The integration of Artificial Intelligence (AI) techniques holds promise for advancing the optimization of industrial processes, such as the use of Cold Spray (CS) in the field of Additive Manufacturing (AM). This paper explores the intersection of AI and Cold Spray technology, highlighting its potential to enhance various aspects of AM, including material deposition, surface properties, and process eficiency. Through the utilization of Machine Learning (ML) and Deep Learning (DL) techniques, AI facilitates the analysis of vast datasets encompassing parameters such as powder properties, substrate characteristics, and process conditions, thereby enabling the identification of optimal deposition strategies. Furthermore, AI-driven predictive models ofer insights into the complex interactions between process variables, leading to improved understanding and control of the CS process.
eras embedded within AM systems, enabling real-time substrate, thereby ensuring the integrity of the coating
monitoring and proactive adjustments. Specifically, the [
The primary objective of our research is to design an The distance between the CS nozzle and the substrate,
innovative methodology for computer-aided manufactur- known as the standof distance, afects particle
velocing (CAM) systems by leveraging ML and DL approaches ity and impact energy. Adjusting this parameter
optito develop ad-hoc models. These models attempt to use mizes coating thickness, uniformity, and surface finish
cutting-edge technology to discover the best combina- [
These activities involve a multidisciplinary team from els comes into play. By leveraging AI algorithms and the University of Salerno and the University of Naples machine learning techniques, manufacturers can analyze Federico II. The CAIS Lab 1, laboratory from Computer the amounts of data to identify the most efective parameScience Department of the University of Salerno, provides ter settings more quickly and accurately than traditional important support in developing algorithms that require methods. This not only streamlines the optimization prosignificant time and resources. Meanwhile, the University cess but also enhances the overall eficiency of the cold of Naples Federico II focuses its eforts on the thorough spray deposition, leading to significant time and cost savcollection and organization of data. ings while maintaining or even improving the quality of the final product.
CS is an emerging technology for micrometer-sized powder deposition, increasingly utilized in additive manufacturing for creating individual components and repairing damaged parts. Among the advantages of CS is the fact that it mitigates thermal degradation and facilitates eficient deposition between the sprayed material and the
CS, utilizing kinetic energy and operating at temperatures well below the melting point of metallic particles, presents a promising avenue for enhancing the surface properties of polymers. While extensively explored and utilized on metal substrates, the application of this technology to polymers remains relatively uncharted territory, and the underlying physics are not yet fully elucidated. This is particularly significant because the charac- The output parameters under consideration are the teristics of the final coating, such as powder deformation penetration depth of the particle and the degree of flator penetration depth, crucial for coating adhesion, are tening. In CS, penetration depth refers to the distance influenced by a multitude of factors. These factors in- into the substrate material that the sprayed particles can clude the properties of both the metallic powder and penetrate and adhere to. This depth depends on various the polymeric substrates, alongside the specific spray- factors such as particle velocity, temperature, and maing parameters employed in the process. Consequently, terial properties. Flattening, on the other hand, refers accurately predicting the behavior of metallic particles to the deformation of the sprayed particles upon impact upon impact on various substrates remains a challenge. with the substrate surface. It describes how much the Developing a physical model capable of accurately rep- particles spread out and flatten upon hitting the substrate. resenting the deposition processes of metallic particles Flattening is influenced by parameters like particle veonto nonmetallic substrates proved impractical. Validat- locity, temperature, particle size, and substrate material ing such models would require the collection of extensive properties. testing scenarios and outcomes using advanced tools, in- We employed three distinct ML models: Linear Regrescluding sensors and high-speed cameras. sion (LR), Gaussian Process Regression (GPR), and Neural
In this context, Machine Learning (ML) ofers the po- Networks (NNs). LR is a straightforward technique
utitential to reduce the number of necessary experimental lized to establish a linear relationship between variables.
