The authors' motivation is that traditional inspection methods are still commonly used in modern factories, but their limitations make AOI techniques increasingly relevant. The paper describes in detail the three phases of AOI, which are image acquisition, solder joint localization, and classification. The research of [PERSON] et al. [9] has drawn attention to the growing importance of visual quality control of products in all kinds of manufacturing processes. The study also highlighted that many factories still use traditional methods instead of automation to detect defects in products, these methods are expensive and labor-intensive, but due to the natural limitations of workers, efficiency is highly dependent on fatigue. This research has made it clear that there is an urgent need for novel automated defect detection systems in manufacturing processes. The authors implemented a CNN for image classification and successfully validated the approach on a simple benchmark dataset of defect-free and defective images. However, the paper did not consider a real-world example the proposed solution was also a CNN model, similar to the work of [PERSON] et al. [5]. As previous works have shown, automating visual inspection is an essential step towards efficient production to ensure fast and reliable product quality control. The use of deep learning solutions leads to excellent performance, but the bottleneck to the uptake of the technology is a large amount of training data or annotation and the lack of comprehensive knowledge of AI. In our proposed pipeline, beyond minimizing the labelling efforts, the domain experts may also identify misclassifications by trying more than one evaluating network that we put forth here. Thus, the proposed pipeline supports domain experts to independently evaluate their data without the in-depth involvement of AI specialists. We developed a prototypical pipeline jointly with a software tool called NIPGBoard used to conduct visual inception. The tool builds on the data projection benefits of the well-known TensorBoard visualization toolkit of TensorFlow [10]. In contrast to the aforementioned works, one of our main goals was efficient clustering of soldering images for easier annotation, rather than filtering defective images. Due to their sheer number (\(\approx\)75k) of samples in our database, it is difficult for the domain experts to label them by hand as it is a tedious and time-consuming process, meaning that an automated method is required. For this reason, we consider deep learning-based clustering techniques since they have become an influential trend in clustering methods and still have great potential. Another reason for considering these techniques would be that state-of-the-art deep learning architectures are available online with pre-trained weights on the ImageNet dataset [11]. Therefore, by utilizing these pre-trained models, it is possible to extract latent space representations (embeddings) from the network and train an independent algorithm to create clusters based on the features captured by these representations for any required dataset, indicating that the clustering representations of the data maybe be efficient independently from the database. The only condition is that the images have to be resized to fit the pre-trained model input shape. Training a model to fit these representations can be done with or without any labels, meaning it is possible to label a subset of the overall dataset, making this problem somehow like a semi-supervised problem instead of a fully unsupervised task. Both methods have their advantages and disadvantages, but in general, they both may work well. Nevertheless, for high accuracy, a ratio of the data needs to be manually annotated by the experts. This ratio depends on the required precision, dataset complexity, number of unique categories, variations within each class, and the dataset distribution.