How to Reduce Trial Runs with CAE ？ - Kehui Mold Co., Limited

Trial runs in injection molding are costly and time-consuming, with traditional development often requiring 3-5 iterations to achieve stable production. Through precise CAE simulations (e.g., Moldflow), trial runs can be reduced to 1-2 attempts, or even achieve “first-time success.” Below is a systematic solution for you reference.

Core Benefits of CAE in Reducing Trial Runs

Traditional vs. CAE-Optimized Workflow Comparison

Stage	Traditional Method	CAE-Assisted Process	Cost/Time Savings
Design Validation	Relies on experience; issues found after mold fabrication	Virtual feasibility verification	50% fewer design changes
Process Tuning	Iterative parameter adjustments during trials	Parameter optimization via simulation	60% shorter
Defect Resolution	Post-trial mold repairs	Defect prediction and pre-optimization	30% lower mold

Key Savings

Mold modification costs: CAE identifies sink marks, warpage, etc., avoiding rework.
Material waste: Virtual trials reduce plastic consumption (critical for expensive engineering plastics).
Time savings: CAE analysis finishes 2-3 weeks earlier than physical trials in parallel engineering.

6 Key CAE Technologies to Minimize Trial Runs

1. Full-Process Simulation Coverage

Workflow: Product Design → Mold Flow Analysis → Filling/Packing/Cooling/Warpage → Defect Prediction → Optimization → Trial Validation
Critical Phases: Fill balance, Weld line locations, Cooling efficiency, Shrinkage/warpage

2. High-Precision Modeling: Mesh Requirements

3D Mesh: For thick-walled parts (≥3mm) or complex cooling analysis.
Dual-Domain Mesh: For thin-walled parts (≤2mm); faster computation.
Material Data: Use supplier-provided PVT and viscosity curves.

3. Defect Prediction & Optimization

Common Defects	CAE Prediction Method	Optimization Solutions
Short Shot	Fill time analysis, pressure curves	Add gates/adjust injection speed
Sink Marks	Volumetric shrinkage cloud maps	Optimize packing pressure/cooling
Warpage	Displacement analysis	Adjust fiber orientation/wall thickness
Weld Lines	Weld line location prediction	Modify gate/raise mold temp

4. Process Window Analysis

Design of Experiments (DOE): Simulate multi-parameter combinations (mold temp, melt temp, injection speed) to identify robust settings.

5 Mold System Co-Optimization

Cooling Design:

Conformal cooling improves efficiency by 30%.
Target cooling uniformity (Δt ≤10°C).
Runner Balancing: Adjust runner sizes for synchronized multi-cavity filling (≤5% Difference).

Virtual-Physical Data Alignment

Calibrate CAE models with actual trial data (injection pressure, temperature).
Fine-tune material parameters/boundary conditions (e.g., mold thermal conductivity).

Case Analysis

Case 1: First-Trial Success for Automotive Dashboard

Issue: Traditional methods required 3 trials to fix sink marks and warpage.

CAE Solution:

Predicted sink marks at rib roots (red zones).
Optimized packing (2-stage pressure + extended time).
Adjusted cooling layout (added channels near thick sections).

Result: Passed first trial, saving ＄ 20 K.

Case 2: Zero-Defect Production for Medical Thin-Wall Part

Challenge: 0.5mm wall thickness; 40% scrap rate traditionally.

CAE Solution:

Determined optimal injection speed via flow-front temp analysis.
Implemented valve gate sequencing for controlled filling.

Result: 1 trial success, yield increased to 98%.

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