Process parameters can have a significant effect on the quality and performance of 3D printed parts. That is especially true for parameters affecting the temperature profile during manufacturing as temperature is the driving mechanism for bond formation between neighboring material. Supported by corresponding literature, experimental data obtained from a thermal camera exhibit a 23 °C difference between actual nozzle temperature and printing temperature specified during slicing of the part. This can result in lower mechanical properties and dimensional accuracy than originally designed, or even result in failed prints due to poor adhesion between the part and the heated bed. So, in this work, a hybrid experimental, numerical study of the factors affecting the nozzle temperature is performed, in order to calibrate and achieve the desired printing temperature. Specifically, a typical hotend is designed and is heated up to the printing temperature. The hotend exchanges heat with the environment, due to convection and radiation. The actual nozzle temperature at different combinations of printing conditions and materials is obtained. The numerical results are validated by experiments, showing a difference of less than 3%. Data driven models with accurate nozzle temperature predictions are provided to aid in selecting optimal slicing parameters.

The optimization of composite cure cycles is critical in materials engineering, requiring a deep understanding of the material’s cure kinetics. This study employs Differential Scanning Calorimetry (DSC) coupled with advanced fitting algorithms to determine the most accurate cure kinetics models for optimizing time-temperature profiles. Given the wide variability in model selection and fitting approaches in existing literature, our research systematically compares several cure kinetics models and their fitting methodologies to identify the most effective strategies. We highlight the advantages and limitations of each approach, with a particular focus on the robustness of the Levenberg–Marquardt (LM) algorithm compared to the Trust-Region-Reflective (TRR) algorithm. Given the existence of multiple local minima in the objective function, a global approach (genetic algorithm) is employed. Our findings reveal that, although the TRR and LM algorithms are faster, they are less accurate than the genetic algorithm. Moreover, the Kamal-Sourour model shows the highest prediction accuracy among the tested models. This comparative analysis provides a systematic approach and crucial insights into selecting the optimal cure kinetic model and corresponding parameters, based on experimental data, allowing for enhancing the efficiency and accuracy of composite curing processes.

Composite curing through infrared radiation (IR) has become a popular autoclave alternative due to lower energy costs and short curing cycles. As such, understanding and measuring the effect of all parameters involved in the process can aid in selecting the proper constituents as well as curing cycles to produce parts with a high degree of cure and low curing time. In this work, a numerical model that takes inputs such as part geometry, material properties, curing-related properties and applied curing cycle is created. Its outputs include the degree of cure, maximum curing temperature and total curing time. A genetic algorithm and a design of experiments (DOE) sequence cover the range of each input variable and multiple designs are evaluated. Correlations are examined and factor analysis on each output is performed, indicating that the most important inputs are activation energy, specimen precuring, applied curing temperature and curing duration, while all the others can be considered constant. Finally, response surfaces are created in order to effectively map and provide estimations of the design space, resulting in a curing cycle optimizer given certain restrictions over the input parameters.

Modern impact monitoring systems combine multiple inputs with machine learning (ML) models for impact detection, localization, and event assessment. Their accuracy relies on large, event-representative datasets, used for algorithmic development and ML model training. High-fidelity numerical models can provide augmented datasets by overcoming the cost and time limitations of experimental methods. This research presents an end-to-end numerical methodology for impact detection based on simulation (training) and experimental (testing) data. Initially, a finite element model (FEM) of our experimental setup utilizing piezoelectric transducer (PZT) sensors mounted on a thermoplastic plate is created. From the experimental impact signals, a few consistent cases are identified for feature extraction. A design of experiments explores the range of each parameter, and through surrogate optimization, the material and piezoelectric properties of the setup are determined. Subsequently, a virtual dataset, involving multiple impact cases, is created to train the ML models performing impact detection. Testing with experimental data shows results consistent with literature studies that used only experimental data for both training and testing. This work provides a systematic methodology for representative dataset generation and impact monitoring through ML, while addressing accurate FEM parameter identification from a few experimental tries.