# Applications of Machine Learning and Computer Vision in Industry 4.0
## 3 An Overview of Published Articles  
The article by [PERSON] et al. (contribution 1) is devoted to the realm of additive manufacturing (AM), also known as 3D printing, where sorting a large number of parts swiftly and precisely is crucial. However, traditional recognition methods frequently fall short, creating a bottleneck in the process. This article introduces a novel workflow that leverages the power of neural networks to address this challenge. The proposed workflow hinges on the generation of synthetic training data. This data is meticulously crafted from computer-aided design (CAD) models, which act as digital blueprints for the AM parts. By feeding this synthetic data into a neural network, the researchers essentially train the network to recognize the distinctive characteristics of various AM parts. Once trained, the network can then be employed to classify real AM parts with remarkable accuracy, eliminating the need for any modifications to the parts themselves. To validate the efficacy of their proposed workflow, the researchers conducted an industrial case study. The results were promising, demonstrating that the neural network achieved a high degree of accuracy in classifying AM parts. This paves the way for the potential integration of this workflow into real-world AM production lines, streamlining the sorting process and enhancing overall efficiency. In essence, this article presents a groundbreaking approach to AM part recognition using neural networks. By harnessing the power of synthetic training data, the proposed workflow offers a solution to a longstanding challenge in the AM industry, potentially leading to significant improvements in sorting efficiency and production throughput. [PERSON] and [PERSON] (contribution 2) present an article about a new method for controlling manipulators, which are robotic arms used in various industrial applications. The article discusses the challenges associated with maintaining manipulator stability during operation, as instability can lead to safety hazards and decreased performance. To address this challenge, the authors propose a system that leverages the combined strengths of two machine learning techniques: Long Short-Term Memory (LSTM) networks and XGBoost. LSTM networks are adept at identifying patterns in sequential data, making them well-suited for analyzing the continuous stream of vibration signals collected from the manipulator. XGBoost, on the other hand, is a powerful tool for making accurate predictions based on various data inputs. By combining these techniques, the researchers create a model that can effectively learn the complex relationships between the vibration signal features and the manipulator's stability state. The model is trained on a dataset of vibration signals collected under various operating conditions, allowing it to generalize to unseen scenarios. The article also introduces a scoring system that translates the model's predictions into a user-friendly stability score. This score provides a clear indication of the manipulator's current stability level, enabling human operators to make informed decisions about its control. The effectiveness of the proposed method has been validated through extensive experiments. The results demonstrate that the LSTM-XGBoost model achieves superior performance compared to baseline methods in predicting manipulator stability. This paves the way for the potential integration of this approach into real-world manipulator control systems, enhancing safety, efficiency, and reliability in industrial operations. The article presents a significant contribution to the field of manipulator control by introducing a novel and effective method for predicting stability using machine learning. The combination of LSTM networks and XGBoost, along with the proposed scoring system, offers a promising solution for addressing this critical challenge in industrial robotics. Research by [PERSON] et al.