A Warpage Optimization Method for Injection Molding Using Artificial Neural Network Combined Weighted Expected Improvement
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Abstract
A surrogate-based warpage optimization for injection molding is proposed in this study. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters, i.e., the mold temperature, melt temperature, injection time, packing time, packing pressure, and cooling time are the design variables. The warpage values are reduced by optimizing the process parameters. A new optimization iteration scheme based on artificial neural network (ANN) combined weighted expected improvement (WEI) is employed to speed up the optimization process and to ensure very rapid and steady convergence. The ANN is used to build an surrogate warpage function for the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is executed by the WEI function, which is an infilling sampling criterion. Although the design of experiment (DOE) size is small, this criterion can precisely balance the local and global search and tend to find the global optimal design. As examples, a TV cover and a scanner are investigated. The results show that the proposed approach can effectively reduce the warpage of the injection molding parts.
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© 2012, Carl Hanser Verlag, Munich
Articles in the same Issue
- Contents
- Contents
- Review Papers
- Rheo-chemistry in Reactive Processing of Polyolefin
- Regular Contributed Articles
- Visualization Analysis of a Multilayer Foam Development Process in Microcellular Injection Molding
- Visualization Analysis of Resin Flow Behavior around a Flow Front Using a Rotary Runner Exchange System
- Influence of the Calendering Step on the Adhesion Properties of Coextruded Structures
- The Effect of Silane Treated Hybrid Filler on the Mechanical and Thermal Performance of Carboxylated Nitrile Butadiene Rubber (XNBR) Composites
- Bioplastics from Blends of Cassava and Rice Flours: The Effect of Blend Composition
- A Warpage Optimization Method for Injection Molding Using Artificial Neural Network Combined Weighted Expected Improvement
- Bi-axially Oriented Blown Film Technology
- Stretch-Blow Molding of PET Copolymers – Influence of Molecular Architecture
- Rotational Molding of Polyamide-6 Nanocomposites with Improved Flame Retardancy
- Effect of Blending Protocol on the Rheological Properties and Morphology of HDPE/LLDPE Blend-based Nanocomposites
- High-Strength PET Fibers Produced by Conjugated Melt Spinning and Laser Drawing
- Effect of Blend Ratio of h-LLDPE and LDPE on Tear Properties of Blown Films
- PPS-News
- PPS News
- Seikei Kakou Abstracts
- Seikei-Kakou Abstracts
Articles in the same Issue
- Contents
- Contents
- Review Papers
- Rheo-chemistry in Reactive Processing of Polyolefin
- Regular Contributed Articles
- Visualization Analysis of a Multilayer Foam Development Process in Microcellular Injection Molding
- Visualization Analysis of Resin Flow Behavior around a Flow Front Using a Rotary Runner Exchange System
- Influence of the Calendering Step on the Adhesion Properties of Coextruded Structures
- The Effect of Silane Treated Hybrid Filler on the Mechanical and Thermal Performance of Carboxylated Nitrile Butadiene Rubber (XNBR) Composites
- Bioplastics from Blends of Cassava and Rice Flours: The Effect of Blend Composition
- A Warpage Optimization Method for Injection Molding Using Artificial Neural Network Combined Weighted Expected Improvement
- Bi-axially Oriented Blown Film Technology
- Stretch-Blow Molding of PET Copolymers – Influence of Molecular Architecture
- Rotational Molding of Polyamide-6 Nanocomposites with Improved Flame Retardancy
- Effect of Blending Protocol on the Rheological Properties and Morphology of HDPE/LLDPE Blend-based Nanocomposites
- High-Strength PET Fibers Produced by Conjugated Melt Spinning and Laser Drawing
- Effect of Blend Ratio of h-LLDPE and LDPE on Tear Properties of Blown Films
- PPS-News
- PPS News
- Seikei Kakou Abstracts
- Seikei-Kakou Abstracts