Reconfigurable manufacturing systems are becoming the only viable option to respond to changing product volumes and product specification, which are currently major challenges for the manufacturing industry. Part of this adaptation requires vision systems to be quickly updated to handle new unseen products. For deep learning-based vision systems, this means re-training on images that might not be available. Although there is some existing work on synthetic image generation in manufacturing contexts using a variety of domain randomisation techniques, there is a lack of understanding of which domains are critical in the effectiveness of the resulting trained model. There are currently no open tools to systematically conduct such ablation studies. This paper presents a tool based on Blender and CAD models to enable the study of domain randomisation in the generation of synthetic-only datasets that can yield accurate object recognition models. Preliminary results to validate the implemented domain randomisation techniques and the ability to generate the synthetic images are presented. Once generated, synthetic data sets are used to train a YOLOv8 model for object detection as a second tool validation step. Future work will look at performing ablation studies and expanding the range of domain randomisation methods to further study the capabilities of synthetic images.