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Digital Twin of Ceramic Processing
Jingzhe Pan
School of Engineering
University of Leicester
Challenges for the ceramics industry
⪠High rejection rate
e.g. 60% of the amount of revenue generated [1]
⪠Excessively high cost of post-sintering machining
e.g. 60â90% of total cost of finished product [2]
1) J Bhamua, KS Sangwan, Reduction of Post-kiln Rejections for improving Sustainability in Ceramic Industry: A Case Study,
Procedia CIRP, 26 (2015), 618 â 623.
2) AN Samant and NB Dahotre, Laser machining of structural ceramics â A review, Journal of the European Ceramic Society
29 (2009) 969â993.
Computer modelling of ceramic processing
⪠Spray drying and die filling
⪠Injection moulding/powder compaction
⪠Drying
⪠Sintering
H. Riedel 1997Fraunhofer-Institut fĂźr
Werkstoffmechanik, WĂśhlerstr.
Freiburg, Germany
Finite element modelling of
sintering deformation
Jingzhe Pan, International Materials Reviews 2003 Vol. 48 No. 2
Work by Panâs team
co-firing of bi-layered beam
Work by Panâs team
nonuniform initial density
Work by Panâs team vs experiment
Work by Panâs team
cracking during constrained sintering
Challenges for computer modelling of sintering
⢠no chemistry/material input
Challenges for computer modelling of sintering
⢠extremely sensitive constitutive law
Thermodynamics dictates that
áśđđđ =đΨ
đđđđ
- strain rate potentialΨ
Challenges for computer modelling of sintering
⢠extremely sensitive constitutive law
Challenges for computer modelling of sintering
⢠extremely sensitive constitutive law
H. Riedel 1997Fraunhofer-Institut fĂźr
Werkstoffmechanik, WĂśhlerstr.
Freiburg, Germany
Finite element modelling of
sintering deformation
Requires individual measurement
of the constitutive properties
Digital Twin
A digital twin of sintering (Leicester ongoing work)
đđđ
áśđđđL
Ď
Training an artificial neural network to learn a constitutive law
đáśđđ =Ďľ0áś
Ď0 đż0đż 3
3
2đ Ď đ đđ + 3đ Ď Ďm â Ďs δđđ
đđđ
áśđđđ = ?L =1.6 Âľm, Ď = 70%, đđ = 1.0MPa
Work by Venkat Ghantasala, PhD student; Shuihua Wang, PDRA
0.896 0.735 0.606 0.658 0.802 0.912
Training an artificial neural network to learn the constitutive law
đđđ
áśđđđL
Ď
Training an artificial neural network to take chemistry
and material inputs
đđ
Training an artificial neural network to learn from
manufacturing data
Concluding remark
⢠A digital twin turns the manufacturing process of advanced
ceramics into a material laboratory, such that issues are
identified and resolved, and the process is optimised.
⢠Simulation-based control under real-time constraint is
possible.
⢠The digital twin can communicate with production through
the Internet of Things (5G), opening the door for separation
of skills in manufacturing and mathematical modelling.