# A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing

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## Abstract

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## 1. Introduction

## 2. Methodology

#### 2.1. Design Approximations

#### 2.2. ML Approach

#### 2.3. Parameters Utilized

## 3. Results

#### 3.1. Assessment of ML Design Models

#### 3.2. Validation

#### 3.3. Combination of Models to Improve Results

#### 3.4. Algorithm Extension to Additive Manufacturing (AM)

## 4. Practical Application of TO and ML

## 5. Possible Future Work

## 6. Conclusions

- The work encompasses the development of code for training data generation, the utilization of software for topology optimization, and the implementation of an AI algorithm. Specifically, the adopted AI algorithm is a conditional generative adversarial network (cGAN) known as Pix2Pix. This cGAN was trained using pairs of data, where each pair comprises color-coded images containing the cantilever’s parameters and the corresponding topology-optimized target structures. Throughout this study, diverse models for generating topology-optimized cantilevers were conceived and subsequently investigated. Moreover, the analysis extended to exploring the relationship between the quantity of training data and the model’s accuracy.
- The developed model uses an ML-trained model to substitute or streamline the traditional iterative phases of the TO, which shortens the development cycle and decreases overall development costs.
- Parameters subject to parameterization in this study encompassed the load type, force application location, quantity of applied forces, force amplitude, direction of force application, dimensions of the construction space, and manufacturing constraints that specify that regions are to be preserved and free. Test data pairs that remained undisclosed to the algorithm were employed for validation purposes.
- The model displaying the highest accuracy is the third model for all potential variations. This model focuses on varying the parameters related to the positions of two applied forces along the opposite edge of the cantilever’s restraint. The varying parameters in the seventh model include a force that varies in location and direction and a color-coded rectangle that traverses the construction space and delineates areas where no material can be placed. This shows the robust nature of the trained algorithm. The combination of the model with sub-models is also a tested method to increase the accuracy of the results. Models 2, 3, and 4 with sub-models also prove the robust nature of the algorithm.
- There are several artefacts in the ML model compared to TO. Dependability of up to 91% was reached for artefact-free structures. However, the resulting artefact-filled pictures may be used extremely dependably as design templates. Additionally, Step 7, which was used to evaluate production restrictions, has always complied with and performed quite dependably with constraints.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 13.**Differences between models with different numbers of training data pairs for Step 7 (70–4800).

**Figure 14.**Generation of images from input data for which the model was not trained (deviating shapes).

Steps | Varying Parameters | Possible Variations | Number of Training Data | |

1 | Location of a point load Y+ (along the right outer edge). | 101 | 30 | |

2 | Location of a point load Y± (along the right outer edge). | 202 | 65 | |

3 | Location of two point loads X+Y+ (along the right outer edge). | 10,201 | 700 | |

4 | Location of two point loads X±Y± (along the right outer edge). | 40,804 | 1500 | |

5 | Location of two point loads X+Y± (Y± along the right outer edge) (X+ in the entire installation space). | 2,060,602 | 5000 | |

6 | Location and amplitude of a point load Y±. Location and length of two line loads X± Y± (Y± along the right outer edge) (line load X± vertical with length 5–30) (line load Y± horizontal with length). | 17.1 × 10^{9} | 10,000 | |

7 | Location of a point load Y± (along the right outer edge). Passive element (variable rectangle) free in installation space. | 303,126,250 | 4800 |

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## Share and Cite

**MDPI and ACS Style**

Ullah, A.; Asami, K.; Holtz, L.; Röver, T.; Azher, K.; Bartsch, K.; Emmelmann, C.
A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing. *J. Manuf. Mater. Process.* **2024**, *8*, 220.
https://doi.org/10.3390/jmmp8050220

**AMA Style**

Ullah A, Asami K, Holtz L, Röver T, Azher K, Bartsch K, Emmelmann C.
A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing. *Journal of Manufacturing and Materials Processing*. 2024; 8(5):220.
https://doi.org/10.3390/jmmp8050220

**Chicago/Turabian Style**

Ullah, Abid, Karim Asami, Lukas Holtz, Tim Röver, Kashif Azher, Katharina Bartsch, and Claus Emmelmann.
2024. "A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing" *Journal of Manufacturing and Materials Processing* 8, no. 5: 220.
https://doi.org/10.3390/jmmp8050220