Research Papers

PDQ (Product Data Quality): Representation of Data Quality for Product Data and Specifically for Shape Data

[+] Author and Article Information
Yoshihito Kikuchi

Department of Electronics and Information Engineering, Faculty of Engineering, Hokkai-Gakuen University, Minami-26, Nishi-11, Chuo-ku, Sapporo 064-0926, Japan

Hiroyuki Hiraoka

Department of Precision Mechanics, Faculty of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan

Akihiko Otaka

 Nihon Unisys, Ltd., 1-1-1 Toyotsu, Koto-ku, Tokyo 135-8560, Japan

Fumiki Tanaka

Systems Science and Informatics Division, Graduate School of Information Science and Technology, Hokkaido University, Kita-14 Nishi-9, Kita-ku, Sapporo 060-0814, Japan

Kazuya G. Kobayashi

Department of Mechanical Systems Engineering, Faculty of Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-shi, Toyama 939-0398, Japan

Atsuto Soma

 ELYSIUM Co., Ltd., 11-1 Asahimachi, Chuo-ku, Hamamatsu-shi, Shizuoka 430-0927, Japan

J. Comput. Inf. Sci. Eng 10(2), 021003 (Apr 22, 2010) (8 pages) doi:10.1115/1.3402615 History: Received July 18, 2007; Revised March 02, 2010; Published April 22, 2010; Online April 22, 2010

In the communication and sharing of product data, if the difference of the required data quality and the data quality actually incorporated into data is significant, it causes various problems. It is often the case that a creator of low quality data does not realize it unless it is harmful for his job. In most cases, low quality data passed to subsequent processes, such as manufacturing process, cause problems since these are not appropriate from the machining precision point of view or the detailed shape modeling point of view. In these cases, rework or repair of data is necessitated before commencing the target process, which results in significant economy loss and delay of product development. Today’s product model data are dumb data because design intents and data quality incorporated are not explicitly represented. Receiving systems cannot know whether the data passed possess sufficient quality for the target job or not. Another problem is that engineers in later processes, such as the manufacturing process, cannot issue data quality related request beforehand in a commonly agreed manner. The problems mentioned above are caused by the lack of a commonly agreed representation of product data quality (PDQ) information. Our proposed solution is designed to enable the communication and sharing of data quality information. This paper reports the development of a PDQ standard (ISO 10303-59), which is a resource part of ISO 10303 Standard for the Exchange of Product Model Data (STEP) (2008, “ISO 10303-59, Industrial Automation Systems and Integration. Product Data Representation and Exchange. Part 59 Integrated Generic Resource: Quality of Product Shape Data,” International Standard Organization, Geneva). The objective of ISO 10303-59 is to establish a PDQ model and to enable the use of PDQ data independently or in combination with product data. The developed PDQ information model represents concepts such as data quality criteria, measurement requirements, and measured results. Based on the PDQ model, the PDQ for shape data model, which is a specialization of the PDQ model to 3D shape data quality, is also developed.

Copyright © 2010 by American Society of Mechanical Engineers
Topics: Maintenance , Shapes , Errors
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Figure 1

Typical examples of the inappropriateness of product data: (1) microscopic gap constructed with short length edges and a narrow width face (left) and (2) short length edge (right)

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Figure 2

Outline of the PDQ model framework and its extension to shape model

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Figure 3

PDQ-S data reference to geometrical/topological data

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Figure 4

PDQ information usage scenarios

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Figure 5

Concept of subtyping of PDQ model for classified error phenomenon at shape data

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Figure 6

Representation of requirements for short length edge



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