高品质钢铁产品的批量生产需要时间测试的制造实践和现代技术。下载188bet金宝搏由于钢圈通过轧制，镀锌，颜色涂层和钢制制造设备中的其他线路，操作员必须根据每个线圈的特性和所需的厚度和平坦度轮廓设置烤箱温度，线路速度和对准。未正确设置一条线会导致超额废料。它还可以损坏烤箱并导致线关闭几天。
At Ruukki Metals, we built and deployed a web application with MATLAB®that enables operators to select and apply the proper settings throughout the steel manufacturing process. We built a second, standalone MATLAB application that our engineers use to aggregate and analyze production metrics from multiple databases, track individual coils, and refine our process.
自从部署这些应用程序以来，我们具有更加一致和高效的过程，较少的废料，改进的平坦度和较短的偏移量。例如，在炼金厂中，OFF-CAUGE长度 - 不达到目标厚度的线圈的量 - 已经从数米到50厘米或更小减少。这些改进的关键是定期优化设置计算参数，以及MATLAB中的数据可视化。用于检测偏差线圈的这些MATLAB可视化的力量不能夸大。
Identifying Potential Process Improvements
必须调整到k线的速度和温度eep the thickness and flatness of each coil within required tolerances. Before MATLAB based web applications were available, our operators relied on their own experience, personal notes, and judgment for this work. With multiple shifts running each day, this practice led to inconsistent results.
一旦处理了线圈，工程师难以耗时，以确定用于任何特定线圈或一组线圈的设置。例如，为了检查冷轧机上的厚度，镀锌后的输出，以及横向厚度曲线，它们通常花费时间收集必要的数据，加工，并产生理解结果所需的块。
Analyzing Big Data with MATLAB and Neural Networks
At the heart of our new process is a set of data warehouses that we use to store and access information about the coils as they pass through the plant. A Microsoft®基于SQL的数据仓库存储厚度公差，尺寸，每个线圈的原材料等级，以及线圈的预期客户。一个奇迹®Historian data warehouse stores time-series data for the coil’s thickness and flatness and other process measurements. An Oracle®基于数据仓库存储检测到的缺陷或异常，并在线圈中测试结果。所有在一起，可以为每年加工的60,000个线圈存储多达4000个不同的测量。
With MATLAB and Database Toolbox™, we developed an application that retrieves data from each database, merges it in a separate Microsoft Access database, and creates documentation as needed. When a new coil is about to be processed in the line, this application analyzes the merged and stored data to calculate oven temperatures and other parameters used to set up equipment. In the galvanneal process, for example, the application uses a neural network created with Neural Network Toolbox™ to calculate setup values.
We relied on Neural Network Toolbox to implement a number of other key application features. We used self-organizing maps to classify coils by zinc mass, iron percentage, and flatness (Figure 1).
图1.使用神经网络工具箱创建的自组织地图。左上角的群集83显示有469个线圈，平坦度具有类似的平台特性。
We also used Neural Network Toolbox to create a neural model. The model generates roll gap predictions based on finite element methods (FEM) (Figure 2).
In total, we created more than 30 different MATLAB applications for analyzing, visualizing, and exporting data for coils in the production chain. This data includes thickness, flatness, transverse profiles, raw material quality, and other characteristics (Figure 3).
部署应用程序
We used MATLAB Compiler™ to create a desktop application that provides easy access to the data analysis and visualization applications in MATLAB that we had developed. Thanks to our use of JDBC drivers, no ODBC database connections were needed, and we did not have to use database wizards to create a database connection for the applications. Engineers can install and run this application from any PC without having to install MATLAB. Currently, more than 20 engineers are using it to gain insights into how our production chain is working today and how it might be improved in the future. Using this application, analyses that took days to complete manually can be completed in less than a minute.
我们还创建了一个基于Web的应用程序，可通过我们网络上的任何Web浏览器访问，该行运营商可以用于查看所需的数据来监视并设置该行（图4）。要构建此.NET应用程序，请使用Matlab Builder™NE将MATLAB代码作为DLL打包为DLL。DLL从我们的数据库中检索信息，并创建将作为比特流发送给主应用程序的绘图，托管在Microsoft Internet信息服务（IIS）Web服务器上。
使用此应用程序，我们的运营商准备在每个新线圈进入一行之前进行必要的调整。自从部署此应用程序和我们使用Matlab和Matlab编译器创建的独立可执行文件以来，我们已经看到了更少的错位，减少废料，并且在工厂的效率和一致性增加。
最近，我们使用MATLAB在脾气和TANDEM磨机中的冷轧过程中模拟弯曲过程，我们执行了使我们能够改善出于此过程的线圈的平坦度的模拟。MATLAB提供了一种强大的工具，用于访问像COMSOL Multiphysics这样的FEM模型®. We have created several applications that use a COMSOL-MATLAB link that enables us to input values to an FEM model and visualize results from COMSOL.
