• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.12 no.6
• /
• pp.142-151
• /
• 2013

This study develops an automation system for metallic mold design that is applicable in forging non-axial symmetric parts. The metallic mold design program is used to design the metallic mold using two-dimensional axial symmetric metallic molds and to predict the stress concentration using finite element analyses. Then, the program redesigns the metallic mold using variables such as the optimal split diameter, maximum allowable inner pressure, fit tolerance, and stress distribution, which are calculated using the metallic mold design program. When the involute spur gear is forged, stress concentration occurs on the tooth root bounded at the symmetric surface. The SCM4 material is suitable for metallic molds because the stress is less than the yield strength of the insert and it acts on the tooth root regardless of the inner pressure. The metallic mold for forging non-axial symmetric parts can be designed through adjusting the magnitude of the contact pressure. The program developed in this study can be applied to metallic mold designs in involute spur gears of forging, which is an ordinary non-axial symmetric part.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.4
• /
• pp.77-87
• /
• 1994

This paper deals with an automated forming sequence design system by which designers can determine desirable operation sequences even if they have little experience in the design of cold forging process. The forming sequence design in the cold forging is very important and requires many kinds of technical and empirical knowledge. They system isproposed, which generates forming sequence plans for the multistage cold forging of axisymmtrical solid products. Since the process of metal forming can be considered as a transformation of geometry, treatment of the geometry of the product is a key in planning process. To recognize the geometry of the product section, section entity representation and primitive geometries were used. Section entity representation can be used for the calculation of maximum diameter, maximum height, and volume. Forming sequence for the part can be determined by means of primitive geometries such as cylinder, cone, convex, and concave. By utilizing this geometrical characteristics (diameter, height, and radius), the product geometry is expressed by a list of the priitive geometries. Accordingly the forming sequence design is formulated as the search problem which starts with a billet geometry and finishes with a given product one. Using the developed system, the sequence drawing with all dimensions, which includes the proper sequence of operations for the part, is generated under the environment of AutoCAD. Based on the results of forming sequence, process variables(strain, punch pressure, die inner pressure, and forming load) are determined.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2003.08a
• /
• pp.35-40
• /
• 2003

In order to develope springback control technology for high-strength steel sheets, some studies have been conducted: dome stretching test, stepped s-rail forming and springback measurement, and optimally shaped initial blank design. First, to find out the formability of TRIP60, dome stretching test was performed. Next the stepped s-rail die, which was designed to form a channel type panel with large twist and wall curl, was manufactured and used to know the effect of controlling forming variables, such as blank holding force and flange amount on the springback. Furthermore, new measurement method of the springback was introduced to define wall curl and twist in geometrically complex panels. Finally, the optimally shaped initial blank was employed to verify one of the best ways to control the springback in channel type, high strength sheet panels.

• Design & Manufacturing
• /
• v.17 no.1
• /
• pp.33-39
• /
• 2023

The ball end mill is a type of cutting tool that is widely used to process complex mold shapes including aspheric surfaces. Unlike the flat end mill in which the cutting edge is formed on the cylindrical handle, the cutting edge is formed from the cylindrical handle to the hemispherical shape, which is advantageous for processing curved shapes. However, since the cutting speed continuously changes during machining due to the helix angle of the cutting edge or the machining inclination angle, it is difficult to obtain a precise machined surface. Therefore, in this paper, machining was performed while changing the helix angle of the ball end mill and the angle of the machining slope under the same cutting conditions for STD 11 material, which is widely used as a mold material. Through this, the effect of the two variables on the roughness of the machined surface was analyzed. As a result, if the helix angle was 0 degrees, it showed the best surface roughness of Ra. 0.16 ㎛. When the helix angle was 20 degrees, the best surface roughness of Ra. 0.18 ㎛ was occurred.

• Design & Manufacturing
• /
• v.17 no.4
• /
• pp.1-7
• /
• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.3
• /
• pp.320-326
• /
• 2017

Multistage deep-drawing technology is used widely in the production of mobile phone battery cases to improve productivity and economy. To ensure adequate capacity and rigidity, such cases are fabricated as a rectangular cup with a high slender ratio. The multistage deep-drawing of a rectangular cup entails a high slender ratio, and the heights of the product sides may be non-uniform because of the complicated deformation mechanisms. This causes problems in product assembly that affects the surface quality of the case. This study examined a blank shape that minimizes the height variations of the product to resolve the aforementioned problems. Optimization design and analysis were performed to identify the shape that yields the least variation. The long and short sides of an oval blank were set as the design variables. The objective function was set to yield the lowest height difference, and the thickness reduction rate of the product was set to the target range. In addition, the height of the final shape was set as a constraint. The height difference was minimized successfully using the optimized design. The design process of the initial blank for all rectangular shapes can be automated in the future.

