先进制造环境下质量控制的理论与方法

分析介绍了AMT环境下质量控制的新特点及其主要的理论与方法,其中包括统计过程控制(SPC)、统计过程诊断(SPD)、统计过程调整(SPA)、质量方法论、集成质量系统(IQS)、面向质量的设计(DFQ)、虚拟环境下的质量保证技术以及质量控制的自动化与智能化技术。     

灰色预测算法及其在质量控制图中的应用

介绍了一种质量控制图和灰色预测算法,接着提出了灰色预测算法与质量控制图相结合的一个可行的实施方案,并加以实例说明。     

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively.     

航空专用焊接数据库及专家系统

焊接数据库及专家系统是焊接数字化体系的重要组成部分。近年来,随着航空制造业向数字化、智能化方向转型步伐的加快,吸引了越来越多的航空企业开展航空专用焊接数据库及专家系统设计与开发方面的工作。目前在航空制造领域建立的焊接数据库,已实现航空材料焊接基础数据、焊接参数数据、焊接性能数据及典型案例数据的有效存储和共享。焊接工艺设计专家系统的应用,可辅助焊接工艺文件自动设计与管理,大大缩短了焊接工艺文件编制周期;焊接缺陷诊断专家系统可根据缺陷特征判断缺陷类型,或根据缺陷类型分析缺陷成因,对控制和预防各类缺陷的产生及控制焊接质量具有重要的实际应用价值;焊接信息管理平台在数据库平台、专家系统平台以及用户权限管理基础上,实现了焊接工艺流程的在线智能化协同办公。结合航空制造业焊接数字化发展进程,在已有焊接数据库及专家系统研究的基础上,把焊接质量预测专家系统和制造业大数据分析挖掘技术运用到航空焊接数据库及专家系统设计中,将成为未来航空焊接专家系统的发展趋势。     

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control (MSPC) and stream of variations (SoV) is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error (OSFE) is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.     

激光选区熔化增材制造缺陷智能监测与过程控制综述

激光选区熔化（SLM）技术被认为是最有应用前景的增材制造技术之一,已应用于航空航天、医疗器械等领域。然而,如何确保构件质量的可靠性和制造的可重复性是SLM面临的最大挑战,已被认为是限制SLM及其他金属增材制造技术发展和工业应用的最大壁垒。其中,主要原因是SLM过程中会产生难以控制的缺陷。因此,对SLM进行过程监测和实时反馈控制是解决这一挑战的重要研究方向,也已成为学术界和工业界的研究热点之一。通过对近十年该领域的文献调研,综述了金属激光增材制造中常见的冶金缺陷及其产生机理,对金属增材制造过程产生的信号及其监测手段,如声信号、光信号及热信号等进行了详细描述;总结了信号数据的处理方法,包括传统的统计处理方法和新兴的基于机器学习的智能监测方法;随后,综述了金属增材制造过程的质量控制方法,包括非闭环控制和闭环控制,并对全文进行了总结,展望了未来SLM智能监测和控制领域值得深入的研究方向。     

Collaborative manufacturing technologies of structure shape and surface integrity for complex thin-walled components of aero-engine: Status,challenge and tendency

Presently, the service performance of new-generation high-tech equipment is directly affected by the manufacturing quality of complex thin-walled components. A high-efficiency and quality manufacturing of these complex thin-walled components creates a bottleneck that needs to be solved urgently in machinery manufacturing. To address this problem, the collaborative manufacturing of structure shape and surface integrity has emerged as a new process that can shorten processing cycles, improve machining qualities, and reduce costs. This paper summarises the research status on the material removal mechanism, precision control of structure shape, machined surface integrity control and intelligent process control technology of complex thin-walled components. Numerous solutions and technical approaches are then put forward to solve the critical problems in the high-performance manufacturing of complex thin-wall components. The development status, challenge and tendency of collaborative manufacturing technologies in the high-efficiency and quality manufacturing of complex thin-wall components is also discussed.     

CFRP切削过程中的监测控制研究进展

CFRP加工过程的实时监测和控制是提升零件最终加工质量的重要手段.本文从加工过程物理仿真、数学模型及智能模型三方面总结了 CFRP切削加工预测方法研究现状.同时,基于切削过程状态信息获取、特征信息提取以及监测模型构建等方面概述了刀具磨损和加工质量在线监测方法研究进展.在此基础上,探讨了关于在CFRP切削过程中切削力和振...     

面向航空智能制造的DT与AI融合应用

针对航空装备复杂制造场景下制造过程管控维度多尺度大、制造资源组成复杂性高、质量问题跟踪定位难度大等问题，结合数字孪生（DT）与人工智能（AI）技术特点，开展了面向航空智能制造的DT与AI融合应用研究。基于数字孪生与人工智能应用现状，系统性地阐述了数字孪生与人工智能融合机理，分析了支撑数字孪生与人工智能融合驱动航空智能制造的关键技术和数字孪生与人工智能融合驱动的AI控制中心构建涉及的关键问题，在此基础上重点讨论了加工制造过程自适应控制、智能车间生产过程智能管控、制造过程资源调度与优化决策、产品智能质量控制等应用场景，为数字孪生与人工智能在航空智能制造融合应用提供参考。     

先进复合材料制造及过程控制技术

先进复合材料在民用飞机中的大量应用带来了突出的减重优势和经济效益,同时对复合材料制造技术和成型质量提出了高标准要求。针对先进复合材料在民用飞机中的应用,介绍了国内外先进的大型民用飞机中典型复合材料构件的制造方案,并按照过程质量控制要求,从原材料、工艺过程及产品检验三方面进行了复合材料构件制造过程质量控制分析。分析表明,发展基于自动化的先进复合材料整体成型技术并实现复合材料制造过程的高水平控制,对满足民机适航性和经济性的高标准要求、实现复合材料在民机领域的大规模应用与批产具有重要意义。