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Lee, Young Hae; Moon, Seungki; Lee, Hong-Chul

doi:10.1023/a:1018565022922

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…They developed feature and machining parameters array and tested it in their expert system named "ASALUS" designed for rotational parts. Young et al [22] presented an approach for automated part recognition using time series analysis and neural networks. They developed signatures and autocorrelation functions which are the invariant to the transformation such as scaling, translation, and rotation.…”

Section: Introductionmentioning

confidence: 99%

“…By analyzing the past research, it is seen that researchers have focused on identifying the features and calculating the machinable volumes for rotational parts [5,13,20,21] using various feature recognition techniques [25] such as pattern recognition [6,7], volumetric decomposition [8,20], attributed adjacency graph [17,26], syntactic time series analysis and neural networks [22] and projective feature recognition [23]. However, only in [5,13] a distinction between rough and finish cuts has been made while developing the process plans.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A STEP AP 203–214-based machinable volume identifier for identifying the finish-cut machinable volumes from rough-machined parts

Anbalagan

Mehta

Jain

2008

Int J Adv Manuf Technol

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This paper presents a STEP AP203-214-based machinable volume identifier (MVI) to identify the finish-cut machinable volume in prismatic parts by deducting the rough-machined part from the final part. The MVI provides an intermediate link between rough and finish machining computer-aided process planning system for automatic generation of process plans while machining prismatic parts. To calculate the machinable volumes of manufacturing features, the MVI utilizes the output of the feature identifier which contains the information about the dimensional details, edge loops, edges, vertices, coordinate points, and location planes of the features. In this research, a total of 234 features have been considered; out of which, 32 are normal and 202 are tapered. To calculate the machinable volumes for these features, generalized methodologies are developed for 17 basic feature types, each having a varying number of specific features. Initially, the pattern strings are generated for the front and back face of the rough-machined feature and final feature. Then, MVI uses the predefined syntactic pattern strings stored in the strings database and checks with the generated strings of the feature to determine the shape of the machinable volume stored in the volumes database. After determining the shape, one relevant methodology or more (for features having combination of more than one taper) are selected from among the 17 "feature type" specific methodologies developed for finish-cut machinable volume identification. In this article, methodology is presented for one basic feature type which covers 14 features and explained through one case study. The final output from this module is stored as a text file with full dimensional details of machinable volumes for later use inside the machining planning module. The proposed MVI can be used in Computer Integrated Manufacturing Industries as an intermediate linker to achieve a robust manufacturing environment.

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Section: Introductionmentioning

confidence: 99%