• Korean Journal of Computational Design and Engineering
• /
• v.4 no.1
• /
• pp.43-51
• /
• 1999

Producing the relevant information (features) from the CAD models of CAM, called feature recognition or extraction, is the essential stage for the integration of CAD and CAM. Most feature recognition methods, however, have problems in the recognition of intersecting features because they do not handle the intersection geometry properly. In this paper, we propose a machining feature recognition algorithm, which has a solid model consisting of orthogonal primitives as input. The algorithm calculates candidate features and constitutes the Intersection Geometry Matrix which is necessary to represent the spatial relation of candidate features. Finally, it recognizes machining features from the proposed candidate features dividing and growing systems using half space and Boolean operation. The algorithm has the following characteristics: Though the geometry of part is complex due to the intersections of design primitives, it can recognize the necessary machining features. In addition, it creates the Maximal Feature Volumes independent of the machining sequences at the feature recognition stage so that it can easily accommodate the change of decision criteria of machining orders.

• Smart Media Journal
• /
• v.12 no.2
• /
• pp.9-14
• /
• 2023

In this paper, we proposed a face recognition network which attempts to use more facial features awhile using smaller number of training sets. When combining the neural network together for face recognition, we want to use networks that use different part of the facial features. However, the network training chooses randomly where these facial features are obtained. Other hand, the judgment basis of the network model can be expressed as a saliency map through gradCAM. Therefore, in this paper, we use gradCAM to visualize where the trained face recognition model has made a observations and recognition judgments. Thus, the network combination can be constructed based on the different facial features used. Using this approach, we trained a network for small face recognition problem. In an simple toy face recognition example, the recognition network used in this paper improves the accuracy by 1.79% and reduces the equal error rate (EER) by 0.01788 compared to the conventional approach.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.7 no.1
• /
• pp.126-134
• /
• 1998

In order to increase the modeling power and productivity of CAD/CAM systems, demands for customization of CAD/CAM system increased. Customization of a CAM system involves making it easier to learn and use, adding new modeling features not supported in a general purpose CAM system, and providing parametric inputting mechanism. A customization from a commercial CAM system (OMEGA) has implemented for two dimensional free curve CNC glass scribing machine of medium size company in Cheonan. A CAD technician who has no CAM experience can operate this customized CAM system satisfactorily.

• Journal of agricultural medicine and community health
• /
• v.33 no.2
• /
• pp.181-192
• /
• 2008

Objective: The use of complementary and alternative medicine (CAM) is common especially among patients with osteoarthritis The aim of this study was to investigate the utilization rate and expenditures of patients who use CAM. Method: Two hundred seventy four patients with osteoarthritis were interviewed by a telephone survey. A structured questionnaire about sociodemographic features and type, cost, satisfaction and reason of CAM utilization was used Results: Among 274 patients with osteoarthritis, 251 patients(91.6%) had used at least one type of CAM during six months. There was a significant difference in sex (female), age (70 years), medical security (insurance), educational level between the user and non-user of CAM. Hyperthermia was the most use. The average cost for CAM utilization was 120 thousands won/person during six months and there was no difference in sociodemographic features among the out-of-pocket cost of users. The scores of satisfaction for CAM use were ranged between 60-70. Conclusions: CAM became a popular source of health care because of elderly and lay referral system. And Korean spent a substantial amount of out-of-pocket money on CAM without benefit. Health care system and professionals should pay more attention to CAM, make a evidence for CAM.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.10
• /
• pp.2446-2456
• /
• 1993

By virtue of progress of computer science, CAD/CAM technology has been developed greatly in each area. But the problems in the integration of CAD/CAM are not yet solved completely. The reason is that the exchange of data between CAD and CAM is difficult because the domains of design and manufacturing are different in nature. To solve this problem, a feature based modeller is developed in this study, which makes it possible to communicate between design and manufacturing through features. The modeller has feature, the concept of semi-bounded plane is introduced, and implemented as a B-rep sheet model using half-edge data structure. The features are then created on a part by local modification of the boundary on a part based on feature template information. This approach generalizes the modelling of features in a geometry model.

