In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

In order to reduce the manufacturing costs of the glass lens, it is necessary to manufacture a lens using a UV curable resin or a thermosetting resin, which is a curable material, in order to replace a glass lens. In the case of forming a lens using a thermosetting material, it is necessary to form several lenses at once using the wafer-level lens manufacturing technologies due to the long curing time of the material. When a lens is manufactured using a curable material, an error in the shape of the lens due to the shrinkage of the material during the curing process is an important cause of defects. The major factors for these shape errors and deformations are the shrinkage and the change of mechanical properties in the process of changing from a liquid material during curing to a solid state after complete curing. Therefore, it is necessary to understand the curing process of the material and to examine the shrinkage rate and change of physical properties according to the degree cure. In addition, it is necessary to proceed with CAE for lens molding using these and to review problems in lens manufacturing in advance. In this study, the viscoelastic properties of the material were measured during the curing process using a rheometer. Using the results, Rheological investigation of cure kinetics was performed. At the same time, The shrinkage of the material was measured and simple mathematical models were created. And using the results, the molding process of a single lens was analyzed using Comsol, a commercial S/W. In addition, the experiment was conducted to compare and verify the CAE results. As a result, it was confirmed that the shrinkage rate of the material had a great influence on the shape precision of the final product.

In order to reduce the weight of parts and materials for the development of high-efficiency engines in accordance with the strengthening of automobile fuel efficiency regulations, the existing casting material is changed to a iron plate material, and plastic processing and turning operations are performed to lighten the weight and reduce the manufacturing cost. Among the pulley components applied to the damper pulley, the HUB product was manufactured by plastic machining instead of the existing casting process, and the inspection standardized for automating the inner diameter and parallelism measurement of the turning result of the new hub part with improved quality, and the inspection system for this Development of design and operation software to automate the inspection of the inner diameter and parallelism of the hub was described. The representative specifications of the development equipment are a hub inner diameter 22mm inspection system, a three-point inspection system with a parallelism of 0.15mm on the top.

The demand for new processing technology that can improve productivity is increasing in industries that require large-scale and various products. In response to this demand, a robot machining system with flexibility is required. Because of the low rigidity of the robot, the robot machining system has a large error during machining and is vulnerable to vibration generated during machining. Vibration generated during machining deteriorates machining quality and reduces the durability of the machine. To solve this problem, a stage for fixing the spindle during machining is required. In order to compensate for the robot's low rigidity, a system combining a piezoelectric actuator for generating a large force and a guide mechanism to actuate with a desired direction is required. Since the rigidity of flexible hinges varies depending on the structure, it is important to optimal design the flexible hinge and high-rigidity system. The purpose of this research is to make analytic model and optimize a flexible hinge and to design a high rigidity stage. In this research, to design a flexible hinge stage, a concept design of system for high rigidity and flexure hinge modeling is carried out. Based on analytic modeling, the optimal design for the purpose of high rigidity is finished and the optimal design results is used to check the error between the modeling and actual simulation results.

The recent increasing application rate of advanced high-strength steel(AHSS) for automotive parts makes it difficult to ensure the durability of forming tools. Significant load and friction generated during the piercing process of AHSS increase the wear rate and the damage degree to dies. These harsh process conditions also yield product failures, such as dimensional inconsistency of pierced holes and insufficient quality of hole's sheared edge. This study analyzed the effect of punch wear on the sheared surface of pierced parts and the forming load during the piercing process. Wear-shaped punches showed approximately 20% higher piercing load than normal-shaped punches, and the rollover ratio of the sheared surface also increased. It is considered that the dull edge of wear-shaped punches does not penetrate directly into the material but shears after tensioning it in a piercing direction. In addition, wear-shaped punches experienced compressive load even after completing the piercing process during the down-stroke and tensile load during the up-stroke. This load variation is related to the smaller diameter piercing holes produced by wear-shaped punches compared to normal-shaped punches. Thus, we demonstrated the predictability of the wear level of dies through a comparative analysis of the piercing load pattern.

