digital twin of ceramic processing - lucideon

Digital Twin of Ceramic Processing

Jingzhe Pan

School of Engineering

University of Leicester

jp165@le.ac.uk

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. 60–90% 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 – 623.

2) AN Samant and NB Dahotre, Laser machining of structural ceramics – A review, Journal of the European Ceramic Society

29 (2009) 969–993.

Computer modelling of ceramic processing

▪ Spray drying and die filling

▪ Injection moulding/powder compaction

▪ Drying

▪ Sintering

H. Riedel 1997Fraunhofer-Institut für

Werkstoffmechanik, Wöhlerstr.

Freiburg, Germany

Finite element modelling of

sintering deformation

Jingzhe Pan, International Materials Reviews 2003 Vol. 48 No. 2

Work by Pan’s team

co-firing of bi-layered beam

Work by Pan’s team

nonuniform initial density

Work by Pan’s team vs experiment

Work by Pan’s 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

ሶ𝜀𝑖𝑗 =𝜕Ψ

𝜕𝜎𝑖𝑗

- 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 für

Werkstoffmechanik, Wöhlerstr.

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)

𝜎𝑖𝑗

ሶ𝜀𝑖𝑗L

ρ

Training an artificial neural network to learn a constitutive law

𝜀ሶ𝑖𝑗 =ϵ0ሶ

σ0 𝐿0𝐿 3

3

2𝑐 ρ 𝑠𝑖𝑗 + 3𝑓 ρ σm − σs δ𝑖𝑗

𝜎𝑖𝑗

ሶ𝜀𝑖𝑗 = ?L =1.6 µm, ρ = 70%, 𝜎𝑠 = 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

𝜎𝑖𝑗

ሶ𝜀𝑖𝑗L

ρ

Training an artificial neural network to take chemistry

and material inputs

𝑐𝑖

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.