Angsumalin Senjuntichai

Spiral Defect Reduction of Hard Disk Drive Media

The objective of this study is to reduce a number of defects in Hard Disk Drive (HDD) manufacturing due to spiral scratch on media by applying DMAIC steps of Six Sigma approach. The spiral scratch is firstly identified as the significant loss with 6.03% defective rate. Secondly, the paddle to disk space, top cover edge sharpness, pitch static attribute and number of load/unload cycle are found to be the key process input variables (KPIV). The experiment based on four KPIVs is then designed following Box Behnken design. With the results from the experiment, the response surface method is applied to determine the optimal setting for these four KPIVs with respect to the minimum percentage of the spiral scratch. Finally, the process with the optimal settings of the paddle to disk space at 3 mm, top cover edge sharpness at 0.002 inch, pitch static attitude at 0.01 inch and number of load/unload cycle at 10,000 times is implemented and monitored by the p control chart. After the improvement, the defective rate of the spiral scratch is decreased by 48.8% from 6.03% to 3.09%.

Comparison of in-Process Cutting State Detection in CNC Turning Using Different Neural Network Systems

The aim of this research is to propose and compare the in-process detection systems of the cutting states of the continuous chip, the broken chip and the chatter for the carbon steel in CNC turning process by utilizing the sensor fusion, which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The new six parameters proposed for the inputs of the neural network systems, which are the enegy spectral densities of three dynamic cutting forces, sound signal, accelation signal, and the standard deviation of acoustic emission signal. All signals of parameters have been integrated via the different neural network systems by using the pattern recognition and the percertron technique to detect the cutting states, which are. Among the cutting states of chip formation and chatter, the broken chip is required for the reliable and stable cutting system. The experimentally obtained results showed that the in-process detection system using the neural network with the pattern recognition technique can be effectively used to detect the cutting states with the higher accuracy and reliability more than the one with the perceptron technique.

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

Development of Efficient Washing System for Reduction of Oil Contamination on Machining Parts

The objective of this study is to develop an efficient washing system to remove cutting oil from machining part surface. The proposed washing system consists of two processes: the dipping process and the modified automatic ultrasonic washing process. The automatic ultrasonic washing process is redesigned and developed to reduce operating cost and increase productivity from the previously developed machine. For this proposed system, experiments have been performed to determine the washing conditions that yield satisfactory proportion of defectives due to oil contamination. Under the suggested operating conditions, the proportion of defectives due to oil contamination is reduced from 12.8% to 1.78%, which leads to

Intelligent Prediction of Tool Wear in Ball-End Milling Process Based on Dimensionless Cutting Force Ratio

16,800 defective cost reduction. The proposed washing system yields 42.9% increase in washing productivity. Furthermore, it as has more standard procedure than the current washing process.

Bit Error Rate Improvement of Hard Disk Drive

This research proposed an in-process tool wear prediction during the ball-end milling process by utilizing the cutting force ratio. The dimensionless cutting force ratio is proposed to cut off the effects of the work material and the combination of cutting conditions. The in-process tool wear prediction model is developed by employing the exponential function, which consists of the spindle speed, the feed rate, the depth of cut, the tool diameter, and the cutting force ratio. The experimentally obtained results showed that the cutting force ratio can be utilized to predict the tool wear of ball-end milling tool. The new cutting tests have been employed to verify the model and the results run satisfaction. It has been proved that the in-process tool wear prediction model can be used to predict the tool wear regardless of the cutting conditions with the highly acceptable prediction accuracy.

Process Setting through General Linear Model and Response Surface Method

The objective of this research is to improve the performance of the read-write head process in Hard disk drive manufacturing with respect to Bit Error Rate (BER). With the preliminary survey, the process capability index (Cpk) of BER was 0.72 which is less than the one side acceptable value at 1.25. To improve Cpk of BER, five phases of Six sigma approach are applied starting from define, measure, analyze, improvement and control phases. At 95% confidence, thermal protrusion, writing current amplitude, writing current overshoot, number of defects on media and writing head width are the significant factors for Bit Error Rate due to their p-value less than 0.05. Since the number of defects and writing head width are uncontrollable factors, the experiment are designed and performed based of general factorial design with three levels of each controllable factor. At 5% significance level, there are the interaction effects between the thermal protrusion and the writing current amplitude as well as the interaction affects between the writing current amplitude and the writing current overshoot. With the general linear model (GLM), the suggested values for the thermal protrusion, writing current amplitude and writing current overshoot are 35 DAC, 10 mA and 9 mA, respectively. Under the suggested condition, Cpk of BER is increased from 0.72 to 2.38 and the percentage of defective due to head related failure is reduced from 21.85% to 9.86%.

In-Process Chatter Detection in Surface Grinding

The objective of this study is to improve the efficiency of the flow‐wrap packaging process in soap industry through the reduction of defectives. At the 95% confidence level, with the regression analysis, the sealing temperature, temperatures of upper and lower crimper are found to be the significant factors for the flow‐wrap process with respect to the number/percentage of defectives. Twenty seven experiments have been designed and performed according to three levels of each controllable factor. With the general linear model (GLM), the suggested values for the sealing temperature, temperatures of upper and lower crimpers are 185, 85 and 85° C, respectively while the response surface method (RSM) provides the optimal process conditions at 186, 89 and 88° C. Due to different assumptions between percentage of defective and all three temperature parameters, the suggested conditions from the two methods are then slightly different. Fortunately, the estimated percentage of defectives at 5.51% under GLM process...