<p>The concurrently existing wear mechanisms during the machining of hard materials necessitate conducting numerous experiments to generate a comprehensive dataset of worn cutting tool images while developing on-machine tool wear monitoring systems. The higher costs and time associated with image dataset preparation limit the number of experiments that can be conducted during the model development, resulting in limited exposure to practical machining conditions. This paper presents a systematic approach for generating diverse images of worn cutting tools by selectively applying image augmentation techniques. The augmentation techniques are critically evaluated to effectively capture the variabilities during image acquisition in a real-time machining environment. The techniques were chosen to simulate the presence of coolant, dust on the camera lens, lighting variations, differences in tool size and shape, and faulty camera setup. The initial dataset of 200 images labeled across four tool wear categories was generated by conducting machining experiments. A larger and balanced dataset of 28,000 images was created by applying these augmentation techniques to 80 % of the original dataset. An EfficientNet-b0 model was trained on the augmented dataset using a transfer learning approach. Subsequently, the remaining 20% of the original dataset was extended by applying combinations of data augmentation techniques for the model testing. The trained model demonstrated robust prediction abilities with an accuracy of 84.16% and a Matthews Correlation Coefficient (MCC) of 0.772. The analysis of misclassifications using the GradCAM-based explainability technique revealed that the chosen image augmentation techniques effectively captured the variability of the machining environment.</p>