Nonlinear aircraft system identification using artificial neural networks enhanced by empirical mode decomposition

Fused deposition modelling (FDM) is well-known as an inexpensive and the most commonly used additive manufacturing process. In FDM, build orientation is one of the critical factors that affect the quality of the printed part. However, the activity of determining a build orientation for an FDM part, i.e., part orientation for FDM, usually relies on the knowledge and experience of domain experts. This necessitates an approach that enables the capture, representation, reasoning, and reuse of the data and knowledge in this activity. In this paper, a description logic (DL) ontology-supported part orientation approach for FDM is presented. Firstly, a set of top-level entities are created to construct a DL ontology for FDM part orientation. Then a DL ontology-supported alternative orientation generation procedure, a DL ontology-supported factor value prediction procedure, and a DL ontology-supported optimal orientation selection procedure are developed successively. After that, the application of the presented approach is illustrated via part orientation on six FDM parts. Finally, the effectiveness and efficiency of the presented approach are demonstrated through theoretical predictions and printing experiments and the advantages of the approach are demonstrated via an example. The demonstration results suggest that the presented approach has satisfying effectiveness and efficiency and provides a semantic enrichment model for capturing and representing FDM part orientation data and knowledge to enable automatic checking, reasoning, query, and further reuse.

/pdf/description-logic-ontology-supported-part-orientation-for-30a3alnd.pdf

Description Logic Ontology-Supported Part Orientation for Fused Deposition Modelling

Vat photopolymerization is characterized by its high precision and efficiency, making it a highly promising technique in ceramic additive manufacturing. However, the process faces a significant challenge in the form of recoating defects, necessitating real-time monitoring to maintain process stability. This article presents a defect detection method that leverages multi-image fusion and deep learning for identifying recoating defects in ceramic additive manufacturing. In the image fusion process, multiple single-channel recoating images captured by monitoring camera positioned near the photopolymerization equipment are merged with curing area mask image to create a three-channel color image. The recoating images suffer from perspective distortion due to their side view. To facilitate fusion with the curing area image, image rectification technique is applied to correct the perspective distortion, transforming the side view recoating images into a top-down view. Subsequently, the fused images are processed using a channel-wise YOLO (You Only Look Once, CW-YOLO) method to extract features, enabling the distinction of various types of defects. When compared with other deep learning models, CW-YOLO achieves higher detection accuracy while maintaining a detection rate of 103.58fps, meeting the requirements for real-time detection. Furthermore, the paper introduces the F1 score as a comprehensive evaluation metric, capturing both detection accuracy and recall rate. The results show that the F1 score is enhanced by approximately 10% after image fusion, demonstrating that the proposed method can significantly improve defect detection, particularly in cases involving difficult-to-distinguish defects like material shortages and scratches. 

Multi-Image Fusion-Based Defect Detection Method for Real-Time Monitoring of Recoating in Ceramic Additive Manufacturing

An aerodynamic parameters regression estimate method based on Wavelet Neural Network by Partial Least Square feature extraction is proposed. This method can overcome problems of data noise and multiple correlations among parameters, accurately describe the dynamic characteristics of flight vehicle. Firstly, using Partial Least Square extracts basic feature of training samples in flight data. The second, aerodynamic parameters are regression estimated based on Wavelet Neural Network by using basic feature extracted. Finally, the method proved by experiment is effective and feasible to be used for flight vehicle aerodynamic parameters regression estimate.

http://ieeexplore.ieee.org/iel7/6621666/6639389/06639753.pdf

PLS -WNN algorithm and its applications in aerodynamic parameters regression estimate

In the context of a CNC machining application, this study proposes an alternate approach for creating toolpaths designed explicitly for machining rectangular bosses utilizing the STEP AP242 file format as a data source. The suggested methodology ensures data accuracy and integrity maintenance by extracting exact geometric information from digital models using Geometric Data Extraction (GDE) tools. The value of the Cartesian multi-point decimal point in the data structure and variations in the reference number of the Cartesian point every time a user accesses and converts to a STEP (AP203) file are two drawbacks of AP203 when compared to the widely utilized AP203 in prior technique studies. This method makes it possible to create G-code efficiently, which is necessary for managing computer numerical control (CNC) machines, by utilizing the power of STEP AP242 and GDE. The process entails finding and separating the rectangular island milling features in the STEP AP242 files, taking out critical geometric characteristics, and creating toolpaths that are optimal for CNC machining. Through this approach, the final G-code instructions are guaranteed to be precisely calibrated to consider every island feature's distinct quality while preserving overall machining efficiency. Several real-world case studies and valuable applications are investigated to confirm the efficacy of the suggested method. The results show significant increases in productivity, decreased waste material, and improved precision of CNC machining, all of which contribute to a more effective and economical production process.

