# Geometric Design Process Automation with Artificial Intelligence

Jörg Brünnhäuser, Pascal Lünnemann, Ursina Bisang, Ruslan Novikov,
Florian Flachmeier, Mario Wolff

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Jörg Brünnhäuser, Pascal Lünnemann, Ursina Bisang, Ruslan Novikov, Florian Flachmeier, et al. Geometric Design Process Automation with Artificial Intelligence. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.35-42, 10.1007/978-3-031-16407-1_5. hal-05386008

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# Geometric Design Process Automation with Artificial Intelligence

Jörg Brünnhäußer<fnref n="1" />(✉), Pascal Lünnemann<fnref n="1" />, Ursina Bisang<fnref n="1" />, Ruslan Novikov<fnref n="1" />, Florian Flachmeier<fnref n="2" />, and Mario Wolff<fnref n="2" />

**Abstract.** Design tasks are largely performed manually by engineers, while machine learning is increasingly able to support and partially automate this process to save time or costs. The prerequisite for this is that the necessary data for training is available. This paper investigates whether it is possible to use data-driven methods to support the design of jounce bumpers at BASF. Based on the analysis of the use case, the geometry of the jounce bumper is approximated with a spline to generate suitable data for training. Based on this, data for training the machine learning model is generated and simulated. In the training process, the appropriate feedforward neural network and the best combination of hyperparameters are determined. In the subsequent evaluation process, it is shown that it is possible to predict the geometries of jounce bumpers with our proof of concept. Finally, the results are discussed, the limitations are shown and the next steps to further improve the results are reflected.

**Keywords:** Design automation · Machine learning · Data-driven design · Synthetic data

## 1 Introduction

Automation in engineering combined with continuous data management has led to a comprehensive stock of design-describing data in many companies over the years. Depending on the management, documentation and quality, these could be used as a basis for establishing data-driven algorithms for further automation and support in engineering. This paper discusses research on a case study concerning the potential of automated geometry generation using machine learning on this very data, and presents a possibility of using synthetic data for design automation of components of high geometric similarity and high variation in behavior.

The case study carried out and considered here addresses jounce bumpers (see Fig. 1). As an essential element in the suspension system, jounce bumpers, together with the coil spring and shock absorber, provide the non-linear behavior while protecting the shock

<footnote n="1">¹ Fraunhofer Institute for Production Systems and Design Technology, Pascalstraße 8-9, 10587 Berlin, Germany
joerg.bruennhaeusser@ipk.fraunhofer.de</footnote>

<footnote n="2">² BASF Polyurethanes GmbH, Elastogranstr. 60, 49448 Lemförde, Germany</footnote>
absorber from damage under high loads. The design of jounce bumpers Lemförde, is very complex and time-consuming. The development process of the jounce bumper is integrated into the product development processes of the automotive OEM. Starting from the vehicle design, the BASF engineering department receives a description of the jounce bumper to be developed, including the boundary conditions and costs to be considered. Essential data are the bump behavior to be mapped (e.g. on the basis of a load-deflection curve), as well as the limiting installation space. The designs can be continuously and automatically loaded, parameterized in the CAD environment and then subsequently simulated.

Within the framework of this procedure, numerous product-describing data in relation to the bump behavior have been created over the years. This information could therefore contain comprehensive knowledge from which machine learning (ML) models for assisting or improving the design process can be derived. One possibility to do this would be to train a machine learning model to imitate the simulation to speed up validation or train another model to generate suitable designs, based on these validations. The approach we chose is to teach a model to generate suitable designs from simulated requirements. This decision was made as the current simulation works well even though not fast enough to be used as objective function in model training, it fulfils its current task and anecdotal evidence implies that is very hard to imitate

the simulation. In the following, reflecting on the case study, the method of a multivariate regression in the form of a feedforward neural network is presented based on the literature review. Following the implementation of this method, findings are explained and discussed.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Profile of a jounce bumper.</figcaption></figure>

## 2 Literature Review

The potential for using artificial intelligence for design automation is widely known. In particular, due to the increasing variability of products as a result of higher individualization [1, 2]. At the most basic level, a distinction can be made between approaches that integrate simulations into the automation of the design [3] (for example, generative design approaches) and those that use the results of representative simulations to generate substitute models [2]. Using generative design, unlike when using machine learning more constraints and rules have to be defined. Krish [4] showed that a generative design approach is suitable for complex multi-criteria design problems. The paper proposed a method that starts with a CAD genotype and varies it randomly within specific limits. After that the designs are filtered and the resulting designs can be adapted by an engineer.

