CAE Thermal-Fluid Analysis: Predicting Mold Temperature Rise ~Visualising Temperature Distribution and Optimising Design Using CAE~

In injection molding and thermoforming, “mold temperature management” is a critically important factor determining product quality. Uneven temperature distribution can cause warping, sink marks, weld line defects, and other issues, making stable mass production difficult.

However, in actual molding operations, it is difficult to directly monitor temperature changes within the mold. Consequently, temperature prediction technology using thermal-fluid analysis via CAE (Computer Aided Engineering) has gained significant attention in recent years.

This article explains the concept of mold temperature prediction through thermal-fluid analysis and its benefits.

■ What is Thermal-Fluid Analysis?

Thermal-fluid analysis is a numerical method for analysing the flow of fluids (air, steam, resin, etc.) and the associated heat transfer. In the molding field, it enables simultaneous evaluation of phenomena such as:

Fluid flow (velocity, pressure)

Heat transfer (conduction, convection)

Temporal changes in temperature distribution

This analysis evaluates changes in mold temperature over time by flowing superheated steam at 400°C through the mold cavity.

■ Overview of the Analysis Model

The subject of analysis is a 250mm square mold insert model featuring a structure with steam inlet and outlet ports. Its distinctive characteristics are as follows:

• Comparison of two patterns with modified steam inlet shapes (nozzle section)

  

• Porous structure configured within flow channel

  
  

  Inflow conditions:

  Pressure: 0.1013 MPa

  Flow velocity: 8 m/s

  Temperature: 400°C

  
  

  By conducting analysis in an environment simulating actual molding conditions, behaviour closely resembling that of the actual machine can be reproduced.

  
  

  ■ Differences in temperature distribution due to shape modification

  
  

  Analysis results clearly demonstrate that variations in steam inlet geometry significantly influence mold temperature distribution.

  
  

  Specifically, the following relationship has been confirmed:

  - Flow patterns change depending on flow path geometry

  - Differences in velocity distribution affect heat transfer efficiency

  - Consequently, mold surface temperatures vary

Furthermore, this analysis is a transient analysis (time-dependent analysis), evaluating the temperature distribution changes over a 600-second period of steam flow.

Crucially, this means it captures not only the “final temperature” but also the process of “how the temperature rises”.

■ Benefits of Utilising CAE

Employing computational fluid dynamics (CAE) enables the acquisition of extensive information previously unattainable through conventional prototyping and on-site adjustments. The primary benefits are as follows:

① Visualisation of temperature distribution within the mold

Temperatures within the mold cavity, difficult to measure in actual equipment, can be understood as surface distributions using CAE.

Identification of high-temperature and low-temperature zones

Visualisation of temperature variations

Detection of hotspots

This enables prediction of issues during the design phase.

② Pre-evaluation of the impact of shape modifications

As demonstrated in this analysis, altering the inlet shape alone can significantly change the temperature distribution.

Using CAE allows for design considerations such as:

Optimisation of flow path design

Improvement of cooling/heating efficiency

Reduction of molding defect risks

to be conducted without prototyping.

③ Understanding behaviour including time variations

Transient analysis enables evaluation of:

Heating ramp-up time

Time to temperature stabilisation

Transient temperature variations

This is highly effective for cycle time reduction and mass production stability studies.

④ Reduction in Prototyping Iterations and Cost Savings

Traditionally, the process required:

‘Prototype → Evaluation → Modification → Re-prototype’

However, utilising CAE enables:

Narrowing down optimal designs before prototyping

Reducing rework

Shortening development periods

⑤ Accumulation and standardisation of design know-how

As CAE results can be stored as data, this facilitates:

Reuse of successful cases

Clarification of design rules

Prevention of reliance on individual expertise

■ Future prospects: Integration with machine learning

This document also addresses ‘thermal-fluid analysis using machine learning’, with the following developments anticipated:

Automatic proposal of optimal shapes based on past analysis results

Significant reduction in calculation time

Real-time optimisation

In essence, CAE has the potential to evolve from an “analysis tool” into a “design support AI”.

■ Summary

Utilising thermal-fluid analysis enables the prediction of mold temperature rise and distribution in advance.

Particularly important points are as follows:

Differences in flow path geometry significantly impact temperature distribution

Transient analysis enables understanding of temporal changes

CAE enables optimal design prior to prototyping

This delivers numerous benefits: improved quality, cost reduction, and shortened development cycles.

Future integration with machine learning will realise even more advanced design support, fundamentally transforming the approach to mold design itself.