<p>Large Format Additive Manufacturing (LFAM) enables the rapid production of composite components using short fiber-reinforced thermoplastics. This process offers high strength-to-weight ratios and efficient material deposition. However, the mechanical performance of printed parts is strongly dependent on fiber orientation. Reliable characterisation of orientation remains challenging due to the limitations of manual microscopy analysis. It is further hindered by the scarcity of large, annotated datasets required for deep learning inference (i.e., the automatic prediction of fiber orientation from image data using trained neural networks). This study addresses this gap by proposing a physics-informed methodology for the synthetic generation of microscopy-like images that replicate the microstructure of LFAM-processed composites. The simulation pipeline, implemented in Python, creates large-scale datasets by generating non-overlapping ellipsoidal fiber distributions with known geometrical and statistical parameters. Each synthetic image is paired with an analytically computed orientation tensor. This enables the supervised training of convolutional neural networks (CNNs) without the need for manual annotation. The system allows full control over fiber orientation, aspect ratio, and area fraction. It is calibrated using real micrographs obtained via X-ray Microscopy (XRM). The resulting synthetic images closely reproduce the statistical features of real microstructures and provide robust ground truth for model training and validation. Overall, this scalable approach enables rapid dataset generation and facilitates the development of accurate AI models for automated orientation analysis. It also supports future applications in digital twin frameworks, in-line quality control, and material characterisation in LFAM.</p>