In contrast, artificial intelligence methods do not need the manual setup of many specific rules because they try to learn those rules implicitly from data. Data-driven services are becoming more and more important due to their ability to make correct predictions in very complex situations where deterministic systems would be either very difficult to develop or very time consuming in generating results. Shah et. al. For instance are using a multi-target regression model to extract the parameters of a single-diode model of photovoltaic modules [5]. The model accelerates the performance evaluation
of photovoltaic modules. This work uses basic machine learning algorithms like SVM or random forest instead of neural networks.

Another approach combines generative exploration and machine learning: in Oh et. al. [6] generative adversarial networks are used to create different optimized design options. Then, anomaly detection can be used to identify new designs. This helps the designer to spot relevant results. Jang et. al. [7] propose reinforcement learning to get a high amount of diverse topology designs. On the downside, the computational effort of this approach seems to be high. Multioutput regression is a supervised learning technique that is used to learn a function to predict multiple connected output values from a single input [8]. Multioutput regression can be used for different things, Shah et. al. [5] uses it to extract the parameters of photovoltaic cells and we use it to generate jounce bumper designs. The use of simulations for digital mock-ups is common when designing products as [9] show in their literature review, this is the original purpose of the BASF tool used during the data creation step, but we employ it differently from the original purpose to generate our training data.

## 3 Method

Our approach was based on the CRISP-DM [10]. The underlying user story is about specifying the requirements of a bumper and getting an improved design as a result. Next, we analyzed the data with the subject matter experts. An available database contains all the designs simulated so far. It contains about 14.000 designs with basic information such as density, dimensions and block height, load-deflection curves calculated from the simulation stored and about 400 optional parameters to describe the bumper. Figure 2 shows the basic concept for solving the user story. The requirements, which include the desired force-displacement curve, are the input for a machine learning model. The model is to predict a suitable new shock absorber geometry, which is the output. Analysis of the data revealed that the complex form of the geometric description by 400 - often optional and therefore empty - parameters are not ideal for training a machine learning model.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Concept to predict and evaluate jounce bumper geometries.</figcaption></figure>

One possible solution is to use a simplified model to describe the geometry of the shock absorber, which has significantly fewer parameters. However, it is very difficult to map the relation between the simplified geometry and the original geometry. To solve this problem and to make an estimation of the usability in ML models beforehand, a geometry model of a specific jounce bumper was created based on two splines (inner and outer geometry of the rotationally symmetric part) and these were varied in a regular way to create many different designs for the training process. The generated designs were then simulated to determine their load behavior. This procedure resulted in a synthetic
data set, which, however, does not take the existing data into account. In addition to the geometry-describing data and behavioral data from the simulation, the consideration of the requirements is particularly relevant for an application in the design process. The requirements describe the necessary load-deflection curves as well as geometric limitations of the available installation space. These data result from the simulation and can be derived from the geometry-describing data. Thus, there is a complete description system of the geometric parameters with associated load-deflection curves. Based on this data, a feedforward neural network was trained, which attempts to learn the relationship between requirements and suitable geometries. In the final step, the evaluation of the model's predictions was carried out. Besides the basic evaluation in terms of mathematical metrics, such as the squared error, different load-deflection curves of generated bumpers were compared with those from the requirements.

## 3.1 Generation and Variation of Spline-Based Jounce Bumper Geometry

The geometry variations were generated by repeatedly varying specified measurement variables. The resulting permutations of the template geometry formed the basis of the simulation data used in our machine learning process. The template geometry used, was a custom-build spline-based jounce bumper in STP format with the splines being second order 'B-spline-curves with knots'.

The spline curve as well as other measurement variables were read and processed by a custom Python script. The processed data points were set in relation to define thresholds and ranges within which the variation could be performed. We minimized the amount of potentially unstable geometries as far as possible, by limiting the range in which the measurement variables of the template geometry could be modified. However, stricter thresholds may lead to variations with features too similar, which in turn could negatively impact the performance of the machine learning process. Each change in the spline curve and geometry was performed using a modifiable step range and increment range using simple script functions. For the initial test, about 18000 geometries were generated. The outputs are STP geometry files that are used in simulation stages to calculate the load-deflection curves for the requirements used in the machine learning process.

## 3.2 Data Preparation and Training of Machine Learning Model

After the splines were varied as described above and simulated, the corresponding requirements were generated with a Python script: For the measurements of the piston rod, cup and striker, the measurements of the attachment parts used for the simulation were taken as requirements. To describe the load-deflection curve, 6 points were taken from the load distribution in regular intervals, from these a polynomial of the 5th grade was calculated. Although it does not describe the load-deflection curve perfectly, the level of detail is sufficient for a proof of concept.

