Case study: how to achieve ultra-high accuracy from mould flow simulation

While flow simulation data can reduce the time until a tool is ready to produce samples, the stakeholders involved need to work centrally in unison, instead of sequentially.

That’s the view taken by Borealis, Oerlikon HRSflow and Engel, in a recent collaborative simulation, the results of which will be presented at K 2022.

Using a demanding mould, the three firms were able to show how injection moulding parameters can be optimised in the simulation and transferred directly to the machine as a proposed initial setting, provided that all relevant data of the injection moulding machine, the processed material as well as the hot runner system and other components are available.

The three firms say the end result was a highly effective and precise configuration – they will present the findings at K 2022.

The simulation was carried out on Moldflow, using a family mould with three cavities and a weight difference of 1:11 from the smallest to the largest component.

Engel provided a duo injection moulding machine, and supplied detailed technical intelligence from the start. For example, the group shared details of the geometries of the nozzle used, as well as the sim link data interface for the direct connection of Moldflow to the CC300 control unit of the injection moulding machine.

Borealis provided comprehensive, precise data on the flow behaviour of the Daplen EE001AI polypropylene, and Oerlikon HRSflow contributed a servo-driven 8-cavity hot runner system that allows the volume flow to be controlled, filling each cavity individually.

The three firms suggested that during mould configuration on projects with multiple stakeholders, the parties involved typically would not discuss possible causes of faults and remedial measures until after sampling has been completed.

By sharing as much data as possible up front, it was possible to optimise parameters like temperature control, injection characteristics and all other pressure and time-related injection moulding parameters.

The parameters determined in several iteration steps were then transferred to the machine via sim link. The data exchange between simulation software and injection moulding machine can take place in both directions. It thus also enables the analysis of process data, which in turn provides potential for process optimisation. Access to sensitive design data is not necessary.

The first production run demonstrated the high precision of the settings achieved via Moldflow. The filling behaviour and the positioning of the weld lines matched the simulation 100%. The warpage behaviour of the largest part, the door trim measuring around 600 mm x 400 mm, was predicted to within ±2 mm, and the dimensions across the diagonal of over 650 mm were maintained with maximum deviations of only 0.04 %. After a few optimisation steps and without major manual readjustment, all three moulded parts met the quality requirements.

This joint project has not only shown that real production has run almost identically to the simulation, but the result also opens up potential for more sustainable production. For example, it is possible to see in the design phase whether a machine is suitable for the intended product or whether energy can be saved and production efficiency increased by using a smaller machine. In addition, the setting data suggestion developed in the simulation reduces the number of necessary setting cycles. This leads to fewer rejects and lower energy consumption during sampling.

Markus Kralicek, Business Development Manager at Borealis, Michael Fischer, Head of Business Development Automotive Technologies at Engel, and Stephan Berz, Vice President Sales at Oerlikon HRSflow and General Manager DACH, report on the project in a free webinar which is available to view here: https://www.hrsflow.com/ww/en/events/borealis-engel-oerlikon-hrsflow-webinar.

In addition, all three will present the results during K 2022 at the Oerlikon HRSflow stand (Hall 1, Stand D10) on Thursday, 20.10.2022 at 4.30 p.m. and on Tuesday, 25.10.2022 at 4.00 p.m.