel extrusion foaming technology that integrates fused deposition modeling (FDM) 3D printing with supercritical carbon dioxide (SC-CO₂) microcellular foaming technology. The influence of the 3D-printed structure on the microcellular morphology of the oriented multi-stage microporous scaffold was investigated and optimized. The combination of FDM and SC-CO₂ foaming technology enables a continuous extrusion foaming process for preparing oriented multi-stage microporous scaffolds. The mechanical strength of the scaffold reached 15.27 MPa, meeting the requirements for bone repair in a low-load environment. Notably, the formation of open pores on the surface of the oriented multi-stage microporous scaffold positively affected cell proliferation, differentiation, and activity, as well as the expression of anti-inflammatory and pro-inflammatory factors. In vitro cell experiments (such as CCK-8) showed that the cell proliferation rate in the oriented multi-stage microporous scaffold reached 100–300% after many days of cultivation. This work provides a strategy for the design and manufacture of PLLA scaffolds with hierarchical microcellular structures and biocompatibility for bone repair. Full article

As industrial companies transition from the Industry 4.0 stage to the more human-centric and resilient Industry 5.0 paradigm, there is a growing need for structured assessment tools to prioritize modern technologies. This paper presents an integrated multi-criteria decision analysis (MCDA) approach to support the strategic assessment of technologies from three complementary perspectives: economic, organizational, and technological. The proposed model encompasses six key transformation areas and 22 technologies representing both the Industry 4.0 and 5.0 paradigms. A hybrid approach combining the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods is used to identify cause–effect relationships between the transformation areas and to construct technology rankings in each of the assessed perspectives. The results indicate that technologies such as the Internet of Things (IoT), cybersecurity, and supporting IT systems play a central role in the transition process. Among the Industry 5.0 technologies, hyper-personalized manufacturing, smart grids and new materials stand out. Moreover, the economic perspective emerges as the dominant assessment dimension for most technologies. The proposed analytical framework offers both theoretical input and practical decision-making support for companies planning their transformation towards Industry 5.0, enabling a stronger alignment between implemented technologies and long-term strategic goals. Full article

Combustion processes in compression ignition engines lead to the inevitable generation of nitrogen oxides, which cannot be limited to the currently desired levels just by optimising the in-cylinder processes. Therefore, simulation-based engine development needs to include all engine-related aspects which contribute to tailpipe emissions. Among them, the SCR (selective catalytic reduction) aftertreatment-related processes, such as urea–water solution injection, urea decomposition, mixing, NOx catalytic reduction, and deposits’ formation, are the most challenging, and require as much attention as the processes taking place inside the cylinder. Over the last decade, the urea-SCR aftertreatment systems have evolved from underfloor designs to close-coupled (to the engine) architecture, characterised by the short mixing length. Therefore, they need to be tailor-made for each application. This study presents the CFD-based development of a multi-platform SCR system with a short mixing length for mobile non-road applications, compliant with Stage V NRE-v/c-5 emission standard. It combines multiphase dispersed flow, including wall wetting and urea decomposition kinetic reaction modelling to account for the critical aspects of the SCR system operation. The baseline system’s design was characterised by the severe deposit formation near the mixer’s outlet, which was attributed to the intensive cooling in the mounting area. Moreover, as the simulations suggested, the spray was not appropriately mixed with the surrounding gas in its primary zone. The proposed measures to reduce the wall film formation needed to account for the multi-platform application (ranging from 56 to 130 kW) and large-scale production capability. The performed simulations led to the system design, providing excellent UWS–exhaust gas mixing without a solid deposit formation. The developed system was designed to be manufactured and implemented in large-scale series production. Full article

Current non-destructive testing (NDT) methods, such as those based on wave velocity measurements, lack the sensitivity necessary to detect early-stage damage in concrete structures. Similarly, common signal processing techniques often assume linearity and stationarity among the signal data. By analyzing wave attenuation measurements using advanced signal processing techniques, mainly Hilbert–Huang transform (HHT), this work aims to enhance the early detection of damage in concrete. This study presents a novel energy-based technique that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert spectrum analysis (HSA), to accurately capture nonlinear and nonstationary signal behaviors. Ultrasonic non-destructive testing was performed in this study on manufactured concrete specimens subjected to micro-damage characterized by internal microcracks smaller than 0.5 mm, induced through controlled freeze–thaw cycles. The recorded signals were decomposed from the time domain using CEEMDAN into frequency-ordered intrinsic mode functions (IMFs). A multi-criteria selection strategy, including damage index evaluation, was employed to identify the most effective IMFs while distinguishing true damage-induced energy loss from spurious nonlinear artifacts or noise. Localized damage was then analyzed in the frequency domain using HSA, achieving an up to 88% reduction in wave energy via Marginal Hilbert Spectrum analysis, compared to 68% using Fourier-based techniques, demonstrating a 20% improvement in sensitivity. The results indicate that the proposed technique enhances early damage detection through wave attenuation analysis and offers a superior ability to handle nonlinear, nonstationary signals. The Hilbert Spectrum provided a higher time-frequency resolution, enabling clearer identification of damage-related features. These findings highlight the potential of CEEMDAN-HSA as a practical, sensitive tool for early-stage microcrack detection in concrete. Full article

