[数据驱动的智能服务]数据驱动的复杂产品智能服务技术与应用

随着传感器、数据采集装置和其他具备感知能力的模块在复杂产品服务运行阶段的应用，复杂产品运维系统的数字化和智能化程度越来越高，具有实时、多源、异构、海量等特性的数据成为提高复杂产品系统可靠和低成本运行的决策依据，数字孪生技术提供了一种有效途径。介绍了数据驱动的复杂产品智能服务研究进展；分析了数据驱动的智能服务基本特征与框架模型；提出了数据驱动的复杂产品智能服务方法，主要包括面向服务的复杂产品建模与仿真方法、数据驱动的服务需求获取与精准分析预测方法、基于数字孪生的设备故障识别与动态性能预测方法、数据驱动的装备视情维修与备件库存联合多目标决策优化方法、基于数字孪生的复杂产品辅助维修技术、多要素协同的复杂装备能效精准分析预测方法、基于数据挖掘的复杂产品运行优化控制方法等；给出了智能服务系统的应用案例。所提出的框架和方法可为现代制造服务的智能化转型升级提供参考。     

带检测环节的制造单元布局优化

针对带检测环节的智能制造单元，考虑其具有随机性因素的特点，本文对其机器布局问题进行研究。首先对该智能制造单元的生产过程及特点进行分析，建立其机器布局优化问题的随机非线性整数规划的数学模型；然后建立该智能制造单元对应的仿真模型，对该优化问题进行求解；最后分析抽检率的变化对最优解造成的影响，并对该智能制造单元的性能指标进行方差分析，研究不同的布局方案和抽检率对系统性能的影响程度。研究成果为企业进行智能制造单元的机器布局提供决策依据。     

Dynamic facility layout problem in footwear industry

Dynamic facility layout problem (DFLP) deals with the arrangement of machines in a site as to minimize the sum of materials handling and re-layout costs by considering multi periods. The DFLP studies in the literature provide several different algorithms and utilize the well known test problems to assess their performance. However, real life applications are overlooked. The industries such as footwear and clothing are prone to seasonal demand changes. Therefore, time horizons and layout/re-layout of the machines within the facility should be studied carefully. This study considers a footwear facility and several scenarios are generated by using the real life data. A clonal selection based algorithm is proposed to solve the real life DFLP. The performance of the algorithm, further the effect of time periods on solution quality and applicability of the results are tested and promising results are obtained.     

A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction

The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.     

Additively manufactured respirators: quantifying particle transmission and identifying system-level challenges for improving filtration efficiency

The COVID-19 pandemic has disrupted the supply chain for personal protective equipment (PPE) for medical professionals, including N95-type respiratory protective masks. To address this shortage, many have looked to the agility and accessibility of additive manufacturing (AM) systems to provide a democratized, decentralized solution to producing respirators with equivalent protection for last-resort measures. However, there are concerns about the viability and safety in deploying this localized download, print, and wear strategy due to a lack of commensurate quality assurance processes. Many open-source respirator designs for AM indicate that they do not provide N95-equivalent protection (filtering 95% of SARS-CoV-2 particles) because they have either not passed aerosol generation tests or not been tested. Few studies have quantified particle transmission through respirator designs outside of the filter medium. This is concerning because several polymer-based AM processes produce porous parts, and inherent process variation between printers and materials also threaten the integrity of tolerances and seals within the printed respirator assembly. No study has isolated these failure mechanisms specifically for respirators. The goal of this paper is to measure particle transmission through printed respirators of different designs, materials, and AM processes. The authors compare the performance of printed respirators to N95 respirators and cloth masks. Respirators in this study printed using desktop- and industrial-scale fused filament fabrication processes and industrial-scale powder bed fusion processes were not sufficiently reliable for widespread distribution and local production of N95-type respiratory protection. Even while assuming a perfect seal between the respirator and the user’s face, although a few respirators provided >90% efficiency at the 100−300 nm particle range, almost all printed respirators provided <60% filtration efficiency. Post-processing procedures including cleaning, sealing surfaces, and reinforcing the filter cap seal generally improved performance, but the printed respirators showed similar performance to various cloth masks. The authors further explore the process-driven aspects leading to low filtration efficiency. Although the design/printer/material combination dictates the AM respirator performance, the identified failure modes originate from system-level constraints and are therefore generalizable across multiple AM processes. Quantifying the limitations of AM in producing N95-type respiratory protective masks advances understanding of AM systems toward the development of better part and machine designs to meet the needs of reliable, functional, end-use parts.     

钛/铝异种金属冷金属过渡增材制造

采用TC4和ER2319焊丝直流/变极性冷金属过渡实现异种金属电弧增材制造，通过金相显微镜、扫描电镜、透射电镜、能谱、硬度试验、纳米压痕以及拉伸试验等方法对钛/铝构件界面组织特征与力学性能进行分析.结果表明，在钛合金表面堆积铝合金时，只有少量的钛合金熔化，钛原子扩散到液态铝合金中，形成不同长度的TiAl₃金属间化合物.10 μm左右的反应层在钛/铝界面形成.邻近钛侧的反应层均匀连续，靠近铝合金一侧的反应层呈现长条状或块状.界面反应层的显微硬度介于钛合金和铝合金显微硬度之间.构件的最高抗拉强度为111 MPa.     

原子及近原子尺度制造——制造技术发展趋势

探讨了制造技术发展趋势，论述了制造历史发展的三个阶段，指出原子及近原子尺度制造（ACSM）是下一代制造技术的主流发展趋势，对未来科技发展和高端元件制造具有重要意义。详细分析阐述了ACSM的主要研究内容及关键科学问题，并提出了相应的建议和措施。     

水下连接器毂座加工工艺研究

针对水下连接器典型工件毂座的加工难点,通过研究其结构特点与材料特性,制定了工艺路线,并通过工艺试验,研究了抑制颤振和自适应插补在复杂型面数控加工中实现技术,确定了关键工序的工艺参数,满足了水下连接器毂座的加工精度和使用性能要求,也为类似结构加工制造提供借鉴。     

Microstructure and oxidation behavior of NiCr-chromium carbides coating prepared by powder-fed laser cladding on titanium aluminide substrate

In the present study, a NiCr–Cr₃C₂ powder mixture was prepared by mechanical alloying and then coated on titanium aluminide substrates by the powder-fed laser cladding process using a set of optimum parameters. The high temperature oxidation behavior of the substrate and coating was studied by isothermal annealing at 900 °C for 5 h. It was found that the microstructure of the coating is composed of γ solid solution with different chromium carbide phases (Cr₃C₂, Cr₇C₃ and Cr₂₃C₆). The presence of different chromium carbides in the microstructure of coating can be attributed to the partial melting of primary Cr₃C₂ and the formation of non-equilibrium carbide phases during rapid cooling of laser cladding. The NiCr-chromium carbide laser cladded coating samples showed superior oxidation resistance compared to the substrate. The oxidation mechanism of both coating and substrate follow the parabolic law, where the parabolic rate constant of the coating was 20% of that of the substrate at 900 °C. Time-of-Flight Secondary Ion Mass Spectroscopy (ToF-SIMS) and Grazing Angle X-Ray Diffraction (GAXRD) analysis revealed that the surface of the oxide layer formed on the NiCr-chromium carbides coating and the substrate is mostly composed of Cr₂O₃ and TiO₂, respectively.     

Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.