Prediction Model for BTA Deep-Hole Machining using Fuzzy Clustering Approach: Experimental Study

In this article, a subtractive clustering-based fuzzy identification method and a Sugeno-type fuzzy inference system are used for modeling in metal cutting. This approach is considered with its application on the experimental study of Boring and Trepanning Association (BTA) deep-hole drilling. The model for the surface roughness is identified by using the cutting speed and feed as input data and roughness as the output data. Using subtractive clustering in both input and output spaces performs the model-building process. Minimum error model is obtained through enumerative search of clustering parameters. The fuzzy model obtained is capable of predicting the surface roughness for a given set of inputs (speed and feed). Therefore, the operator can predict the quality of the surface for a given set of working parameters and will then be able to set the machining parameters to achieve a certain surface quality. The fuzzy model is verified experimentally by further experimentation using different sets of inputs. The tool life is also investigated using the same approach. The fuzzy inference system obtained is capable of predicting the tool life for a given set of cutting parameters. Therefore, the operator will be able to predict how many minutes the cutting tool is going to last and will set the time for the next tool change.     

一种T-S型动态模糊推理模型的建模算法研究

以一种进化聚类算法（ECM）为基础,提出了一种新的T-S型动态模糊推理模型的建模算法.以往许多神经模糊模型都不适用于自适应在线学习,而文章模型能够实时地调整模糊规则库及规则参数,具有较强的在线学习能力.仿真结果表明,该方法是有效的.     

有序聚类法在锦凌水库风化岩体定量分带中的应用

应用有序聚类法对辽宁省锦凌水库坝基的风化岩体进行定量分带,所得结果与地质观察方法、规范法等所得出的结果进行了分析比较.采用岩体风化差异系数法进行综合分析,最终推荐按照有序聚类方法进行风化岩体分带.     

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。     

A fuzzy clustering algorithm enhancing local model interpretability

In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in literature. Some examples illustrate the proposed approach.     

H原子对固态合金化颗粒表面成份分布影响的研究

本文研究了氢化物ＴｉＨ２对Ｆｅ＋Ｔｉ机械合金化非晶储氢合金的表面成份偏聚和性能的影响。应用Ｘ射线光电子谱（ＸＰＳ）和俄歇电子谱（ＡＥＳ）对样品表面由表及里逐层测量成份分布。同时应用热重分析方法研究了样品氧化性能和产物。研究结果表明，在机械合金化过程中，Ｈ原子不仅能使ＦｅＴｉ非晶化，同时促使表面产生明显的Ｆｅ原子偏聚。表面Ｆｅ／Ｔｉ原子比接近７。非晶ＦｅＴｉ（Ｈ）相的氧化分两个阶段进行，Ｔｉ原子首先氧化（８３３Ｋ），随后Ｆｅ原子氧化（８９０Ｋ）。     

划分IC缺陷团的聚类算法

大量实验表明了在集成电路（IC）制造过程中形成中的缺陷团之间具有很强的相关性，然而现用于划分缺陷团的方法均忽略了缺陷之间的相关性。因此，得到了缺陷分布规律不能有效地反映缺陷在圆片上的分布，为了提高IC成品率和可靠性仿真和设计的精度，提高缺陷分布模型的准确性，该文提出了划分缺陷团的聚类算法，该方法依据缺陷形成的动力学基础，充分考虑了缺陷团之间的相关性，实验证明该算法用于划分IC的缺陷团是十分有效的。     

Machine assignment and part-families formation using group technology

The conventional way of solving the group technology (GT) problem is to start from an assignment of parts to machines and try to find a partitioning of machine cells and part families. The similarity between parts is measured based on commonality of the machines assigned to them. However, parts are assigned to machines based on their operation requirements and the operation capabilities of machines. Similarity between parts should be based on their required operations. In this paper, the authors attempt to solve or facilitate solving the GT problem at the assignment level. An algorithm for assigning parts to machines is provided which utilizes the types of operations required by parts and applies GT principles in producing the assignment. This leads to better partitioning of machine cells and part-families. Furthermore, operation sequences required by parts in determining the similarity between parts have been considered. An algorithm to form part-families based on the operation sequence similarity coefficient has been developed. The resulting families are then used by the assignment algorithm to produce machine assignments to part-families. The use of the algorithm is demonstrated by examples.     

Software for dynamic reconfigurable order picking systems

This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.