Paper Info
Title
Function-on-function regression for assessing production quality in industrial manufacturing.
Abstract
Key responses of manufacturing processes are often represented by spatially or time-ordered data known as functional data. In practice, these are usually treated by extracting one or few representative scalar features from them to be used in the following analysis, with the risk of discarding relevant information available in the whole profile and of drawing only partial conclusions. To avoid that, new and more sophisticated methods can be retrieved from the functional data analysis (FDA) literature. In this work, that represents a contribution in the direction of integrating FDA methods into the manufacturing field, the use of function-on-function linear regression modelling is proposed. The approach is based on a finite-dimensional approximation of the regression coefficient function by means of two sets of basis functions, and two roughness penalties to control the degree of smoothness of the final estimator. The potential of the proposed method is demonstrated by applying it to a real-life case study in powder bed fusion additive manufacturing for metals to predict the mechanical properties of an additively manufactured artefact, given the particle size distribution of the powder used for its production.
Year
DOI
Venue
2020
10.1002/qre.2786
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
Keywords
DocType
Volume
additive manufacturing,functional data analysis,functional linear regression,particle size distribution,stress&#8211,strain curve
Journal
36.0
Issue
ISSN
Citations 
SP8.0
0748-8017
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
B. Palumbo163.31
Fabio Centofanti200.34
Francesco Del Re300.34