Regular expression pattern matching is widely used in applications such as computer virus vaccine, NIDS and DNA sequencing analysis. Hardware-based pattern matching is used when high-performance processing is required due to time constraints. ReCPU, SMPU, and REMP, which are processor-based regular expression matching processors, have been proposed to solve the problem of the hardware-based method that requires resynthesis whenever a pattern is updated. However, these processor-based regular expression matching processors inefficiently handle repetitive operations of regular expressions. In this paper, we propose a new instruction set to improve the inefficient repetitive operations of ReCPU and SMPU. We propose REMPi, a regular expression matching processor that enables efficient iterative operations based on the REMP instruction set. REMPi improves the inefficient method of processing a particularly short sub-pattern as a repeat operation OR, and enables processing with a single instruction. In addition, by using a down counter and a counter stack, nested iterative operations are also efficiently processed. REMPi was described with Verilog and synthesized on Intel Stratix IV FPGA.

In this study, we designed an vibration cutting tool that can achieve improvements such as low cutting force, interrupted chip evacuation and better surface quality of cutting performance to obtain high-quality surface roughness and improvement of tool wear, which is an issue in the machining of high-hardness mold steel. Among the resonance frequency modes of the vibration cutting tool, the bending mode was used to maximize the driving amplitude of the vibration tool tip, and the resonance frequency was confirmed through the finite element method. After measuring the actual resonant frequency of the designed tool using an optical fiber sensor, the cutting force and machining surface of vibration cutting and conventional cutting were compared and analyzed in the turning process of high hardness mold steel (STAVAX). As a result of the experiment, the cutting force was reduced by about 20 % compared to the conventional cutting process, and the surface roughness was also improved by about 60 %. This study suggested that the tool wear and surface quality of high-hardness steel can be improved through the vibration cutting method in the machining of high hardness mold steel.

In this study, the simulation of the resin transfer molding process method using MoldFlow has been investigated. This work explains the thermoset material model, fabric permeability model, the flow model and the cure model. It has been shown that the simulation result can predict filling and cure performances.

The qualities of the products produced by injection molding are strongly influenced by the process variables of the injection molding machine set by the engineer. It is very difficult to predict the qualities of the injection molded product considering the stochastic nature of the manufacturing process, since the processing conditions have a complex impact on the quality of the injection molded product. It is recognized that the artificial neural network(ANN) is capable of mapping the intricate relationship between the input and output variables very accurately, therefore, many studies are being conducted to predict the relationship between the results of the product and the process variables using ANN. However in the condition of a small number of data sets, the predicting performance and robustness of the ANN model could be reduced due to too many input variables. In the present study, the ANN model that predicts the length of the injection molded product for multiple combinations of process variables was developed. And the accuracy of each ANN model was compared for 8 process variables and 4 important process inputs that were determined by the feature selection. Based on the comparison, it was verified that the performance of the ANN model increased when only 4 important variables were applied.

Micro-fluidic chip has been fabricated by lithography process on silicon or glass wafer, casting using PDMS, injection molding of thermoplastics or 3D printing, etc. Among these processes, 3D printing can fabricate micro-fluidic chip directly from the design without master or template for fluidic channel fabricated previously. Due to this direct printing, 3D printing provides very fast and economical method for prototyping micro-fluidic chip comparing to conventional fabrication process such as lithography, PDMS casting or injection molding. Although 3D printing is now used more extensively due to this fast and cheap process done automatically by single printing machine, there are some issues on accuracy or surface characteristics, etc. The accuracy of the shape and size of the micro-channel is limited by the resolution of the printing and printing direction or layering direction in case of SLM type of 3D printing using UV curable resin. In this study, the printing direction and thickness of each printing layer are investigated to see the effect on the size, shape and surface of the micro-channel. A set of micro-channels with different size was designed and arrayed orthogonal. Micro-fluidic chips are 3D printed in different directions to the micro-channel, orthogonal, parallel, or skewed. The shape of the cross-section of the micro-channel and the surface of the micro-channel are photographed using optical microscopy. From a series of experiments, an optimal printing direction and process conditions are investigated for 3D printing of micro-fluidic chip.