• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.386-392
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• 2009

This paper reports on the research which investigates acoustic signals acquired in progressive compressing, hole blanking, shearing and burr compacting process. The work piece is the head pin of the electric connector, whose raw material is the preformed steel bar. An acoustic sensor was set on the bed of hydraulic press. Because the acquired signals include the dynamic characteristics generated for all the processes, it is required to investigate signal characteristics corresponding to unit process. The corresponding dynamic characteristics to the respective process were first studied by analyzing the signals respectively acquired from compressing, blanking and compacting process. The combined signals were then periodically analyzed from the grinding to the grinding in the sound frequency domain and in the ultrasonic wave. The frequency of around 9 kHz in the sound frequency domain was much correlated to the tool wear. The characteristic frequency in the acoustic emission domain between 100 kHz and 500 kHz was not only clearly observed right after tool grinding but its amplitude was also related to the wear. The frequency amplitudes of 160 kHz and 320 kHz were big enough to be classified by the noise. The noise amplitudes are getting bigger, and their energy was much bigger as coming to the next regrinding. The signal analysis was based on the real time data and its frequency spectrum by Fourier Transform. As a result, the acousto-ultrasonic signals were much related to the tool wear progression.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.194-202
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• 2020

In this paper, experimental and numerical study on a combined sheet metal forming and plate forging of a seal part of a passenger car's hub bearing is conducted to develop the new process of which target is to remove machining process by plate forging and to achieve near-net shape manufacturing. The previous process of a sheet metal forming inevitably needed a machining process for making stepped sheet after conventional sheet metal forming in a progressive way. The stepped sheet is intended to be formed by plate forging in this study. Through the systematic way of developing the combined forming process using solid elements based-elastoplastic finite element method (FEM), several conceptual designs are made and an optimized process design in terms of geometric dimensioning and tolerance of straightness of the thin part is found, which is exposed to bending in metal forming of axisymmetric part. The predicted straightness measured by the slope angle of the tilted thin region is compared with the experimental straightness, showing that they are in a good agreement with each other. Through this study, a systematic approach to optimal process design, based on elastoplastic FEM with solid elements, is established, which will contribute to innovating the conventional small-scaled sheet metal forming processes which can be dealt with by solid elements.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.4
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• pp.309-314
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• 2004

0.5mm thick steel is used to manufacture nail files. The first process is blanking and the second process is making about 300 holes of 0.8-l.0mm in diameter. This process depends mainly on etching which takes 33% of manufacturing cost and it can make manufacturing cost rise. The residual etching reagent is not environmentally friendly and the steel material is apt to rust as well. To solve these problems, researches on the following subjects are performed: proper material to prevent from rusting and strip layout strategies in stamping to replace etching process with press process which makes use of die. And new quill type punch is developed to replace the regular standard punch, one of the die parts, which frequently get broken while working. And these researches and developments lead to develop a progressive perforating die.

• Smart Structures and Systems
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• v.2 no.2
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• pp.115-126
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• 2006

The Terra-Scope system is an affordable 4-D down-hole seismic monitoring system based on independent, microprocessor-controlled sensor Pods. The Pods are nominally 50 mm in diameter, and about 120 mm long. They are expected to cost approximately $6000 each. An internal 16-bit, extremely low power MCU controls all aspects of instrumentation, eight programmable gain amplifiers, and local signal storage. Each Pod measures 3-D acceleration, tilt, azimuth, temperature, and other parametric variables such as pore water pressure and pH. Each Pod communicates over a standard digital bus (RS-485) through a completely web-based GUI interface, and has a power consumption of less than 400 mW. Three-dimensional acceleration is measured by pure digital force-balance MEMS-based accelerometers. These accelerometers have a dynamic range of more than 115 dB and a frequency response from DC to 1000 Hz with a noise floor of less than $30ng_{rms}/{\surd}Hz$. Accelerations above 0.2 g are measured by a second set of MEMS-based accelerometers, giving a full 160 dB dynamic range. This paper describes the system design and the cooperative shared-time scheduler implemented for this project. Restraints accounted for include multiple data streams, integration of multiple free agents, interaction with the asynchronous world, and hardened time stamping of accelerometer data. The prototype of the device is currently undergoing evaluation. The first array will be installed in the spring of 2006.

• Design & Manufacturing
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• v.16 no.4
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• pp.1-6
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• 2022

Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.