First Steps Towards an Intelligent Laser Welding Architecture Using Deep Neural Networks and Reinforcement Learning

AbstractTo address control difficulties in laser welding, we propose the idea of a self-learning and self-improving laser welding system that combines three modern machine learning techniques. We first show the ability of a deep neural network to extract meaningful, low-dimensional features from high-dimensional laser-welding camera data. These features are then used by a temporal-difference learning algorithm to predict and anticipate important aspects of the system's sensor data. The third part of our proposed architecture suggests using these features and predictions to learn to deliver situation-appropriate welding power; preliminary control results are demonstrated using a laser-welding simulator. The intelligent laser-welding architecture introduced in this work has the capacity to improve its performance without further human assistance and therefore addresses key requirements of modern industry. To our knowledge, it is the first demonstrated combination of deep learning and Nexting with general value functions and also the first usage of deep learning for laser welding specifically and production engineering in general. This work also provides a unique example of how predictions can be explicitly learned using reinforcement learning to support laser welding. We believe that it would be straightforward to adapt our approach to other production engineering applications

Minimization of defects generation in laser welding process of steel alloy for automotive application

Laser welding (LW) thanks to its flexibility, limited energy consumption and simple realization has a prominent role in several industrial sectors. LW process requires careful parameters' tuning to avoid generating internal defects in the microstructure or a poor weld depth, which reduce the joining mechanical strength and result in waste. This work exploits a supervised machine learning algorithm to optimize the process parameters to minimize the generated defects, while catering for design specifications and tolerances to predict defect generation probability. The work outputs a predictive quality control model to reduce non-destructive controls in the LW of aluminum for automotive applications

Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques

Laser welding is particularly relevant in the industry thanks to its simplicity, flexibility and final quality. The industry 4.0 and sustainable manufacturing framework gives massive attention to in situ and non-destructive inspection methods to predict laser weld final quality. Literature often resorts to supervised Machine Learning approaches. However, selecting the ApTest method is non-trivial and often decision making relies on diverse and unclearly defined criteria. This work addresses this task by proposing a statistical comparison method based on nonparametric tests. The method is applied to the most relevant supervised Machine Learning approaches exploited in literature to predict laser weld quality, specifically, considering the optimisation of a new production line, hence focussing on supervised Machine Learning methods that do not require massive data set, that is, Generalized Linear Model (GLM), Gaussian Process Regression, Support Vector Machine, Classification and Regression Tree, and Genetic Algorithms. The statistical comparison is carried out to select the best-performing model, which is then exploited to optimise the production process. Additionally, an automatic process to optimise Machine Learning models and process parameters is resorted to, basing on Bayesian approaches, to reduce operator effect. This work provides quality and process engineers with a simple framework to compare Machine Learning approaches performances and select the most suitable process modelling technique