- Home
- /
- อังศุมาลิน เสนจันทร์ฒิไชย
รศ. ดร.อังศุมาลิน เสนจันทร์ฒิไชย
- 6th Floor of Engineering 4 Bldg., Room 611
- +66-2218-6827
- angsumalin.s@chula.ac.th
Education
D.Eng Industrial Engineering
Asian Institute of Technology, Thailand, 2012
M.S.I.E. Industrial Engineering
University of Minnesota, United States, 1999
B.Eng. Industrial Engineering
Chulalongkorn University, Thailand, 1994
Chulalongkorn University, Thailand, 1994
Expertise
Economics & Financial Engineering
Statistics & Data Analysis
Publications
2014
Kulpiya Seri, Senjuntichai Angsumalin
Package chip defect reduction on integrated circuit Journal Article
In: Applied Mechanics and Materials, vol. 462-463, pp. 578 – 584, 2014, (Cited by: 2).
@article{Seri2014578,
title = {Package chip defect reduction on integrated circuit},
author = {Kulpiya Seri and Senjuntichai Angsumalin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891047743&doi=10.4028%2fwww.scientific.net%2fAMM.462-463.578&partnerID=40&md5=278e7d02001fc74656907ee61110b88a},
doi = {10.4028/www.scientific.net/AMM.462-463.578},
year = {2014},
date = {2014-01-01},
journal = {Applied Mechanics and Materials},
volume = {462-463},
pages = {578 – 584},
abstract = {This research applies Six Sigma approach in order to reduce defect by increasing the assembly process capability index (Cpk) of Integrated Circuit (IC) production process. This study applies five phases (DMAIC) of Six Sigma approach beginning with define (D), measure (M), analyze (A), improve (I) and control (C) phases, respectively. The response of the research identified in the define phase is the chipped width with Cpk of 0.66 determined from the measure phase. The half-factorial experiments are implemented in the analyze phase to find the significant factors which are water temperature, water pressure and feed rate. In improve phase, the additioanl expriments are performed according to the Box-Behnken design in order to determine the non-linear relation between the chipped width and all mentioned factors. The optimal setting of each factors are determined by applied the response surface optimizer. Under the optimal setting, the control charts are used in the control phase to monitor the chipped width. The resulted Cpk of the response is increased to 1.39 which is greater than the one-sided accpetable process capability of 1.25. © (2014) Trans Tech Publicutions, Switzerland.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This research applies Six Sigma approach in order to reduce defect by increasing the assembly process capability index (Cpk) of Integrated Circuit (IC) production process. This study applies five phases (DMAIC) of Six Sigma approach beginning with define (D), measure (M), analyze (A), improve (I) and control (C) phases, respectively. The response of the research identified in the define phase is the chipped width with Cpk of 0.66 determined from the measure phase. The half-factorial experiments are implemented in the analyze phase to find the significant factors which are water temperature, water pressure and feed rate. In improve phase, the additioanl expriments are performed according to the Box-Behnken design in order to determine the non-linear relation between the chipped width and all mentioned factors. The optimal setting of each factors are determined by applied the response surface optimizer. Under the optimal setting, the control charts are used in the control phase to monitor the chipped width. The resulted Cpk of the response is increased to 1.39 which is greater than the one-sided accpetable process capability of 1.25. © (2014) Trans Tech Publicutions, Switzerland.
2013
Aunticha Pongtrairat, Angsumalin Senjuntichai
Spiral defect reduction of hard disk drive media Journal Article
In: Applied Mechanics and Materials, vol. 421, pp. 93 – 98, 2013, (Cited by: 2).
@article{Pongtrairat201393,
title = {Spiral defect reduction of hard disk drive media},
author = {Aunticha Pongtrairat and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886262032&doi=10.4028%2fwww.scientific.net%2fAMM.421.93&partnerID=40&md5=be23bf054afe65a91af3248dbb15a44d},
doi = {10.4028/www.scientific.net/AMM.421.93},
year = {2013},
date = {2013-01-01},
journal = {Applied Mechanics and Materials},
volume = {421},
pages = {93 – 98},
abstract = {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%. © (2013) Trans Tech Publications, Switzerland.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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%. © (2013) Trans Tech Publications, Switzerland.
Acharaporn Dumrongvanich, Angsumalin Senjuntichai
Bit Error Rate improvement of Hard disk drive Journal Article
In: Advanced Materials Research, vol. 740, pp. 670 – 675, 2013, (Cited by: 0).
@article{Dumrongvanich2013670,
title = {Bit Error Rate improvement of Hard disk drive},
author = {Acharaporn Dumrongvanich and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884794931&doi=10.4028%2fwww.scientific.net%2fAMR.740.670&partnerID=40&md5=0f0033862b126b3ac1fd1a71d0062df5},
doi = {10.4028/www.scientific.net/AMR.740.670},
year = {2013},
date = {2013-01-01},
journal = {Advanced Materials Research},
volume = {740},
pages = {670 – 675},
abstract = {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 pvalue 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%. © (2013) Trans Tech Publications, Switzerland.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 pvalue 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%. © (2013) Trans Tech Publications, Switzerland.
