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Aligning a lens system relative to an imager is a critical challenge in camera manufacturing. While optimal alignment can be mathematically computed under ideal conditions, real-world deviations caused by manufacturing tolerances often render this approach impractical. Measuring these tolerances can be costly or even infeasible, and neglecting them may result in suboptimal alignments. We propose a reinforcement learning (RL) approach that learns exclusively in the pixel space of the sensor output, eliminating the need to develop expert-designed alignment concepts. We conduct an extensive benchmark study and show that our approach surpasses other methods in speed, precision, and robustness. We further introduce relign, a realistic, freely explorable, open-source simulation utilizing physically based rendering that models optical systems with non-deterministic manufacturing tolerances and noise in robotic alignment movement. It provides an interface to popular machine learning frameworks, enabling seamless experimentation and development. Our work highlights the potential of RL in a manufacturing environment to enhance efficiency of optical alignments while minimizing the need for manual intervention.
Process curves are multivariate finite time series data coming from manufacturing processes. This paper studies machine learning that detect drifts in process curve datasets. A theoretic framework to synthetically generate process curves in a controlled way is introduced in order to benchmark machine learning algorithms for process drift detection. An evaluation score, called the temporal area under the curve, is introduced, which allows to quantify how well machine learning models unveil curves belonging to drift segments. Finally, a benchmark study comparing popular machine learning approaches on synthetic data generated with the introduced framework is presented that shows that existing algorithms often struggle with datasets containing multiple drift segments.
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real and complex data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To tackle these challenges, we introduce causalAssembly, a semisynthetic data generator designed to facilitate the benchmarking of causal discovery methods. The tool is built using a complex real-world dataset comprised of measurements collected along an assembly line in a manufacturing setting. For these measurements, we establish a partial set of ground truth causal relationships through a detailed study of the physics underlying the processes carried out in the assembly line. The partial ground truth is sufficiently informative to allow for estimation of a full causal graph by mere nonparametric regression. To overcome potential confounding and privacy concerns, we use distributional random forests to estimate and represent conditional distributions implied by the ground truth causal graph. These conditionals are combined into a joint distribution that strictly adheres to a causal model over the observed variables. Sampling from this distribution, causalAssembly generates data that are guaranteed to be Markovian with respect to the ground truth. Using our tool, we showcase how to benchmark several well-known causal discovery algorithms.
Many production lines require active control mechanisms, such as adaptive routing, worker reallocation, and rescheduling, to maintain optimal performance. However, designing these control systems is challenging for various reasons, and while reinforcement learning (RL) has shown promise in addressing these challenges, a standardized and general framework is still lacking. In this work, we introduce LineFlow, an extensible, open-source Python framework for simulating production lines of arbitrary complexity and training RL agents to control them. To demonstrate the capabilities and to validate the underlying theoretical assumptions of LineFlow, we formulate core subproblems of active line control in ways that facilitate mathematical analysis. For each problem, we provide optimal solutions for comparison. We benchmark state-of-the-art RL algorithms and show that the learned policies approach optimal performance in well-understood scenarios. However, for more complex, industrial-scale production lines, RL still faces significant challenges, highlighting the need for further research in areas such as reward shaping, curriculum learning, and hierarchical control.
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step approach for causal graph learning: First, infer the causal order among random vectors. Second, perform model selection to identify the best graph consistent with this order. We introduce effective and novel solutions for both steps in the vector case, demonstrating strong performance in simulations. Finally, we apply our method to real-world assembly line data with partial knowledge of causal ordering among variable groups.
Process curves are multivariate finite time series data coming from manufacturing processes. This paper studies machine learning that detect drifts in process curve datasets. A theoretic framework to synthetically generate process curves in a controlled way is introduced in order to benchmark machine learning algorithms for process drift detection. An evaluation score, called the temporal area under the curve, is introduced, which allows to quantify how well machine learning models unveil curves belonging to drift segments. Finally, a benchmark study comparing popular machine learning approaches on synthetic data generated with the introduced framework is presented that shows that existing algorithms often struggle with datasets containing multiple drift segments.
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To help address these challenges, we gather a complex dataset comprising measurements from an assembly line in a manufacturing context. This line consists of numerous physical processes for which we are able to provide ground truth causal relationships on the basis of a detailed study of the underlying physics. We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods. To accomplish this, we employ distributional random forests in order to flexibly estimate and represent conditional distributions that may be combined into joint distributions that strictly adhere to a causal model over the observed variables. The estimated conditionals and tools for data generation are made available in our Python library 𝚌𝚊𝚞𝚜𝚊𝚕𝙰𝚜𝚜𝚎𝚖𝚋𝚕𝚢. Using the library, we showcase how to benchmark several well-known causal discovery algorithms.
