TY - JOUR
T1 - A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing
AU - Papananias, Moschos
AU - McLeay, Thomas E.
AU - Mahfouf, Mahdi
AU - Kadirkamanathan, Visakan
N1 - Funding Information:
This research is carried out under the UK Engineering and Physical Sciences Research Council ( EPSRC ) funded project Grant Reference: EP/P006930/1 , and the support is gratefully acknowledged.
Publisher Copyright:
© 2022
PY - 2022/4/1
Y1 - 2022/4/1
N2 - There is an increasing demand for manufacturing processes to improve product quality and production rates while minimising the costs. The quality of the products is influenced by several sources of errors introduced during the series of manufacturing operations. These errors accumulate over these multiple stages of manufacturing. Therefore, monitoring systems for product health utilising data and information from different sources and manufacturing stages is a key factor to meet these growing demands. This paper addresses the process of combining new measurement data or information with machine learning-based prediction information obtained as each product goes through a series of manufacturing steps to update the conditional probability distribution of the end product quality during manufacturing. A Bayesian approach is adopted in obtaining an updated posterior distribution of the end product quality given new information from subsequent measurements, and, in particular, On-Machine Probing (OMP). Following the steps of heat treatment, machining, and OMP, the posterior distribution of the previous step can be considered as the new prior distribution to obtain an updated posterior distribution of the product condition as new metrological information becomes available. It is demonstrated that the resulting posterior estimates can lead to more efficient product condition monitoring in multistage manufacturing.
AB - There is an increasing demand for manufacturing processes to improve product quality and production rates while minimising the costs. The quality of the products is influenced by several sources of errors introduced during the series of manufacturing operations. These errors accumulate over these multiple stages of manufacturing. Therefore, monitoring systems for product health utilising data and information from different sources and manufacturing stages is a key factor to meet these growing demands. This paper addresses the process of combining new measurement data or information with machine learning-based prediction information obtained as each product goes through a series of manufacturing steps to update the conditional probability distribution of the end product quality during manufacturing. A Bayesian approach is adopted in obtaining an updated posterior distribution of the end product quality given new information from subsequent measurements, and, in particular, On-Machine Probing (OMP). Following the steps of heat treatment, machining, and OMP, the posterior distribution of the previous step can be considered as the new prior distribution to obtain an updated posterior distribution of the product condition as new metrological information becomes available. It is demonstrated that the resulting posterior estimates can lead to more efficient product condition monitoring in multistage manufacturing.
KW - Bayesian inference
KW - information fusion
KW - machine learning
KW - multistage manufacturing process (MMP)
KW - on-machine probing (OMP)
KW - uncertainty of measurement
KW - UKRI
KW - EPSRC
KW - EP/P006930/1
UR - http://www.scopus.com/inward/record.url?scp=85125225894&partnerID=8YFLogxK
U2 - 10.1016/j.jmapro.2022.01.020
DO - 10.1016/j.jmapro.2022.01.020
M3 - Article
AN - SCOPUS:85125225894
SN - 1526-6125
VL - 76
SP - 475
EP - 485
JO - Journal of Manufacturing Processes
JF - Journal of Manufacturing Processes
ER -