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.

The operating environments of factories and manufacturing units have changed dramatically due to globalization, population, and customization. The existing factories are converted into smart units using information and communications technology (ICT). These smart factories can produce, control, repair, and manage themselves. The manufacturing processes are efficiently optimized using the monitoring and analysis methods of ICT. In this experimental study, we carried out a survey on the system solution providers and consumer companies to determine the field conditions and requirements necessary for assembling a smart factory. Using the results of this survey, we effectively devised smart factory solutions and implemented them on the existing conditions in various factories.

We used a construction method using a CNC milling machine, where free-formed molds were made by cutting EPS (Expanded PolyStyrene) foam with the CNC machine, to build free-formed buildings. CNC milling is off-the-shelf technology that can easily cut EPS foam; however its production cost is too high and the time to manufacture an EPS mold is too long. This paper proposes a novel cutting machine with a fast and cost effective mechanism to manufacture EPS concrete molds. Our machine comprises a cutter and Cartesian coordinate type moving mechanism, where the cutter cuts EPS foam using a hotwire in the shape of '$\sqcap$' and is capable of adjusting its cutting angle in real-time while keeping its cutting width. We proved through cutting experiments on the CNC machine that cutting time was greatly shortened compared to the conventional method and that the resulting concrete mold satisfied manufacturing precision.

Magnetically levitated (Maglev) vehicles maintain a constant air gap between guideway and car bogie, and thereby achieves non-contact riding. Since the straightness and the flatness of the guideway directly affect the stability of levitation as well as the ride comfort, it is necessary to monitor the status of the guideway and to alert the train operators to any abnormal conditions. In order to develop a signal processing algorithm that extracts guideway irregularities from sensor data, virtual testing using a simulation model would be convenient for analyzing the exact effects of any input as long as the model describes the actual system accurately. Simulation model can also be used as an estimation model. In this paper, we develop a state-space dynamic model of a maglev vehicle system, running on the guideway that contains jumps. This model contains not only the dynamics of the vehicle, but also the descriptions of the power amplifier, the anti-aliasing filter and the sampling delay. A test rig is built for the validation of the model. The test rig consists of a small-scale maglev vehicle, tracks with artificial jumps, and various sensors measuring displacements, accelerations, and coil currents. The experimental data matches well with those from the simulation model, indicating the validity of the model.

Generally, a working excavator has only one directional bucket tilting angle, which is up-forward. However, side direction rotation of the bucket would allow variety of working output. We designed a hydraulic rotary actuator comprising a double rod hydraulic cylinder with a rack-pinion gear set for use in excavator bucket with side tilting mechanism, thus converting the linear to angular motion. The proposed side tilting rotary actuator was designed with parts suitable for medium size of heavy duty excavator. These mechanical parts were inexpensive to purchase and the manufacturing cost was reasonable. The proposed mechanism is potentially useful for excavator with variety of working output.

A fine stage is developed for the 3-DOF error compensation of a linear axis in order to improve the positioning accuracy. This stage is designed as a planar parallel mechanism, and the joints are based on a flexure hinge to achieve ultra-precise positioning. Also, the effect of Abbe's offsets between the measuring and driving coordinate systems is minimized to ensure an exact error compensation. The mode shapes of the designed stage are analyzed to verify the desired 3-DOF motions, and the workspace and displacement of a piezoelectric actuator (PZT) for compensation are analyzed using forward and inverse kinematics. The 3-DOF error of a linear axis is measured and compensated by using the developed fine stage. A marked improvement is observed compared to the results obtained without error compensation. The peak-to-valley (PV) values of the positional and rotational errors are reduced by 92.6% and 91.3%, respectively.

We present a multi-sample array device based on a pneumatic system. Solenoid valves were used to control a micro valve in a pneumatic system. The use of a compressor together with a vacuum pump ensured that one outlet could supply both compression and vacuum pressure. The multi-sample array device was fabricated using conventional photolithography and PDMS casting. The device was composed of a multiplexer, sample array, and rinsing. The multiplexer could control four sample solutions injecting into the sample array chamber. Sample solution not arrayed was removed by DI-water from the rinsing inlet. To prevent trapping of microbubbles in the channel during injection of sample solution into the device, surfactant was added in PDMS solution to serve as a hydrophilic surface treatment. As a result, the device could be used as a sample array for 64 cases, using four samples and three columns of three chambers.

The goal of this paper is to investigate the effects of out-of-plane deposition angle on product characteristics of a UV photo-curing process. Specimens are manufactured from a commercialized UV photo-curing machine, the NOBEL V1.0. The influence of the out-of-plane deposition angle of the specimen on surface characteristics, including morphology of the sloped surface, pick-to-pick distance of convex region, and roughness of the sloped surface, is examined via the observation of the sloped surface. In addition, the influence of the radius of curvature of the specimen on the surface roughness of the sloped surface is evaluated. The effects of the out-of-plane deposition angle on impact strength of specimens are investigated via Izod impact experiments. Finally, we discuss the influence of the out-of-plane deposition angle on failure characteristics of specimens for impact loads.