• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.89-101
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• 2008

The most used tool for quality control is control chart in manufacturing industry. But it has limitations at current situation where most of manufacturing facilities are automated and several manufacturing processes have interdependent relationship such as CCM assembly line. To Solve problems, we propose quality management system based on data mining that are consisted of monitoring system where it monitors flows of processes at single window and feature extraction system where it predicts the yield of final product and identifies which processes have impact on the quality of final product. The quality management system uses decision tree, neural network, self-organizing map for data mining. We hope that the proposed system can help manufacturing process to produce stable quality of products and provides engineers useful information such as the predicted yield for current status, identification of causal processes for lots of abnormality.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.145-149
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• 2017

One of the challenges facing precision manufacturers is the increasing feature complexity of tight tolerance parts. All engineering drawings must account for the size, form, orientation, and location of all features to ensure manufacturability, measurability, and design intent. Geometric controls per ASME Y14.5 are typically applied to specify dimensional tolerances on engineering drawings and define size, form, orientation, and location of features. Many engineering drawings lack the necessary geometric dimensioning and tolerancing to allow for timely and accurate inspection and verification. Plus-minus tolerancing is typically ambiguous and requires extra time by engineering, programming, machining, and inspection functions to debate and agree on a single conclusion. Complex geometry can result in long inspection and verification times and put even the most sophisticated measurement equipment and processes to the test. In addition, design, manufacturing and quality engineers are often frustrated by communication errors over these features. However, an approach called profile tolerancing offers optimal definition of design intent by explicitly defining uniform boundaries around the physical geometry. It is an efficient and effective method for measurement and quality control. There are several advantages for product designers who use position and profile tolerancing instead of linear dimensioning. When design intent is conveyed unambiguously, manufacturers don't have to field multiple question from suppliers as they design and build a process for manufacturing and inspection. Profile tolerancing, when it is applied correctly, provides manufacturing and inspection functions with unambiguously defined tolerancing. Those data are manufacturable and measurable. Customers can see cost and lead time reductions with parts that consistently meet the design intent. Components can function properly-eliminating costly rework, redesign, and missed market opportunities. However a supplier that is poised to embrace profile tolerancing will no doubt run into resistance from those who would prefer the way things have always been done. It is not just internal naysayers, but also suppliers that might fight the change. In addition, the investment for suppliers can be steep in terms of training, equipment, and software.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.150-153
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• 2022

This paper reports a technique that automatically extracts object shapes through Dense-Net, and subsequently, detects the objects using Mask R-CNN in a manufacturing site, in which workers and objects are mixed. It is based on the customized factory dataset by targeting workers, machines, tools, control boxes, and products as the objects. Mask R-CNN supports multi-object recognition as a well-known object recognition method, while Dense-Net effectively extracts a feature from multiple and overlapping objects. After immediate implementation using the two technologies, the object is naturally extracted from a still image of the manufacturing site to describe image. Afterwards, the result is planned to be used to detect workers' abnormal behavior by adding a label on the objects.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1109-1114
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• 2015

This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.97-102
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• 2008

In this paper we propose an object-based image retrieval method using spatial color model and feature points registration method for an effective color query detection. The proposed method in other to overcome disadvantages of existing color histogram methods and then this method is use the HMMD model and rough set in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the database image and query image. Here, we select candidate regions in the similarity between the query image and database image. And we use SIFT registration methods in the selected region for object retrieving. The experimental results show that the proposed method is more satisfactory detection radio than conventional method.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.3
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• pp.41-46
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• 2001

This paper discusses the feasibility of improving micro-machining accuracy by using two-dimensional(2-D) vibration cutting. Vibration cutting is generated by two piezo actuators arranged orthogonally : one is actuated by a sine curve voltage input, and the other is actuated by a phase-shifted sine curve voltage. A tool attached to the vibrator oscillates in a 2-D elliptical motion, depending on the frequencies, amplitudes, and the phase shifts of two input signals and the workpiece feedrate. Along the elliptical tool locus, cutting is done in the lower part, and non-cutting is done in the upper part. By this way a unique feature of 2-D vibration cutting, that is, air lubrication between a tool and chips, is caused. Another unique feature of 2-D vibration cutting was experimentally verified, that is, some negative thrust force occurs as the direction of chip movement on a tool rake face is reversed. Those features not only help chips flow smoothly and continuously but also reduce cutting force, which results in a higher quality machined surface. Through tool path simulations and experiments under several micro-machining conditions, the 2-D vibration cutting, compared to conventional cutting, was found to result in a great decrease in the cutting force, a much smoother surface, and much less burr.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1816-1819
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• 2003

As a result of milling operation, we expect to have burr at the outward edge of workpiece. Also, it causes undesirable problems such as deburring cost, low quality of machined surface, and bottleneck in manufacturing process. Though it is impossible to totally remove burr in machining, it is necessary to plan a machining process that minimizes the occurrence of burr. In this paper, a scheme is proposed which identifies the tool path of the milling operation with minimum burr. In the previous research, a Burr Expert System was developed where the feature identification, the cutting condition identification, and the analysis on exit burr formation are the key steps in the program. The Burr Expert System predicts which portion of workpiece would have the exit burr in advance so that we can calculate the burr length of each milling operation. Here, the critical angle determines whether the burr analyzed is an exit burr or not. So the burr minimization scheme becomes to minimize the burr with critical angle. By iterating all the possible tool paths in certain milling operation, we can identify the tool path with minimum burr.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.25-33
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• 2003

A burr has been defined as undesirable projection of material formed as a result of plastic flow from a cutting or shearing operation. It is unavoidable in all kinds of machining operation. As a result, burr makes troubles on manufacturing process due to deburring cost, quality of products and productivity. In this study, the primary interest is about exit burr. The burr formation mechanism in each type of burr is classified. Data bases are developed to predict burr formation result. In the milling operation, we develop an algorithm to analyze the burr formation mechanism by the geometrical analysis on the multi featured workpiece with multi cutting path. The algorithm includes three steps, i. e., the feature identification, the cutting condition identification, and the analysis on exit burr formation. We can predict which portion of workpiece would have the exit burr in advance so that we can manage to find a way to minimize the exit burr formation in an actual cutting. Also, this algorithm can be implemented in a commercial CAM package so that we can simulate the NC code to review the burr formation in advance.

• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.53-68
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• 2003

Currently, it is possible to buy almost anything from books(Amazon.com) to airplane tickets(Travelocity.com) using the world wide web. The purpose of this research is to develop a "clean interface" between design and fabrication facilities for the production of custom machined parts through Internet. The current mechanism for production of prototype parts that can be fabricated using standard machine tools like milling machines, requires a process of part description preparation, bidding, contract award, and finally fabrication and delivery of the part. This is a substantially more complex process than buying a book or airplane ticket. In this paper, we try to define the ambiguous part description using XML based data exchange format and to enable e-commerce in this field. The research accomplishments are summarized: 1. Creation of a new format for data exchange of machined prototype parts, 2. Development of a prototype system to illustrate how the XML data can be effectively used to conduct e-Commerce for custom machined parts, 3. Testing of the methodology with a number of parts.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.462-467
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• 2005

This paper presents virtual machining system in order to realize turning process in virtual space. A reliable virtual turning process simulation was developed based on the surface shaping system which is capable of considering geometric model, thermal error model, and vibration model. Accuracy of surface shape resulting from proposed machining simulator was verified experimentally. This paper also developed the watchdog agent that continuously assessed, diagnosed, and predicted performance of products and machines in machining. The Watchdog agent extracted feature signal using time-frequency analysis among various signals from multi-sensor and evaluated machining condition using performance confidence value.