Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling

With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.     

Artificial intelligence capability: Conceptualization,measurement calibration,and empirical study on its impact on organizational creativity and firm performance

Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

人工气候老化后状态调节时间对芳纶纤维拉伸测试的影响

依据GJB 8496—2015标准中的拉伸方法,对人工气候老化前及老化后不进行状态调节、状态调节1 h、状态调节24 h、状态调节48 h、状态调节96 h后的芳纶纤维的拉伸强度进行测量、比较,从而得到结论:芳纶纤维在人工气候老化后,样品的性能有一定程度下降。在刚刚结束老化后立即进行测试,性能下降达到峰值,待状态调节24 h以后,其老化后性能逐步达到一个相对稳定的状态。建议在实际测试中,人工气候老化后的样品应该在老化结束后,放入恒温恒湿间调节24 h以后再测试。     

用于LED光源的反光杯准直效果研究

为研究抛物面型反光杯用于LED光源准直时的特点,基于TracePro仿真了LED光源在不同尺寸反光杯下的有效射程和光斑直径,分析了反光杯和LED尺寸对射程和光斑直径的影响。仿真结果表明:射程与反光杯的口径和深度都有关系。高度相同时,反光杯口径越大,射程越大;口径相同时,射程随反光杯高度增加先增大后减小。LED光源经反光杯准直,较短距离的光斑中心光强很低,形成光环。LED光源面积直接影响目标面光斑的大小,对射程影响较小。     

基于演化算法的复杂机械广义优化设计实现策略研究

针对复杂机械广义优化设计过程中优化模型不易获得这一难题，利用人工神经网络的高度非线性是非线性映射能力，将优化建模转换为求取设计变量到设计方案的映射关系，构建了复杂机械广义优化柔性建模方法；引入模糊评价理论，设计了基于演化计算的优化方法，给出了优化流程图。该策略特别适用于复杂机械概念设计阶段的方案优化。     

水厂生产运行管理决策支持系统的开发

介绍了水厂生产管理决策支持系统的设计原理和方法，其中主要讲述了数据库和知识库的建立，并阐述了该系统的运行功能、管理和维护方法。     

使用人工神经网络对强力旋压有限元模拟进行参数预选

在筒形件强力旋压领域内，介绍了使用人工神经网络的手段，在MARC有限元模拟的前处理中实现参数预选的方法。作为与有限元模拟相结合的旋压智能化设计的一项关键技术，能够达到减少有限元模拟的误差和工作量的目的。