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Machine learning aided design of conformal cooling channels for injection molding

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Abstract

During the injection molding process, the cooling process represents the largest portion of the cycle time. The effectiveness of the cooling system significantly affects the production efficiency and part quality, where it is limited by the conventional cooling channels manufactured by the drilling and casting process. Although the maturing advanced additive manufacturing (AM) technology allows the design and fabrication of complex conformal cooling channels, the temperature variance caused by non-uniform thickness distribution of the part remains unsolved. This issue is caused by the fact that the existing conformal cooling designs do not create the channels conformal to the part thickness distributions. In this work, a machine learning aided design method is proposed to generate cooling systems which conform not only to the part surface but also to the part thickness values. Three commonly used conformal cooling channel topologies including spiral, zig-zag, and porous are selected. A surrogate model is derived for each cooling channel topology to approximate the relationship between the design parameters of the cooling channels, part thickness, and the resulting part surface temperature. Based on the surrogate model, the design parameters of each type of cooling channels are optimized to minimize the part surface temperature variation. At the end of the paper, design cases are studied to validate the effectiveness of the proposed method. Based on the proposed method, much lower temperature variance and a smaller coolant pressure drop are achieved compared with the conventional conformal cooling design.

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Abbreviations

MLACCD:

ML-aided conformal cooling design

TVM:

Temperature variance minimization

SMLTM :

Supervised ML temperature model

l p :

Half of the part thickness

Re:

Reynolds number

d :

Cooling channel diameter

W :

Cooling channel pitch width

l m :

Cooling channel pitch to mold surface distance

\(\phi\) :

Mold porosity

\(\overrightarrow {CEV}\) :

Cooling efficiency variation direction

\(\vec{c}\) :

Cooling channel distribution direction

WOCP :

\(W\)—Optimized control points

\(\Delta \theta\) :

Resolution angle

\(l_{i}\) :

\(i^{th}\) Control line

\(p_{0}\) :

Point with maximum divergence

\(p_{i}^{\left( j \right)}\) :

\(j^{th}\) Point on \(i^{th}\) control line

\(L\left( i \right)\) :

Set of points on \(i^{th}\) control line

\(T_{g}\) :

Target temperature

\(T_{i}^{{\left( {j,k} \right)}}\) :

Temperature on the cooling surface between \(p_{i}^{\left( j \right)}\) and \(p_{i}^{\left( k \right)}\)

\(d_{i}^{{\left( {j,k} \right)}}\) :

Distance between \(p_{i}^{\left( j \right)}\) and \(p_{i}^{\left( k \right)}\)

\(\Delta h_{i}^{j}\) :

Additional height added to the distance from \(p_{i}^{\left( j \right)}\) to the cooling surface during the second optimization step

\(\Delta w\) :

Resolution distance

\(d_{e}\) :

Edge distance

MAX:

Maximum

AVG:

Average

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Funding

This research work is supported by NSERC Strategic Network for Holistic Innovation in Additive Manufacturing (HI-AM) Grant No. NETGP 494158–16.

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Authors and Affiliations

  1. Department of Mechanical Engineering, McGill University, Montreal, QC, Canada

    Zhenyang Gao & Yaoyao Fiona Zhao

  2. Department of Mechanical Engineering, University of Colorado Denver, Denver, CO, USA

    Guoying Dong

  3. Mechanical & Aerospace Engineering, Monash University, Clayton, VIC, Australia

    Yunlong Tang

Authors
  1. Zhenyang Gao
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  2. Guoying Dong
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  3. Yunlong Tang
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  4. Yaoyao Fiona Zhao
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Contributions

Z.G conceived the presented idea, developed the methodology, and drafted the manuscript. G.D developed the machine learning model, revised the manuscript. Y.T conceived the presented idea and designed the cooling channel topologies. Y.F.Z revised the manuscript. All the authors discussed the result and contributed to the final manuscript.

Corresponding authors

Correspondence to Guoying Dong or Yaoyao Fiona Zhao.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Gao, Z., Dong, G., Tang, Y. et al. Machine learning aided design of conformal cooling channels for injection molding. J Intell Manuf 34, 1183–1201 (2023). https://doi.org/10.1007/s10845-021-01841-9

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  • DOI: https://doi.org/10.1007/s10845-021-01841-9

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