trials. However, when we used ML solutions, achieving This relationship elucidates the functional link between
precise predictions requires feeding the model with ac- the independent and dependent variables within a given
curate and a large amount of data. Therefore, a viable dataset. Consequently, LR models the unknown or
deapproach could involve training the model with a com- pendent variable as a linear equation based on the known
bination of precise yet limited experimental data and or independent variable. Gaussian Process Regression
computational data obtained from Finite Element models (GPR), on the other hand, is a nonparametric Bayesian
(FEM), which, while less precise, do not sufer from the approach employed for regression tasks. It excels
parlimitations of experimental data. Consequently, in our ticularly with smaller datasets and infers a probability
work [
The input parameters for the strategies employed can etration values while exhibiting a decrease in flattening be categorized into three main groups: performance. Figure 3 depicted the comparison between the performance of the models. • impact velocity, which encompasses other process The conducted experiments indicate that the accuracy parameters such as temperature and pressure; of the models improves with an increasing amount of • powder parameters (Yp), representing the yield available data. Specifically, the models demonstrate betstrength of the powder material; ter fit to the data when trained on the mixed dataset. By • substrate parameters (Ys), indicating the yield ofering an initial insight into the influence of parameters strength of the substrate material. afecting coating deposition, the integration of ML seems to contribute to the optimization of the CS process.
evolutionary approaches to improve
CS across a spectrum of materials and to explore the the
use of AI models, our attention focused on the integration
of DL models. Through the integration of DL models,
we aim to unlock new insights and capabilities that
propel the field of CS towards greater eficiency, eficacy,
and versatility. In this study [
In this scenario, when evaluating potential substrate
materials, our study examined a range of options
including ABS, PEEK, and Polyamide PA66 (PA66).
Furthermore, in selecting powders for the deposition process, we
incorporated a variety of metallic options such as copper,
aluminum, steel, and titanium. During the initial training
phase, we employed two distinct types of datasets to
develop our models. The first dataset, known as the mixed
dataset, comprised a blend of 30% experimental data and
70% FEM data. Additionally, we utilized a second dataset
consisting solely of FEM data, providing a more focused
exploration of simulated scenarios. Subsequently, during
the evaluation phase, our trained models tested using
exclusively experimental data. As input and output
parameters, we used the same of our previous work [
We employed DL models, specifically focusing on neural network models. These neural networks, inspired by the structure and function of the human brain, are adept at learning complex patterns and relationships in the data.
In the development of these networks, we adopted a genetic algorithm approach. Genetic algorithms (GAs) draw inspiration from biological evolution, employing principles of natural selection and genetic recombination to ifnd optimal solutions for complex problems. Traditionally, GAs are utilized to optimize algorithm parameters; however, in our specific scenario, we employed GAs to design the architecture of the networks. The process of GAs requires several steps: , where potential solutions are randomly selected; , which identifies optimal parents based on their fitness; , where genetic material is recombined to generate new solutions; mutation, introducing random changes to generate genetic diversity; and , which assesses the fitness of the solutions. Each of these steps plays a crucial role in guiding the evolutionary process towards identifying optimal solutions for complex problems. In this work, we presented two NN as best solutions: Wide Neural (WNN) and Trilayered Neural (TNN) Networks.
WNN refers to an artificial neural network architecture that typically has fewer hidden layers but a substantial number of nodes in each layer. The TNN is a form of artificial neural network, also known as a single-layer perceptron, consisting of the input layer, one hidden layer and the output layer. To assess the efectiveness of prior findings. the DL approaches, we computed several performance metrics, including the RMSE, R-squared, MSE, and MAE.
For the flattening, the best results are obtained by the WNN. For the penetration depth, TNN reached the best values, but, except for the R-squared, all the values, on the test set, are high. Overall, the DL models achieved best performance for flattening (see Figure 4).
In our previous study, we evaluated ML algorithms using a mixed dataset. NN model excelled in predicting penetration on the test set, while LR proved optimal for prediction of flattening. To further comprehend our approach, we compared the outcomes of our earlier investigation with those obtained through the integration of GAs and we reported these results in Table 1 and graphically in Figure 1. Interestingly, the WNN demonstrated exceptional performance in predicting flattening, showcasing a marked improvement over previous results. Figure 5: Comparison between ML and DL models However, it is important to note that for penetration prediction, the TNN constructed with the use of GAs exhibited a less favorable performance compared to our
of DL techniques in predicting optimal parameter combinations, enhancing the eficiency and efectiveness of the coating process. With the introduction of the GAs to improve the design of networks, we can streamline the optimization of model architectures, reducing the need for manual hyperparameter tuning, which is often time-consuming and suboptimal.
Next-GenerationEU - National Recovery and Resilience Plan (NRRP) – MISSION 4 COMPONENT 2, INVESTIMENT N. 1.1, CALL PRIN 2022 D.D. 104 02-02-2022 – (OPTIMA: depOsition of cold sPray in the realm of green addiTIve manufacturing through construction of MAchine learning models) CUP N.D53D23017410001.