Advancements in CNC Machining: Rectangular Boss Feature Validation and Toolpath Precision using STEP AP242 and B-Rep Data Structure

Automatic selection system of the building orientation based on double-layer priority aggregation multi-attribute decision-making

The paper proposed an algorithm which can get over the BP algorithm's shortcomings of slow convergence speed, computation complexity and local minimum by using the UKF to estimate the parameters of WNN. Then it takes the phenomenon of aerodynamic modeling of quasi-steady stall for ATTAS aircraft as applying background and uses the algorithm of BP, EKF and UKF to train the WNN respectively. From the simulation results we can see that the UKF algorithm is faster in training speed and more accurate in prediction when compared with BP and EKF and it is competent for modeling of complex nonlinear aerodynamic phenomenon as well.

Aerodynamic force modeling of quasi-steady stall phenomenon based on UKF-WNN

Missile equipment support system is a complex system involving mission profile, operating environment, support personnel, resource scheduling, and other factors, and most of the equipment failures are a process of continuous evolution over time. Based on the characteristics of equipment functions or performance parameters and current detection methods, only part of equipment health degradation trend can be monitored. Therefore, it is necessary to carry out the support system simulation considering the equipment failure and health state comprehensively. In this paper, a mission-oriented modeling framework of missile equipment support system based on agent is proposed. Agents of equipment, support, management, and environment are abstracted. The behavior logic, information transmission link, and interaction mechanism among agents are explained. The failure occurrence handling mechanism and health state degradation handling mechanism are determined. The equipment recovery behavior model, including equipment health state control model, corrective maintenance behavior model, and system reconstruction behavior model, are constructed, and the simulation process and implementation method are explained. Finally, a certain type of missile equipment is taken as an example for simulation. The results show that the mean time between failures can be extended and the times of corrective maintenance can be reduced by maintenance support with integrated consideration of equipment failure and health deterioration. In addition, the simulation results show the variation trend of equipment availability, which can lay a solid foundation for the optimization design of equipment support resources in the future, so as to improve the availability of equipment in the whole life cycle.

/pdf/mission-oriented-missile-equipment-support-system-modeling-3hsqq2sz.pdf

Mission-Oriented Missile Equipment Support System Modeling: Considering the Failure and Health State

针对导弹装备, 尤其是新研装备或历史经验数据欠缺的装备, 在设计阶段保障方案规划不合理的问题, 提出了一种多扰动下面向设计的导弹装备保障系统仿真方法。构建了分层分块的混合递阶仿真框架, 抽象出装备保障过程中的装备类、保障类、管理类、环境类智能体, 并根据保障流程确定了这4类智能体的交互机制及信息传递链路。分析了影响装备保障决策的2类扰动, 建立了装备故障扰动和人员失误扰动模型, 采用双时钟机制实现仿真过程中的扰动注入, 并根据装备维修保障流程及智能体状态跃迁过程建立了保障系统的恢复行为模型。选取了某型导弹装备开展实例仿真, 将保障资源仿真结果与装备初始设计方案和装备实际需求情况进行对比, 验证了仿真系统的正确性, 为在设计阶段制定合理科学的装备保障方案提供了新思路。

Simulation on design-oriented missile equipment support system under multiple disturbances

This paper takes typical air defense equipment as an object, analyzes the characteristics of the data in the whole life cycle of equipment, sorts out the data mining requirements in support design of equipment in demonstration, scheme and development stages, including spare parts design analysis, fault analysis, preventive maintenance analysis, state-based maintenance analysis, using data analysis requirements involved in data mining, and puts forward the realizing methods of data analysis and mining. It can greatly improve the comprehensive support design level of air defense equipment.

Application of data mining in air defense equipment support design

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