2012
Tangjitsitcharoen Somkiat, Angsumalin Senjuntichai
Intelligent monitoring and prediction of surface roughness in ball-end milling process Journal Article
In: Applied Mechanics and Materials, vol. 121-126, pp. 2059 – 2063, 2012, (Cited by: 4).
@article{Somkiat20122059,
title = {Intelligent monitoring and prediction of surface roughness in ball-end milling process},
author = {Tangjitsitcharoen Somkiat and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255158360&doi=10.4028%2fwww.scientific.net%2fAMM.121-126.2059&partnerID=40&md5=a83e02c55ff55999220776a5606dd379},
doi = {10.4028/www.scientific.net/AMM.121-126.2059},
year = {2012},
date = {2012-01-01},
journal = {Applied Mechanics and Materials},
volume = {121-126},
pages = {2059 – 2063},
abstract = {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.},
note = {Cited by: 4},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
Tangjitsitcharoen Somkiat, Senjuntichai Angsumalin
Comparison of in-process cutting state detection in CNC turning using different neural network systems Journal Article
In: Applied Mechanics and Materials, vol. 121-126, pp. 1942 – 1946, 2012, (Cited by: 5).
@article{Somkiat20121942,
title = {Comparison of in-process cutting state detection in CNC turning using different neural network systems},
author = {Tangjitsitcharoen Somkiat and Senjuntichai Angsumalin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255158401&doi=10.4028%2fwww.scientific.net%2fAMM.121-126.1942&partnerID=40&md5=2e5c34392d5d13ca03b8e440484cc5c9},
doi = {10.4028/www.scientific.net/AMM.121-126.1942},
year = {2012},
date = {2012-01-01},
journal = {Applied Mechanics and Materials},
volume = {121-126},
pages = {1942 – 1946},
abstract = {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.},
note = {Cited by: 5},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
2011
Napassavong Rojanarowan, Angsumalin Senjuntichai
Development of efficient washing system for reduction of oil contamination on machining parts Journal Article
In: Advanced Materials Research, vol. 156-157, pp. 1545 – 1554, 2011, (Cited by: 2).
@article{Rojanarowan20111545,
title = {Development of efficient washing system for reduction of oil contamination on machining parts},
author = {Napassavong Rojanarowan and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650849455&doi=10.4028%2fwww.scientific.net%2fAMR.156-157.1545&partnerID=40&md5=3734a91ff0f173fc07536f899071beff},
doi = {10.4028/www.scientific.net/AMR.156-157.1545},
year = {2011},
date = {2011-01-01},
journal = {Advanced Materials Research},
volume = {156-157},
pages = {1545 – 1554},
publisher = {Trans Tech Publications Ltd},
abstract = {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 $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. © (2011) Trans Tech Publications.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 $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. © (2011) Trans Tech Publications.
2010
Angsumalin Senjuntichai
Process setting through general linear model and response surface method Conference
vol. 1285, 2010, (Cited by: 2).
@conference{Senjuntichai2010237,
title = {Process setting through general linear model and response surface method},
author = {Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649864039&doi=10.1063%2f1.3510550&partnerID=40&md5=ffc6126e675f18d31cb35907ddc454b7},
doi = {10.1063/1.3510550},
year = {2010},
date = {2010-01-01},
journal = {AIP Conference Proceedings},
volume = {1285},
pages = {237 – 248},
abstract = {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 condition and the predicted percentage of defectives at 4.62% under RSM process condition are not significant different. But at 95% confidence level, the percentage of defectives under RSM condition can be much lower approximately 2.16% than those under GLM condition in accordance with wider variation. Lastly, the percentages of defectives under the conditions suggested by GLM and RSM are reduced by 55.81% and 62.95%, respectively. © 2010 American Institute of Physics.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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 condition and the predicted percentage of defectives at 4.62% under RSM process condition are not significant different. But at 95% confidence level, the percentage of defectives under RSM condition can be much lower approximately 2.16% than those under GLM condition in accordance with wider variation. Lastly, the percentages of defectives under the conditions suggested by GLM and RSM are reduced by 55.81% and 62.95%, respectively. © 2010 American Institute of Physics.
Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
In-process monitoring and prediction of surface roughness in ball-end milling process Conference
Danube Adria Association for Automation and Manufacturing, DAAAM, 2010, (Cited by: 5).