As inputs of the machine learning model, we used the height of the jounce bumper, the size of the cup, the striker and the piston rod, as well as the coefficients of the polynomial of the load-deflection curve and the block height (minimum height to which a jounce bumper can be compressed to). Labels are the x- and y-coordinates for 10 points
of each of the two splines which describe the inner and outer geometry, as well as the
density the generated jounce bumper should have. Both the input and the labels for the
training data were standardised to have mean zero and a standard deviation of one.

To predict the multiple spline points as well as the density of the jounce bumper
that is described by the splines, multi-output regression was determined to be the most
suitable approach. A feedforward neural network was chosen. The model was trained on
70% of the generated data using Adam [11] as an optimisation algorithm and the mean
squared error (MSE) as loss. The remaining data was used to track the training process
and to test the model on data not seen during training.

To find a good size for the hidden layers as well as learning rate, the dropout rate and the batch size, hyperparameter tuning was done. We specifically used an evolutionary approach to find the most suitable hyperparameters<fnref n="1" />. The best performing neural network had four fully connected hidden layers and a Dropout-layer after every fully connected layer. The activation function of the hidden layers was ReLU [12] and for the final layer we didn't use any activation function. The data preparation and the training of the neural network was implemented in several Jupyter notebooks. The PyTorch framework [13] was used for the training process and the model is compact enough to train it on a personal computer.

4 Results and Discussion

To evaluate the machine learning, the MSE (prediction-label-relation) and R<fnref n="2" /> (fitting-quality) were evaluated. In addition, 10 jounce bumpers that were generated from the test data (not used during training) were randomly chosen and their behavior was simulated. Furthermore, one actual requirement was fed to the model and the resulting jounce bumper was simulated, too.

The generated jounce bumpers were evaluated for two qualities: Behavior in the simulation and fit to the requirements. Although the general deformation behavior was acceptable to good in most cases, the prediction of the initial stiffness by splines showed a design weakness. These splines predicted a much higher initial stiffness than the requirements as well as the later simulation. Also, the adherence to the design space must be ensured, as deviation will result in a lack of confidence in the method.

Figure 3 shows the difference between requirements and the jounce bumpers gen-
erated according to them. Large differences in height were caused by the polynomial
curve describing the load-deflection curve having a wrong height at the y-intercept
when compared to the original load-deflection curve<fnref n="2" />. The differences for inner and
outer radius will have to be investigated further to remedy all differences in geometry
between requirement and output in future iterations.

<footnote n="1">¹ The chosen hyperparameters are 18 neurons in the first hidden layer, 33 neurons in the second one, 40 in the third one and 78 in the fourth and final hidden layer. The learning rate is 0.0073, dropout rate 0.057 and the batch size 512, we trained the model for 900 epochs and chose to use a version saved at epoch 860, as we found it has the best validation performance of the epochs we trained to.</footnote>

<footnote n="2">² The model does not optimize in this case for the actual height, but for the height of the polynomial at the y-intercept, which should be identical to the height of the jounce bumper as this point describes the height of the jounce bumper when no load is applied.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Deviation from the requirements to the predicted jounce bumper geometry.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** A load-deflection curve where the requirements are not met (ID: 1488)</figcaption></figure>

Figure 4 contain examples for the load-deflection curves simulated for two jounce bumpers<fnref n="3" />.

The curves show that the model is able to generate jounce bumpers that will have a similar load-deflection curve to the polynomial requirement curve, especially at higher loads. For lower loads the jounce bumper's height tends to decrease more rapidly than that of the polynomial. In this simulation of the jounce bumper and the simulation that supplied the green requirements curve are more similar and the early flattening of compression in the simulated curves seems to be one behavior of the load-deflection curves that the polynomial curves cannot replicate well. Still, it is encouraging that the model is able to learn the supplied curve in some cases, as shown in Fig. 3. Figure 3 also illustrates the case stated above that the model conforms to the height implied by the polynomial, not the required height, when it generates a jounce bumper. Furthermore, the jounce bumper in Fig. 4 would not be chosen by an engineer, because the area between the actual requirement curve and the simulated one is too large and therefore the generated jounce bumper does not fulfill the actual design goal on the most important criterion.

Based on the results it seems possible to predict jounce bumper geometries from requirements with ML and specifically with a feedforward neural network and the approach of using splines to approximate the geometry of jounce bumpers.

