Treffer: Part Location algorithms for an Intelligent Fixturing System. Part 1 : System description and algorithm development

Title:
Part Location algorithms for an Intelligent Fixturing System. Part 1 : System description and algorithm development
Source:
Journal of manufacturing systems. 20(2):124-134
Publisher Information:
Kidlington: Elsevier, 2001.
Publication Year:
2001
Physical Description:
print, 16 ref
Original Material:
INIST-CNRS
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
DaimlerChrysler Corp., Auburn Hills, Michigan, United States
Dept. of Industrial and Manufacturing Engineering and Dept. of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States
Lamb Technicon Machining Systems, Warren, Michigan, United States
ISSN:
0278-6125
Rights:
Copyright 2001 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mechanical engineering. Mechanical construction. Handling
Accession Number:
edscal.1117825
Database:
PASCAL Archive

Weitere Informationen

An Intelligent Fixturing System (IFS) is currently being developed to hold a family of cylinder heads for machining operations. This system incorporates a Part Location System (PLS) to locate the workpiece reference frame of a cylinder head relative to a pallet. This two-part paper describes the development and evaluation of various part location algorithms that have been created for this application. Part 1 of this paper provides a detailed description of the IFS and the PLS and of the cylinder head location problem. It also provides a detailed description of three different algorithms that were developed for the PLS. The first algorithm, SeQuential Least Squares (SQLS), is based on the sequential, parametric regression model. The second class of algorithms, SiMultaneous Least Squares (SMLS), is based on the simultaneous, parametric regression model. The third algorithm, SQLS-SMLS (fixed cd) is a hybrid of the first two methods.