Reliability based casting process design optimisation

Abstract Deterministic optimum designs are unreliable without consideration of the statistical and physical uncertainties in the casting process. In the present research, casting simulation is integrated with a general purpose reliability based design optimisation (RBDO) software tool that considers uncertainties in both the input variables as well as in the model itself. The output consists of an optimal design that meets a specified reliability. An example casting process design is presented where the shape of a riser is optimised while considering uncertainties in the fill level and riser diameter. It is shown that RBDO provides a much different optimum design than a traditional deterministic approach. The deterministic optimal solution offers a 12% increase in casting yield over typical safety margin design practice, but has an unacceptable 61% probability of failure. The RBDO design has a 7% increase in casting yield over the safety margin approach and a probability of failure of 4·6%.

[1]  Lihui Wang,et al.  Multi-objective Evolutionary Optimisation for Product Design and Manufacturing , 2011 .

[2]  K. Choi,et al.  Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables , 2011, DAC 2010.

[3]  D-M Liao,et al.  Casting CAD/CAE automatic optimal riser design technology , 2011 .

[4]  Hyunkyoo Cho,et al.  Reliability-based design optimization of fluid–solid interaction problems , 2014 .

[5]  Daniel A. Tortorelli,et al.  Optimal riser design for metal castings , 1995 .

[6]  Heung-Geun Kim,et al.  Optimization of a SMES Magnet in the Presence of Uncertainty Utilizing Sampling-based Reliability Analysis , 2014 .

[7]  Ingo Hahn,et al.  THE CLASSIC OPTIMIZATION OF THE CASTING PROCESS – THE " ONE-DIMENSIONAL SEARCH , 2010 .

[8]  Xiang Chen,et al.  Casting Design Through Multi-Objective Optimization , 2011 .

[9]  Kyung K. Choi,et al.  Metamodeling Method Using Dynamic Kriging for Design Optimization , 2011 .

[10]  Kyung K. Choi,et al.  Sampling-Based RBDO of Ship Hull Structures Considering Thermo-Elasto-Plastic Residual Deformation , 2015 .

[11]  Jasbir S. Arora.  Chapter 4 – Optimum Design Concepts: Optimality Conditions , 2012 .

[12]  M. Sutaria,et al.  Automatic Optimization of Casting Feeders Using Feed-Paths Generated by VEM , 2011 .

[13]  Bhallamudi Ravi,et al.  Casting Simulation and Optimisation: Benefits, Bottlenecks, and Best Practices , 2008 .

[14]  Enrico Rigoni,et al.  NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations , 2005, Practical Approaches to Multi-Objective Optimization.

[15]  William Pao,et al.  Computer implementation of Heuvers' circle method for thermal optimisation in castings , 2005 .

[16]  Cem Celal Tutum,et al.  State-of-the-Art Multi-Objective Optimisation of Manufacturing Processes Based on Thermo-Mechanical Simulations , 2011, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing.

[17]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[18]  Garret N. Vanderplaats,et al.  Numerical Optimization Techniques for Engineering Design: With Applications , 1984 .

[19]  K. Choi,et al.  Sampling-based RBDO using the stochastic sensitivity analysis and Dynamic Kriging method , 2011 .

[20]  K. Choi,et al.  Sensitivity analyses of FORM‐based and DRM‐based performance measure approach (PMA) for reliability‐based design optimization (RBDO) , 2010 .

[21]  Jean Kor,et al.  Casting Design through Multi-objective Optimization , 2009, 2009 Second International Conference on Future Information Technology and Management Engineering.