The measured MSE is low on the test data, indicating that the requirement load-deflection curves are fairly closely followed, as are the geometry requirements. Further investigation indicates that the model is only able to make good predictions because it meets the training label, which was the approximated load-deflection curves. But those curves are not very close to the original ones. Therefore, the approximation of the requirements with a polynomial curve seems to be a critical problem of this approach. Another challenge will certainly be the provision of training data for realistic applications, since in this case, for example, the add-on parts were varied only minimally and the installation

<footnote n="3">³ The green curve describes the original requirement, based on the simulation of the jounce bumpers generated as described in Sect. 3.1, the orange curve shows the polynomial approximating the original requirement and the blue curve describes the load-deflection curve that was simulated for the jounce bumper generated by the machine learning model.</footnote>
space was also varied only in rough steps. However, as long as the simulation cannot be
significantly accelerated, the provision of large training data is difficult.

**5 Conclusion**

The aim of the case study presented here was to investigate whether machine learning methods can be applied to the BASF process of designing jounce bumpers based on historical design data. To do this, we identified a user story and created a concept for training an ML model. The available data and the related model were not transferable in their complexity to a representative, general geometry model. The development of an alternative spline-based geometry model and the generation of synthetic data of the spring behavior led to first insights, though. The sample showed a reliable correlation of the load-deflection curve, which, however, cannot be sufficiently approximated by a polynomial function to the nominal parametric.

The reason for this seems to be the approach of the representation of the expected behavior where alternative substitute functions are used in further research. Further potential obviously results from the consideration of further test data. In addition, it will be examined whether the synthetic data can be created more comprehensively if a substitute model is used instead of a simulation.

The approach followed here addresses products and components that are always very similar in their basic structure, but very different in their individual functional requirements. On the one hand, this enables the design of a controllable geometry model and, on the other hand, the prediction of the functional behavior in alternative geometries. The gathered evidence suggest that intelligent models can be created based on synthetic data. This approach is not applicable to components that vary greatly in their geometric structure. Here, approaches that directly integrate FEM simulations seem more promising.

Overall, the study shows that it is not always possible to use existing development data for the qualification of AI algorithms, but that artificial data can be used instead. In general, however, this shows the potential for supporting engineering.

**References**

1. Papakostas, N., Ramasubramanian, A.K.: Digital technologies as a solution to complexity caused by mass personalization. In: Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology, pp. 153–180. Elsevier (2022)

2. Papakostas, N., Ramasubramanian, A.K.: Chapter 6 - Digital technologies as a solution to complexity caused by mass personalization. In: Mourtzis D. (ed.) Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology, pp. 153–180. Elsevier (2022)

3. Mourtzis, D., Angelopoulos, J., Panopoulos, N.: Design of an intelligent robotic end effector based on topology optimization in the concept of industry 4.0. In: Andersen, A.-L., et al. (eds.) CARV/MCPC -2021. LNME, pp. 182–189. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-90700-6_20

4. Krish, S.: A practical generative design method. Comput. Aided Des. 43, 88–100 (2011). https://doi.org/10.1016/j.cad.2010.09.009
5. Shah, R.: Solar cell parameters extraction using multi-target regression methods. In: Leonow-icz, Z. (ed.) Conference proceedings 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), 9–12 June, 2020, Madrid, Spain: The 2020 Edition Will be Held on Scheduled Days From 09th to 12th June 2020 in Web Streaming, pp. 1–6. IEEE, Piscataway (2020)

6. Oh, S., Jung, Y., Kim, S., et al.: Deep generative design: integration of topology optimization and generative models. J. Mech. Des. **141** (2019). https://doi.org/10.1115/1.4044229

7. Jang, S., Yoo, S., Kang, N.: Generative design by reinforcement learning: enhancing the diversity of topology optimization designs (2020)

8. Borchani, H., Varando, G., Bielza, C., et al.: A survey on multi-output regression. WIREs Data Min. Knowl. Disc. **5**, 216–233 (2015). https://doi.org/10.1002/widm.1157

9. Mourtzis, D.: Simulation in the design and operation of manufacturing systems: state of the art and new trends. Int. J. Prod. Res. **58**, 1927–1949 (2020). https://doi.org/10.1080/00207543.2019.1636321

10. Wirth, R., Hipp, J.: Crisp-dm: towards a standard process modell for data mining (2000)

11. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)

12. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Fürnkranz, J., Joachims T. (eds.) Proceedings of the 27th International Conference on Machine Learning (ICML-10), 21–24 June 2010, pp. 807–814. Omnipress, Haifa (2010)

13. Paszke, A., Gross, S., Massa, F., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., et al. (eds.) Advances in Neural Information Processing Systems, vol 32. Curran Associates, Inc. (2019)