@conference{Tangjitsitcharoen20101389,
title = {In-process monitoring and prediction of surface roughness in ball-end milling process},
author = {Somkiat Tangjitsitcharoen and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897851656&partnerID=40&md5=65af7c86d756a612c22b04fc7f9692d3},
year = {2010},
date = {2010-01-01},
journal = {Annals of DAAAM and Proceedings of the International DAAAM Symposium},
pages = {1389 – 1390},
publisher = {Danube Adria Association for Automation and Manufacturing, DAAAM},
abstract = {The objective of this research is to propose a practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtained results 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 multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval (PI) of the in- process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness at 95% confident level. 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.},
note = {Cited by: 5},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The objective of this research is to propose a practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtained results 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 multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval (PI) of the in- process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness at 95% confident level. 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.
Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
Monitoring of surface roughness in CNC turning process Conference
Danube Adria Association for Automation and Manufacturing, DAAAM, 2010, (Cited by: 3).
@conference{Tangjitsitcharoen20101391,
title = {Monitoring of surface roughness in CNC turning process},
author = {Somkiat Tangjitsitcharoen and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953869934&partnerID=40&md5=2f631ccc90bcb99b1abb7a72fb027720},
year = {2010},
date = {2010-01-01},
journal = {Annals of DAAAM and Proceedings of the International DAAAM Symposium},
pages = {1391 – 1392},
publisher = {Danube Adria Association for Automation and Manufacturing, DAAAM},
abstract = {In order to realize the intelligent machine tools, the objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in- process surface roughness model is developed base on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness. It is proved by the cutting tests that the propose 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.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In order to realize the intelligent machine tools, the objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in- process surface roughness model is developed base on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness. It is proved by the cutting tests that the propose 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.
Angsumalin Senjuntichai, Anulark Techanitisawad, Huynh Trung Luong
The analysis of patent option for RDA project valuation* Journal Article
In: Journal of Advanced Mechanical Design, Systems and Manufacturing, vol. 4, no. 3, pp. 683 – 700, 2010, (Cited by: 0; All Open Access, Bronze Open Access).
@article{Senjuntichai2010683,
title = {The analysis of patent option for RDA project valuation*},
author = {Angsumalin Senjuntichai and Anulark Techanitisawad and Huynh Trung Luong},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051935945&doi=10.1299%2fjamdsm.4.683&partnerID=40&md5=0726df388e477dc08a8ffaf132f824b2},
doi = {10.1299/jamdsm.4.683},
year = {2010},
date = {2010-01-01},
journal = {Journal of Advanced Mechanical Design, Systems and Manufacturing},
volume = {4},
number = {3},
pages = {683 – 700},
abstract = {This paper proposes an application of the option valuation approach to evaluate a project investment in three stages: research (R), development (D) and acquisition (A). To reflect different correlation and effect of each type of uncertainty on option values and, consequently, investment decisions, the proposed valuation and decision model incorporates both technical and market uncertainties into the first two technical stages (R&D) of the project, and the market uncertainty only into the last stage (A). Changes in project values are accordingly captured in each stage by the combined geometric Brownian motion and Poisson jump downward processes. The model incorporates the patent sale alternatives in the development and acquisition stages. A dynamic programming, decision tree model is solved to determine the option values and optimal decisions subject to decision rules, critical values, and certain boundary conditions. We subsequently evaluate the model effectiveness by comparing its decisions with those of an existing valuation model and the net present value method. The Monte Carlo simulation results show that under the option valuation by which the loss is limited to the initial costs of investment, a positive profit in a wide range can be obtained with more than 50% chance, in spite of the small average profit. The results of simulation also verify the significance of the chance to sell the patent as a safer decision under the high market uncertainty. In addition, a shorter term of patent agreement significantly improved advantage of the patent sale over the immediate investment in an optimistic situation. Copyright © 2010 by JSME.},
note = {Cited by: 0; All Open Access, Bronze Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper proposes an application of the option valuation approach to evaluate a project investment in three stages: research (R), development (D) and acquisition (A). To reflect different correlation and effect of each type of uncertainty on option values and, consequently, investment decisions, the proposed valuation and decision model incorporates both technical and market uncertainties into the first two technical stages (R&D) of the project, and the market uncertainty only into the last stage (A). Changes in project values are accordingly captured in each stage by the combined geometric Brownian motion and Poisson jump downward processes. The model incorporates the patent sale alternatives in the development and acquisition stages. A dynamic programming, decision tree model is solved to determine the option values and optimal decisions subject to decision rules, critical values, and certain boundary conditions. We subsequently evaluate the model effectiveness by comparing its decisions with those of an existing valuation model and the net present value method. The Monte Carlo simulation results show that under the option valuation by which the loss is limited to the initial costs of investment, a positive profit in a wide range can be obtained with more than 50% chance, in spite of the small average profit. The results of simulation also verify the significance of the chance to sell the patent as a safer decision under the high market uncertainty. In addition, a shorter term of patent agreement significantly improved advantage of the patent sale over the immediate investment in an optimistic situation. Copyright © 2010 by JSME.