Manufacturing industries are increasingly focused on enhancing energy efficiency while maintaining high levels of production throughput and product quality. However, most existing energy-saving control (EC) methods overlook the influence of production quality on overall energy performance. To address this challenge, this paper proposes a dynamic EC method for multistage manufacturing systems with product quality scrap. The method utilizes a Markov decision process (MDP) framework to dynamically control the operational states of all machines based on real-time system conditions. Specifically, for two-stage manufacturing systems, the dynamic EC problem is formulated as an MDP, and the optimal EC policy is obtained by a dynamic programming algorithm. For multistage manufacturing systems, to address the curse of dimensionality, an aggregation procedure is proposed to approximate the optimal EC policy for each machine based on the results of two-stage manufacturing systems. Finally, numerical experiments are performed to demonstrate the effectiveness of the proposed dynamic EC method. For a five-stage manufacturing system, the proposed dynamic EC policy achieves a 13.55% reduction in energy consumption costs and a 3.02% improvement in system throughput compared to the baseline. Extensive case studies demonstrate that the dynamic EC policy consistently outperforms three well-studied methods: the station-level EC policy, the upstream-buffer EC policy, and the energy saving opportunity window policy. Moreover, the results confirm the effectiveness of the proposed method in capturing the influence of product quality scrap on the system energy efficiency. This study presents a sensor-integrated methodology for EC, contributing to the advancement of smart manufacturing practices in alignment with Industry 4.0 initiatives. Full article

Soft-growing robots based on the eversion principle are renowned for their ability to rapidly extend along their longitudinal axis, allowing them to access remote, confined, or otherwise inaccessible spaces. Their inherently compliant structure enables safe interaction with delicate environments, while their simple actuation mechanisms support lightweight and low-cost designs. Despite these benefits, implementing effective navigation mechanisms remains a significant challenge. Previous research has explored the use of pneumatic artificial muscles mounted externally on the robot’s body, which, when contracting, induce directional bending. However, this method only offers limited bending performance. To enhance maneuverability, pneumatic artificial muscles embedded in between the walls of double-walled eversion robots have also been considered and shown to offer superior bending performance and force output as compared to externally attached muscle. However, their adoption has been hindered by the complexity of the current manufacturing techniques, which require individually sealing the artificial muscles. To overcome this multi-stage fabrication approach in which muscles are embedded one by one, we propose a novel single-step method. The key to our approach is the use of non-heat-sealable inserts to form air channels during the sealing process. This significantly simplifies the process, reducing production time and effort and improving scalability for manufacturing, potentially enabling mass production. We evaluate the fabrication speed and bending performance of robots produced in this manner and benchmark them against those described in the literature. The results demonstrate that our technique offers high bending performance and significantly improves the manufacturing efficiency. Full article

Despite its proven effectiveness in inventory management across various industries, Demand-Driven Material Requirements Planning (DDMRP) remains largely a manual process, with few studies investigating its numerical integration. This research proposes a novel multi-stage production control framework grounded in DDMRP principles, enabling effective scheduling of production orders based on either demand forecasts or actual demand, when available. A mixed-integer programming (MIP) model is developed to capture the dynamic interactions between demand, buffer positioning, and replenishment policies, supporting reactive production planning in smart, reconfigurable manufacturing environments. To identify the optimal buffer locations, a Genetic Algorithm (GA) is employed. The MIP model provides the GA with production planning outputs used to evaluate the fitness of decisions regarding buffer placement. To demonstrate the effectiveness of this hybrid GA–MIP approach, simulations are conducted on three representative production configurations. The results show that the proposed method significantly improves the theoretical performance of each configuration by determining optimal buffer locations and planning replenishments, achieving a better balance between inventory levels and demand fulfillment. Full article

In this paper, a novel through-silicon via (TSV) fabrication strategy based on through-hole structures is proposed for low-cost and low-complexity manufacturing. Compared to conventional TSV fabrication processes, this method significantly simplifies the process flow by employing double-sided liner deposition, double-sided barrier layer/seed layer formation, and double-sided Cu electroplating. This method enhances the TSV stability by eliminating Cu contamination issues during chemical–mechanical polishing (CMP), which are a common challenge in traditional blind via fabrication processes. Additionally, the liner and barrier layer/seed layer achieve a high step coverage exceeding 80%, ensuring excellent conformality and structural integrity. For electroplating, a multi-stage bi-directional electroplating technique is introduced to enable void-free Cu filling in TSVs. The fabricated TSVs exhibit an ultra-low leakage current of 135 fA at 20 V, demonstrating their potential for advancing 3D integration technologies in heterogeneous integration. Full article