## 1 Introduction
### Contributions  
Based on the recently published research in the intersection of AM, ML, and mechanical property prediction, one can highlight the following main contributions of this work:  
* A ML pipeline that estimates mechanical properties such as the strength and elasticity of AM composite materials reinforced with continuous fibers--carbon (CRTP), Kevlar (KvRTP), and fiberglass (FGRTP).  
* A dataset was constructed from the peer-reviewed literature. It includes mechanical properties (elasticity and strength), materials for continuous fibers, and printing parameters. A robust analysis and the greater reliability of the ML models that have been tested enable the reproducibility and exploration of polymer-based AM technologies.
* In order to determine the most efficient ML models for predicting mechanical properties, this paper examines a variety of linear and nonlinear ML models. The performance analysis reveals their strengths and weaknesses when used within the AM context. As new algorithms are developed, they can be tested with the existing dataset. The modeling workflow used comprises k-fold cross-validation and Monte Carlo resampling, exploring different lightweight machine learning methods like Bayesian Ridge Regression (BAY), CatBoost Regression (CAT), Decision Tree Regression (DTR), k-nearest Neighbors (KNN), Lasso Regression (LAS), Random Forest Regression (RFR), Ridge Regression (RDG), and Support Vector Regression (SVR).
* The manuscript shows how decisive printing parameters are, such as deposition angles and fiber content, for the mechanical properties of Continuous Fiber Reinforced Polymer Matrix Composites (CFRPCs). Therefore, the results can be used to optimize material properties without conducting extensive experimental campaigns. The identification of such parameters should lead to a more efficient design anddevelopment process.  
Moreover, when applying ML to AM mechanical property prediction, there are implications such as enhanced design capabilities (i), personalized material selection (ii), reduction in experimental campaign development time (iii), and scalability and production efficiency (iv). (i) A designer can maximize a CFRP component by reducing the uncertainty in the mechanical property prediction. This could result in the manufacture of lightweight, high-strength components designed for tailored purposes, improving the overall performance of AM products. (ii) This study emphasizes the importance of various parameters, such as deposition angles and fiber content, that influence the mechanical properties of AM composites. With this in mind, a designer can select materials, in combination with AM parameters, to customize a tailored component more effectively, thus ensuring that the final part meets the requirements and performance standards. (iii) Experimental testing can be costly and time-consuming. By using ML models, manufacturers can lower the costs associated with material testing, thus speeding up the development process. (iv) The results indicate that ML can help popularize AM processes by demystifying the knowledge on how to best optimize printing parameters to achieve a mechanical property target.