The sectors of the Root industry include casting, plastic works, welding, surface treatment, and heat treatment. While the industry is concerned with the processing technologies that are used in most of the manufacturing industries, the sophistication of the corresponding manufacturing information systems is very low. This paper describes a manufacturing information system for the building sector for which the smartphone devices that the workers use in their daily lives are employed, and where the cost of the adaption of the manufacturing system at their factories is minimized. The proposed system consists of the following three parts: UI composer, General Application, and Gateway.
This paper describes the efficient equipment maintenance that can offer the exact time for repair and change of component in root industry. A conventional method offered the fixed time for repair and change of component because the method is based on early guarantee specification of the component. However the operating condition of manufacturing field is often under worse condition than early guarantee condition for high productivity. So, most components can’t use until early guarantee time due to the operation of various different condition. Therefore we suggest the useful method for efficient equipment-maintenance by manufacturing-field analysis and feedback database. For this, the classification of root industry and related equipment is performed and then the detail classification of the process and component for equipment maintenance. And the monitoring module is also designed to gather data for feedback process and the environment is basically implemented for aging and reliability test.
This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed - forward back propagation and the Levenberg - Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.
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