Next Generation Domain-Services in PL-Grid Infrastructure for Polish Science Górecki 1,2, Bachniak 1,2, Liput 2, Rauch 1,2, Kitowski 2,3, Pietrzyk 1,2 1 AGH University, Department of Applied Computer Science and Modelling, 2 AGH University, ACC Cyfronet AGH 3 AGH University, Department of Computer Science, Krakow, Poland CGW’15 Metamodelling of metal forming processes with Artificial Neural Networks by using High Performance Computing infrastructures

Short introduction Metamodeling concept diagram Data collecting idea Training concept with providers – separated clipboards for networks Proper metamodel storing Practical application for metamodels Example results Agenda

Design of production processes Rolling Forging Stamping Welding Flow forming Dimensions: L > 10m, W > 30t Precision: ϕ < 50µ

How to design the production process? Build the model p p – N p input process properties Apply material properties p m – N m input material properties Apply boundary conditions Perform numerical simulation OBJECTIVE – to obtain a set of optimal p p and p m !!! X Solve ill-posed inverse problem (optimization procedure) SENSITIVITY ANALYSIS Allows to determine influence of input parameters of the model on its output parameters. Select and configure SA method Sample of (N p +N m ) hypercube to receive N samples Perform N simulations of the process Analyse results

Metamodeling Metamodeling is a technique to build and use derivatives of models. The main goal is to speed up computations by replacing application used model with application used metamodel. Metamodel is built from simulation data. The most commonly approach to build metamodel is artificial neural network. Their popularity based on simplicity, elasticity and effectiveness. Most popular architecture is Multi-Layer Perceptron.

Metamodeling proces - developed software MSE or RMSE Many ANNs

Metamodeling proces - developed software

Variants: ANN Forming ANN Retraining

Metamodeling process – intelligent provider class IntelligentFormingProvider Preparation of stirring logic Initialization of training logic Stirring data Making training epoch Swapping clipboards

1 Data stirring Metamodeling proces – swapping clipbords Best trained clipboard Best validated clipboard Delayed clipboard Auxiliary clipboard Each clipboard contains: Training errors Learninig objects Neural Network MakeLogic sequence: 2 Modified back propagation algorithm 3 4 Training error calculation Selection of best clipboards Error Parallel Loop

Clipboard error chart - inteligent provider Theoretical error chart Training data ~20% Validation data ~70% Testing data ~10%

Simple ANN based metamodel format [MLP - Single network | Connections: all to all] [inputs count] 2 [layers count] 3 [neurons counts] [weights] [layer 1] [layer 2]... [layer 3]... [biases] [layer 1] [layer 2]... [layer 3]... [activation functions] [layer 1] TansigFunction [layer 2]... [layer 3]... [transformation parameters] [inputs] LinearTransformer : : LinearTransformer : : [outputs]... Number of inputsNumber of layers Number of neurons inside layers Header identifying format Number of layer inputs Number of neurons Number of network inputs Number of network outputs Transformation object Input data range Output data range

Inverse analysis with metamodels Metamodeling GOAL

Implementation idea

Results comparison between FEM and ANN for indentification of model parameters Upsetting process Displacement ANN training duration: 21 days Single optimization time: 59s PC details: Intel i7 2600k 8GB RAM Win7 x64

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