• Korean Journal of Food Science and Technology
• /
• v.23 no.1
• /
• pp.88-92
• /
• 1991

To analyze the effects of the system parameters on the target parameters, which include the amount of water evaporation, water solubility index(WSI) and water absorption index(WAI), test trials of fractional factorial design of the three process variables at three levels were carried out for corn grit with a laboratory twin-screw extruder with three different screw configurations. The system parameters collected from the trials, such as extrusion temperature, specific mechanical energy input(SME) and mean residence time(RT), were showed the ranges of $129{\sim}182^{\circ}C$, $67{\sim}163\;kwh/ton$ and $12{\sim}34\;sec$, respectively. Within these ranges of the system parameters, the target parameters were able to be quantified by using multiple regression equations. The correlation of results with the system parameters blocked by the screw configuration as dependent variables, yield correlation coefficients above 0.90, and the correlation using the system parameters obtained from whole experiment system as the dependent variables yield correlation coefficients around 0.80. The functional relationship, which can be quantified by second order polynomial regression equation with only two system parameters within necessary degree of accuracy, can he graped in three dimensional surface response and contour diagrams.

• Journal of the Society of Naval Architects of Korea
• /
• v.48 no.6
• /
• pp.528-538
• /
• 2011

As a forming method for curved hull plates more efficient than the flame bending, mechanical bending using multi point press forming and die-less forming is discussed in this paper. the mechanical forming is a flexible manufacturing system for automatically forming of hull parts. It is especially suited to varied curved parts. This paper discusses a multiple point pressing machine composed of a pair of reconfigurable punches in order to achieve the rapid forming of curved hull plates using division forming and presents how forming information is obtained from the given design surface. Although the mechanical forming can be efficient in the metal forming, spring back after pressing is a phenomenon which must be carefully considered when quantifying the process variables. If the spring back is not accurately controlled, the fabricated shell plate cannot meet assembly tolerance. This paper describes the principles to calculate the proper stroke of each punch at the divided areas. the strokes are determined by an iterative process of sequential pressing and spring back compensation from an unfolded flat shape to its given design surface. FEA(finite element analysis) is used to simulate the spring back of the plate and the IDA(iterative displacement adjustment) method adjusts the offset of pressing punches from the deformation results and the design surface. The shape deviations of two surfaces due to spring back are compensated by integrated system using FEA and IDA method. For the practical application, It is aimed to develop an integrated system that can automatically perform the compensation process and calculate strokes of punches of the double sides' reconfigurable multiple-press machine and some experimental results obtained with mechanical bending are presented.

• Design & Manufacturing
• /
• v.18 no.1
• /
• pp.1-10
• /
• 2024

Surface roughness is one of the quality factors of molds that significantly influences the quality and performance of the final product, so it should be carefully considered during mold processing. To achieve the targeted surface roughness in mold machining, it typically relies on the utilization of cutting models for predicting cutting forces and experimental studies to optimize machining conditions. Because it is difficult to intuitively deduce the correlation between cutting variables and actual surface roughness, experiments are necessary in various machining conditions to adapt to changing machining environments. Furthermore, in micro-machining environments like in this study, various factors such as the difficulty of detecting micro-cutting signals, the lack of established standard models for predicting micro-cutting forces, and increased machining costs make it challenging to secure surface roughness through interpretation models and experiments. Moreover, although the trend of utilizing artificial intelligence in industries is increasing, there exist limitations in applying the technology due to the extensive time, manpower, and costs involved in collecting high-quality data for constructing artificial neural networks. In this study, to overcome these limitations and supplement experimental data necessary for AI learning, a neural network conversion model was proposed to convert surface roughness prediction equations into experimental data. Then, by using the converted formula data as similar-experimental data along with actual experimental data, an artificial neural network model for predicting surface roughness was constructed. The predicted surface roughness data obtained through the proposed method was compared and analyzed against actual surface roughness data. As a result, the prediction model incorporating similar-experimental data achieved an accuracy improvement of over 36% compared to models using only experimental data. The surface roughness prediction model with similar-experimental data is expected to replace labor-intensive and costly activities of collecting experimental data in various machining environments.