This paper presents an automated synthetic image data generation pipeline aimed at streamlining the training process of object detection models supporting manual assembly processes. By automating the rendering of images from CAD models instead of relying on manually created physical product images, the pipeline enables dataset creation in earlier phases of the product lifecycle while also significantly reducing manual effort. This approach enhances the accessibility for fine-tuned object detection model development. The pipeline integrates two core components: object similarity analysis and synthetic image generation. The similarity analysis groups visually similar objects into unified classes for the object detection model, reducing confusion during detection. The image generation process can be augmented with contextual information from virtual 3D workplace scenes, thereby significantly mitigating the sim-to-real gap. The pipeline is accessed via a REST API, enabling seamless integration with PLM systems for automated retrieval of CAD models and workplace scene data. A workflow manager orchestrates interactions between the user, the PLM system, and the generation pipeline. The effectiveness of the system is validated by evaluating object detection models trained on the synthetically generated datasets against real-world images, demonstrating its potential to improve detection accuracy and robustness in industrial environments.