20MnCr5圆棒锯切困难原因分析

某厂在锯切20MnCr5圆棒过程中锯条容易断裂，采用光学显微镜(OM)和扫描电子显微镜(SEM)等观察分析手段，对锯条容易断裂的原因进行分析。分析结果表明：锯条断裂的原因为加工20MnCr5过程中粘刀崩齿。针对此类加工缺陷，提出了相应的改进措施。     

FPGA-based implementation of classification techniques: A survey

Recently, a number of classification techniques have been introduced. However, processing large dataset in a reasonable time has become a major challenge. This made classification task more complex and expensive in calculation. Thus, the need for solutions to overcome these constraints such as field programmable gate arrays (FPGAs). In this paper, we give an overview of the various classification techniques. Then, we present the existing FPGA based implementation of these classification methods. After that, we investigate the confronted challenges and the optimizations strategies. Finally, we highlight the hardware accelerator architectures and tools for hardware design suggested to improve the FPGA implementation of classification methods.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

Progressive failure analysis of notched composite plate by utilizing macro mechanics approach

In this study, gradual and sudden reduction methods were combined to simulate a progressive failure in notched composite plates using a macro mechanics approach. Using the presented method, a progressive failure is simulated based on a linear softening law prior to a catastrophic failure, and thereafter, sudden reduction methods are employed for modeling a progressive failure. This combination method significantly reduces the computational cost and is also capable of simultaneously predicting the first and last ply failures (LPFs) in composite plates. The proposed method is intended to predict the first ply failure (FPF), LPF, and dominant failure modes of carbon/epoxy and glass/epoxy notched composite plates. In addition, the effects of mechanical properties and different stacking sequences on the propagation of damage in notched composite plates were studied. The results of the presented method were compared with experimental data previously reported in the literature. By comparing the numerical and experimental data, it is revealed that the proposed method can accurately simulate the failure propagation in notched composite plates at a low computational cost.     

我国鲜食玉米标准体系及营养标准研究

近年来我国鲜食玉米产业发展迅猛, 但同时也暴露出诸多产业乱象。梳理现有鲜食玉米标准体系是促进产业标准化、规范化的重要举措。本文通过对我国现行鲜食玉米标准进行梳理, 总结出目前我国鲜食玉米标准体系中抽样及一般性检测标准较完善、技术规程体系成熟、标准体系框架已建立的特点; 同时提出我国鲜食玉米标准体系存在技术规程标准内容重复、品质分级标准混乱、缺乏营养评价指标相关标准、感官评价标准混乱、缺乏甜糯玉米和笋玉米的相关标准、标准体系结构不合理等的问题; 并针对上述问题提出对应应统一技术规程标准, 建立营养评价体系, 统一品质分级标准, 统一感官评价指标, 完善甜糯玉米和笋玉米相关标准的解决办法。为鲜食玉米标准体系的建设及规范化提供参考和建议。     

Phase-change regulation criterion based on size-dependent lattice distortion rate and born theory for VO2 nanomaterials

Vanadium dioxide (VO₂) is a promising thermally induced phase transition material because of the abrupt changes in electrical and optical properties. However, the high phase transition temperature of VO₂ and its unspecified modulation relationship need to be resolved urgently. Herein, we proposed a simple and precise regulation criterion for VO₂ materials based on size-dependent lattice distortion rate and Born theory. The results indicated that the application of a tensile stress changed the elastic properties of the VO₂, which promoted VO₂ phase transition, and regulated the phase transition temperature. Moreover, the specific modulation relationship between the stress and phase transition temperature of VO₂ was confirmed experimentally. These results show that our criterion provides theoretical guidance to regulate VO₂ thermally induced phase transition materials.