• Korean Journal of Computational Design and Engineering
• /
• v.3 no.4
• /
• pp.274-282
• /
• 1998

A feature is a local shape of a product directly related to the manufacturing process. The feature plays a role of the bridge connecting CAD and CAM. In the process planning for he CAM, information on manufacturing is required. To get the a manufacturing information from CAD dat, we need to recognize features. Once features are recognized, they are used as an input for the process planning. In this paper, we thoroughly investigate the composite features, which are generated by interacting simple features. The simple features in the composite feature usually have precedence relation in terms of process sequence. Based on the reason for the precedence relation, we classify the composite features for the process planning. In addition to the precedence relation, approach direction is used as an input for the process planning. In the process planning, the number of set-up orientations are minimized whole process sequence for the features are generated. We propose a process planning algorithm based on the topological sort and breadth-first search of graphs. The algorithn is verified using sample products.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.290-295
• /
• 1989

This paper proposes a Expert CAPP System for integrating CAD/CAM of rotational work-part by rule based approach. The CAD/CAPP integration is performed by the recognition of machined features from the 2-D CAD data (IGES) file. Selecting functions of the process planning are performed in modularized rule base by forward chaining inference, and operation sequences are determined by means of heuristic search algorithm. For CAPP/CAM integration, post-processor generates NC code from route sheet file. This system coded in OPS5 and C language on PC/AT, and EMCO CNC lathe interfaced with PC through DNC and RS-232C.

• Korean Journal of Computational Design and Engineering
• /
• v.3 no.4
• /
• pp.223-235
• /
• 1998

A dedicated CAM(Computer-Aided Manufacturing) system has been developed, which generated tool-path to machine Styrofoam stamping die-patterns in Chrysler Corporation. A previous process to build die-patterns was to "stick build" the pattern, in which stock is cut & glued together, and then the NC machining of part-surface shape completes building a Styrofoam die-pattern. The current process utilizes the developed CAM system, and almost removes the manual work, consequently reduces the overall lead time. The paper presents the overall system structures, tool-path generation, and some features of Styrofoam pattern machining.

• Journal of Multimedia Information System
• /
• v.2 no.3
• /
• pp.255-262
• /
• 2015

The modern manufacturing systems and technologies produce products that are more accurate day by day. This can be reached mainly by improvement the manufacturing process with at the same time restricting more and more the quality specifications and reducing the uncertainty in part. The main objective an industry becomes to lower the part's variability, since the less variability - the better is product. One of the part of this task is measuring the object's uncertainty. The main purpose of this study is to understand the application of bootstrap method for uncertainty evaluation. Bootstrap method is a collection of sample re-use techniques designed to estimate standard errors and confidence intervals. In the case study a surface of an automobile engine block - (Top view side) is measured by Coordinate Measuring Machine (CMM) and analyzed for uncertainty using Geometric Least Squares in complex with bootstrap method. The designed experiment is composed by three similar measurements (the same features in unique reference system), but with different points (5, 10, 20) concentration at each level. Then each cloud of points was independently analyzed by means of non-linear Least Squares, after estimated results have been reported. A MatLAB software tool used to generate new samples using bootstrap function. The results of the designed experiment are summarized and show that the bootstrap method provides the possibility to evaluate the uncertainty without repeating the Coordinate Measuring Machine (CMM) measurements many times, i.e. potentially can reduce the measuring time.

• Smart Media Journal
• /
• v.8 no.3
• /
• pp.31-40
• /
• 2019

To understand how a Convolutional Neural Network (CNN) model captures the features of a pattern to determine which class it belongs to, in this paper, we use Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize and analyze how well a CNN model behave on the CNU weeds dataset. We apply this technique to Resnet model and figure out which features this model captures to determine a specific class, what makes the model get a correct/wrong classification, and how those wrong label images can cause a negative effect to a CNN model during the training process. In the experiment, Grad-CAM highlights the important regions of weeds, depending on the patterns learned by Resnet, such as the lobe and limb on 미국가막사리, or the entire leaf surface on 단풍잎돼지풀. Besides, Grad-CAM points out a CNN model can localize the object even though it is trained only for the classification problem.