In recent years, a steady increase in maritime business and annual container throughput has been recorded. To meet this growing demand, terminal operators worldwide are turning to automated container handling. For the automated operation of a crane, a reliable capture of the environment is required. In current state-of-the-art applications this is mostly achieved with light detection and ranging (LiDAR) sensors. These sensors enable precise three-dimensional sampling of the surroundings, even at great distances. However, the use of LiDAR sensors has a number of disadvantages, such as high acquisition costs and limited mounting positions. This raises the question of whether the LiDAR systems of automated container terminals (ACT) can be replaced with cameras. However, this transformation is not easy to accomplish and is explored in more depth in this paper. The field of camera-based container automation presented in this publication is largely unexplored. To the best of our knowledge, there is currently no automated container terminal in real-world operation that exclusively uses cameras. This publication aims to create a basis for further scientific research towards the goal of a fully camera-based container automation. Therefore, the authors present a narrative review providing a broad overview of the mentioned transformation, identifying research gaps, and suggesting areas for future research. In order to achieve this, this publication examines the fundamentals of an automated container terminal, the existing automation solutions and sensor technologies, as well as the opportunities and challenges of a transformation from LiDAR to camera.

Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW), which efficiently converts electrical energy into thermal energy to join metals. Despite decades of research and extensive application in the automotive and aerospace sectors, weld quality assessment in the GMAW process remains a major challenge. This paper presents a novel learning-based approach relying on a vector quantised variational autoencoder (VQ-VAE) for data representation. In addition, we are the first to provide a time series dataset to the research community that combines labeled and unlabeled time series data from the GMAW domain, thereby enabling further research. The core idea of our approach consists of two stages: In the first stage, we use a learned automatic extraction of local features of the input signal using a VQ-VAE architecture. Based on this, in the second stage, we use a transformer model that processes the discretized features and performs weld quality prediction and classification. Our approach addresses real-world scenarios and improves the prediction of quality and fill existing data gaps by providing a reliable approach for quality assessment during manufacturing based on sensor data.

The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is gas metal arc welding (GMAW). The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in GMAW. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.