M., H., F., M., M., M., A., E. (2008). MACHINING PROCESS PLANNING THROUGH LATENT VARIABLE MODEL INVERSION. test, 13(13th International Conference on Applied Mechanics and Mechanical Engineering.), 135-155. doi: 10.21608/amme.2008.39734
HUSSEIN W. M.; MAC-GREGOR J. F.; MANSOUR D.M. M.; ELBESTAWI M. A.. "MACHINING PROCESS PLANNING THROUGH LATENT VARIABLE MODEL INVERSION". test, 13, 13th International Conference on Applied Mechanics and Mechanical Engineering., 2008, 135-155. doi: 10.21608/amme.2008.39734
M., H., F., M., M., M., A., E. (2008). 'MACHINING PROCESS PLANNING THROUGH LATENT VARIABLE MODEL INVERSION', test, 13(13th International Conference on Applied Mechanics and Mechanical Engineering.), pp. 135-155. doi: 10.21608/amme.2008.39734
M., H., F., M., M., M., A., E. MACHINING PROCESS PLANNING THROUGH LATENT VARIABLE MODEL INVERSION. test, 2008; 13(13th International Conference on Applied Mechanics and Mechanical Engineering.): 135-155. doi: 10.21608/amme.2008.39734

MACHINING PROCESS PLANNING THROUGH LATENT VARIABLE MODEL INVERSION

The International Conference on Applied Mechanics and Mechanical Engineering
Article 118, Volume 13, 13th International Conference on Applied Mechanics and Mechanical Engineering., May 2008, Pages 135-155    PDF (424.13 K)
Document Type: Original Article
DOI: 10.21608/amme.2008.39734
Authors
HUSSEIN W. M.1; MAC-GREGOR J. F.2; MANSOUR D.M. M.3; ELBESTAWI M. A.2
1Egyptian Armed Forces.
2McMaster University, Hamilton, Canada.
3Ain shams University, Cairo, Egypt.
Abstract
ABSTRACT
Manufacturers are always exerting significant effort to improve the quality of machined
parts by suitable choice of process parameters. Furthermore, there is a trend within
industry to improve process performance and product quality through analyzing
available historical data especially in chemical industry. This trend is driven by the need
to reduce product development time and cost. The use of latent variable modeling using
historical data has been proposed in the past for product design and quality
improvement (C.M. Jaeckle and J.F. MacGregor) [23]. This paper outlines the
application of such approach using Projection to Latent Structure (PLS) and its model
inversion to facilitate the choice of cutting parameters for a desired surface roughness
while maximizing the Metal Removal Rate (MRR). The approach is mainly based on
using historical data readily available on most of factory platform and simulated through
experiments conducted on three different milling machines under normal conditions
(sharp tool and stable cut). The model inversion approach is formulated in an
optimization problem using the latent space linear model with nonlinear constraint. The
approach output solutions were validated with the results showing that the proposed
technique can be used for process planning and quality improvement of machining
data.
Keywords
Model inversion; Process Planning; milling; surface roughness modeling; Multivariate Analysis
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