Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing

This study addresses classification methodology for the automatic inspection of a range of defects on the surface of glass substrates in thin film transistor liquid crystal display glass substrate manufacturing. The proposed methodology consisted of four stages: (1) feature extraction by calculating the wavelet co-occurrence signature from the substrate images, (2) handling of imbalanced dataset using the Synthetic Minority Over-sampling TEchnique (SMOTE), (3) reduction of the feature's dimension by principal component analysis, and (4) finally choosing the best classifier between three different methods: Classification And Regression Tree (CART), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). In training the SVM and MLP classifiers, the simulated annealing algorithm was used to obtain the optimal tuning parameters for the classifiers. From the industrial case study, the proposed feature extraction algorithm could remove the defect-irrelevant image features and SMOTE increased the accuracy of all three methods. Furthermore, the optimized SVM and MLP models were more accurate than the CART model whereas a higher accuracy of 89.5% was observed for the proposed SVM model.     

Customizable hardware design of fuzzy controllers applied to autonomous car driving

Nowadays, final products often encompass a certain intelligence therein to deal with variation or lack of precision in the sensing input data. This intelligence is usually acquired via the utilization of existing soft techniques, such as artificial neural networks, genetic algorithms and fuzzy control, among others. Thus, it is profitable to have on-the-shelf shell scalable and adaptive hardware designs that implement these soft techniques. This availability allows for an immediate embedding of any of those designs onto final products. This usually entails a reduced time-to-market of the product. Process control is one of the many applications that took advantage of the fuzzy paradigm. In general, controllers are embedded into the controlled device. This paper presents a novel design of a reconfigurable efficient parallel architecture to implement fuzzy controllers on hardware with almost no design effort for final users. The proposed architecture is herein proven suitable for embedding. It is customizable, so it allows the setup and configuration of the controller parameters, and hence its use for any problem application. Two fuzzy controllers that model autonomous car driving are implemented and their cost and performance evaluated.     

灰色—支持向量机的空调负荷预测

对空调负荷进行准确预测不仅对空调优化控制的意义重大,而且也是实现空调经济运行与节能的关键所在。为了提高建筑空调负荷的预测精度,在分析灰色模型和支持向量机建模特点基础上提出了一种空调负荷组合预测算法。该方法综合了灰色建模计算过程简单以及支持向量机自学习和泛化能力强的优点,能够更加有效地利用样本数据的有效信息,提高模型预测精度。首先,通过灰色建模过程弱化了样本数据的随机因素。然后,对灰色模型输出进行归一化处理及数据重构,以作为支持向量机的输入。最后,通过支持向量机模型的预测得到最终预测结果。将本文所提出的方法应用于福州一栋办公建筑的逐时空调负荷预测中,并与灰色模型及支持向量机模型作比较,证明了组合模型的预测值与实际运行值拟合度最高,平均绝对误差比灰色模型和支持向量机模型分别降低了47.84%和17.39%。该组合预测模型具有较高的预测精度和更好的泛化能力,具有较强的可行性和实用性。     

冲压式舵机动态特性测试系统的研制

为了在实验室条件下模拟冲压式舵机的舵面在空中所受的气动载荷,实现舵机系统动态性能指标的综合检测,设计了基于PXI总线的舵机动态测试系统,工作过程中对舵偏角信号、工作电流信号、扭矩信号进行实时采集、显示,并配合测试流程完成对舵机动态性能的测试。设计中重点考虑了定刚度加载系统的小扭矩加载精度、小转动惯量以及同轴度问题。实验结果表明,该测试系统测试精度高、使用维护方便,满足要求。     

基于悬臂梁称重传感器的圆弧刀研磨力测量仪设计

为解决双圆锥形圆弧刀研磨力在线测量难题,设计了杠杆原理为基础的圆弧刀研磨力测量仪,建立了圆弧刀与磨盘之间研磨力数学模型。该仪器采用悬臂梁称重传感器,并以DSP2812为主控芯片,实现了圆弧刀研磨力的在线采集、处理、存储及实时显示等功能。实验表明:仪器的称重传感精度优于0.15 N,圆弧刀研磨力变化范围降低28.8%,满足圆弧刀研制的需求。该仪器性能稳定,具有较好的实用价值,为高质量的圆弧刀研制和光栅刻划技术提供了技术保障。     

平行梁式电容传感器力学性能的仿真与分析

针对S型平行梁式电容称重传感器参数结构设计问题,运用有限元软件ANSYS Workbench对平行梁式弹性体结构进行参数化模拟仿真,基于二次多项式曲面拟合建立其结构力学性能与主要结构参数的函数关系,并通过算例分析验证了拟合公式的有效性。研究表明,拟合公式对复杂仿真计算后的结果有很好的近似,能更好的反应实际情况,公式结构更加简洁,具有较快的收敛速度、较高的精度和较好的推广能力,有利于准确实现平行梁式电容称重传感器结构参数设计。     

一次多级离心泵运行故障处理及分析

简要介绍了实际生产中多级离心泵存在的问题,并分析了故障原因和处理措施。     

一种基于云模式的试题库系统设计与实现

当前互联网提倡分享、社交的模式,“云模式”的概念逐渐兴起。对试题的评价、分享是试题库发展的重要方向之一。设计一种基于云模式的试题库系统,着重加强试题库系统的互联网属性,将用户参与融入系统之中。同传统的试题库系统最大的不同是融合“云”的元素,各用户添加的试题在用户允许的情况下将被分享到“试题云”,用户在组卷时可以选用试题云中的试题,丰富用户在组卷时的选择,提高试卷的质量。     

自控系统在加氢反应工艺中的应用

通过对化工生产中加氢反应的危险性简要介绍,并以某药厂对加氢反应工艺自控系统改造时从自控方案选择到自控参量、主要自控设施确定及安装、校验和系统调试等主要过程实施为例,说明此种危险可行性,为药厂类加氢工艺自控系统的技术改造提供相应的参考。