• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.9-14
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• 2004

More than three years have passed since Korean companies introduced Six-Sigma. Each company used a lot of quality improvement tools for years. However, the tools have been taught to the companies with a little understanding of Six sigma tools. Therefore, it is difficult to use the tools correctly at appropriate time. This survey paper was conducted on MBBs and BBs of the manufacturing companies that introduced Six-Sigma. It is intended, in this paper, to provide the companies that will be introduce Six-Sigma a guideline of training course and tools by understanding the importance of each DMAIC stage of Six-Sigma, examining the frequently used tools in each process and their performances, and selecting the key tools for each stage based on the result of this survey.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.43-57
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• 2005

Action learning motivates students to solve open-ended problems by 'developing skills through doing'. This paper reviews the concept of action learning and discusses the adoption of action learning approach to teach operations management at universities. It presents the design and delivery of an action-learning course at City University of Hong Kong. The course incorporates classroom lectures, tutorials and an action-learning workshop. The experience gained proves that action learning facilitates student participation and teamwork and provides a venue of accelerating learning where enables students to handle dynamic problem situations more effectively. The paper concludes that adopting action-learning approach can help lecturers to enhance quality teaching in operations management courses, and provide an alternate means of effective paradigm other than traditional classroom teaching and/or computer-based training at universities.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.1-15
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• 1999

This research involves the development and evaluation of a work flow control model for a type of flexible manufacturing system(FMS) called a flexible flow line(FFL). The control model can be considered as a kind of hybrid intelligent model in that it utilizes both computer simulation and neural network technique. Training data sets were obtained using computer simulation of typical FFL states. And these data sets were used to train the neural network model. The model can easily incorporate particular aspects of a specific FFL such as limited buffer capacity and dispatching rules used. It also dynamically adapts to system uncertainty caused by such factors as machine breakdowns. Performance of the control model is shown to be superior to the random releasing method and the Minimal Part Set(MPS) heuristic in terms of machine utilization and work-in-process inventory level.

• Journal of the Korean Professional Engineers Association
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• v.19 no.2
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• pp.34-42
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• 1986

These brief studies and reports of the use of exposed aggrigate precast concrete in practice are combined with a description of some notable co-ordination such as shop drawing, manufacturing and erection in the field construction. Above mentioned exposed aggrigate P. C. means architectural P. C. which was used to the VOTRAKON site in Riyadh, Saudi Arabia. Fabricated P. C. on the site without autoclaving and steam curing plant had been successfully carried out on this project-core building, conference room and others. The project designed by HOPE/VTN international INC. which is located in San Diego, California U.S.A. stands for vocational training and related support facilities contract. We had to submit shop drawings showing complete information for fabrication and installation of P. C. unit reinforcement. Also we should be indicated member dimension and cross section, location size and type of necessary for erection. We can delineate the following characteristic results. One of the most important things how to handle exposed aggrigate P.C. unit as specified was quality assurance and co-ordination for shop drawing, manufacturing and erection.

• International Journal of Quality Innovation
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• v.9 no.1
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• pp.134-152
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• 2008

Almost every country around the world has been focusing on food safety in intense and multifaceted ways. The use of Hazard Analysis Critical Control Points (HACCP) is widely accepted as a food safety management system. This paper investigates the success factors of HACCP practices with reference to the domains of food production, processing and delivery. A literature review of food safety and management articles was conducted. Using the keywords search, the online Emerald Database was used and a total of 102 journal articles were identified between 1994 and 2007. The study examined a list of 20 success factors. Results show that 'food regulations,' 'role of the industry,' 'government policies and interventions,' 'training on food safety and hygiene,' and 'food contamination and/or poisoning' share the spotlight as being the most critical factors for HACCP practices in organisations. Future research could investigate a holistic paradigm that incorporates the success factors and aligns HACCP measures for attaining safety performance goals.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.65-73
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• 2015

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.52-57
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• 2005

In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, an on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.124-129
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• 2003

In the face of global competitor the requirements flor the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a major change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models fir simulation and quantitative description of the industrial operations involved. In this paper, a on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

• Structural Engineering and Mechanics
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• v.33 no.5
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• pp.543-560
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• 2009

The rotating Rayleigh-Timoshenko beam element based on B-spline wavelet on the interval (BSWI) is constructed to discrete short shaft and stiffness disc. The crack is represented by non-dimensional linear spring using linear fracture mechanics theory. The wavelet-based finite element model of rotor system is constructed to solve the first three natural frequencies functions of normalized crack location and depth. The normalized crack location, normalized crack depth and the first three natural frequencies are then employed as the training samples to achieve the neural networks for crack diagnosis. Measured natural frequencies are served as inputs of the trained neural networks and the normalized crack location and depth can be identified. The experimental results of fatigue crack in short shaft is also given.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.28-33
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• 2000

The monitoring of the chatter vibration is necessarily required to do automatic manufacturing system. To this study, we constructed a sensing system using tool dynamometer in order to the chatter vibration on cutting process. And a approach to a neural network using the feature of principal cutting force signals is proposed. with the error back propagation training process, the neural network memorized and classified the feature of principal cutting force signals. As a result, it is shown by neural network that the chatter vibration can be monitored effectively.