Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

国外几种脱硫醇工艺技术

硫醇是一种易挥发、有毒、腐蚀活性强的物质，在环保要求越来越严格的今天，必须将原油和凝析气中的硫醇加以脱除，才能达到环保要求。文章介绍了目前世界上采用的几种脱硫醇工艺。     

非织造布印花技术探讨

介绍了应用于非织造布印花的技术及工艺，对非织造布印花时应注意的事项进行了说明。     

柳钢35t转炉方坯连铸机系统优化措施

介绍柳钢35t转炉方坯连铸机系统优化措施，通过优化使生产稳定性提高，铸坯质量得到改善，提高各项经济技术指标。     

Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.     

S—135钢板热轧工艺研究

S—135钢板是水电站用高强耐磨不锈钢板,既要求强度高、韧性好,又要求耐磨、耐蚀性强,生产难度较大。除我厂外,目前国内尚无其它厂家能够生产这种钢板。该钢板在加热轧制,冷却过程中要解决很多技术难点。我们通过采用特殊的控制轧制和控制冷却工艺,很好地解决了诸多技术难点,使S—135钢板的开发研制取得了成功。为电站用高强度耐磨不锈钢生产提供了宝贵的经验,也为控制轧制及控制冷却技术在中板生产行业的应用起到了积极的推动作用。     

C pm MPPAC for manufacturing quality control applied to the precision voltage reference process

The multiprocess performance analysis chart (MPPAC) based on the process capability index C_pm, called C_pm MPPAC, is developed for analysing the manufacturing quality of a group of processes in a multiple process environment. The C_pm MPPAC conveys critical information of multiple processes regarding the departure of the process and process variability from one single chart. Existing research works on MPPAC are restricted to obtaining quality information from one single sample of each process ignoring sampling errors. The information provided from existing MPPAC charts, therefore, is unreliable and misleading and results in incorrect decisions. In this paper, we consider the natural estimator of C_pm based on multiple samples. Based on the natural estimator of C_pm, we consider the sampling errors by providing an explicit formula with the Matlab program to obtain the estimation accuracy of the C_pm. We tabulate the sampling accuracy of C_pm for sample size determination so that the engineers/practitioners can use it for their in-plant applications. An example of multiple precision voltage reference (PVR) processes is presented to illustrate the applicability of C_pm MPPAC for manufacturing quality control.     

衡阳钢管厂Φ89mm半浮芯棒连轧管机组工艺及设备结构特点

衡阳钢管厂Φ８９ｍｍ半浮芯棒连轧管机组是一种新型机组，其选配的变形工艺路线能很好地适应生产小规格的热轧无缝钢管。本文论述了该机组的工艺及设备结构特点。