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Quarry sites present unique operational challenges where the performance of heavy machinery is critical for maintaining efficiency and safety. In such environments, accurate torque prediction is essential for effective engine management and optimal task execution. This work addresses the torque prediction challenge for a wheel loader operating in quarry conditions by proposing a structured three-phase approach to feature selection that reduces model complexity while preserving predictive accuracy. In the first phase, features are selected based on domain expertise to capture the physical and operational realities of quarry machinery. A comprehensive set of features is then employed to establish a robust performance baseline. In the final phase, a data-driven analysis using SHapley Additive Explanations (SHAP) identifies the top five features that most significantly impact torque prediction. Model efficacy was validated via cross-validation, with R-squared and mean-squared error serving as the key performance indicators. Comparative analysis reveals that while SHAP-ranked features yield statistically optimal results, the expert-selected features are more aligned with the practical requirements of quarry operations. These findings support the design of efficient, interpretable digital twins for real-time decisions in challenging environments.

Developing large-scale industrial systems requires high-quality requirements to avoid costly rework and project delays. However, linguistic ambiguities in natural language (NL) requirements have been a long-standing challenge, often introducing misinterpretations and inconsistencies that propagate throughout the development lifecycle. Such ambiguous NL requirements necessitate early detection and well-reasoned explanations to clarify and prevent further misunderstandings among stakeholders. While solutions have been developed to detect ambiguities in NL requirements, the advent of generative large language models (LLMs) offers new avenues for explanation-augmented requirements ambiguity detection. This paper empirically investigates LLMs for ambiguity detection and explanation in real-world industrial requirements by adopting an in-context learning paradigm. Our results from three industrial datasets show that LLMs achieve a 20.2% average performance increase in classifying ambiguous requirements when prompted with ten relevant in-context demonstrations (10-shot), compared to no demonstrations (0-shot). Additionally, we conducted human evaluations of the LLM-generated outputs with eight industry experts along four dimensions---naturalness, adequacy, usefulness and relevance---to gain practical insights. The results show an average rating of 3.84 out of 5 across evaluation criteria, indicating that the approach is effective in providing supporting explanations for requirement ambiguities.

Quarry sites are complex systems that involve several heavy machines, equipment, people, and management systems working together in an unstructured off-road environment. Gaining accurate insights about these sites requires integrating models at various levels to enable a holistic view systems and processes involved and facilitate effective planning, coordination, and decision-making. In this paper, a multi-level modelling framework is proposed to provide an overall structure for the modelling of quarry sites. The motivation for this framework is drawn from insights gained through a large manufacturing company in the heavy-duty vehicle industry, providing a practical perspective on the modeling approach. The framework integrates models of different operations on site enabling effective simulation and optimization and leading to better understanding of the workflow on site and pointing out any possible bottlenecks. The feasibility of the proposed framework was validated through workshops that included a panel of experts in different areas of the field of off-road machinery production company.

Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation-assisted machine learning model with two concepts: general (adding all predictors at once) and step-wise (adding predictors as they become available in sub-yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10-fold cross-validation. The model's performance on three data sets-a real-world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard-shows a respective R2$R<^>2$ of 0.90, 0.87, and 0.70. Step-wise inclusion of the predictors results differently for the real-world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real-world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates-except for the receiving yard-enhance the predictions. We aim to predict freight train delay departures from the yard by implementing a simulation-assisted machine learning model via two general and step-wise concepts for including the predictors. In the general concept, we use all the predictors from yard operations at once. In the step-wise concept, the predictors are added to the model based on the stages of the operation to understand how each predictor impacts the departure delay. Our machine learning model is trained by real-world and simulation data. image

Construction sites are moving towards using autonomous machines, such as autonomous haulers, to improve productivity and safety. However, enabling efficient and safe navigation of autonomous haulers at an open-pit mining site necessitates a dynamic map of the environment. In our previous works, we introduced a dynamic multi-layered map designed for this purpose. Subsequently, we proposed how to adopt the digital twin standard for manufacturing to implement this map. Yet, the proposed dynamic multi-layered map needs to be validated in real-world scenarios, which are not evident for such off-road domains. This paper presents an analysis of the state-of-practice scenarios used in validating current static maps for a fleet of autonomous haulers performing assigned missions in real-world open-pit mining applications. Drawing from insights from this case study and industrial expertise, this paper suggests validation scenarios for the multi-layer dynamic map. Moreover, the paper discusses simulation tools that could be utilized to assess the feasibility of dynamic maps in such off-road domains at construction sites

This paper presents an interpretable machine-learning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate an approach that can improve interpretability of anomalous classifications obtained from an anomaly detection model. We evaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as our baseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations and reduces computational cost. The explanations are presented together with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approaches employing optimization techniques yielded explanations with greater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

Performance aspects such as travel time, punctuality, and robustness are conflicting goals of utmost importance for railway transports. To successfully plan railway traffic, it is therefore important to strike a balance between planned travel times and expected delays. In railway operations research, a lot of attention has been given to construct models and methods to generate robust timetables-that is, timetables with the potential to withstand design errors, incorrect data, and minor everyday disturbances. Despite this, the current state of practice in railway planning is to construct timetables manually, possibly with support of microsimulation for robustness evaluation. This paper aims to narrow the gap between the state-of-the-art optimization-based research approaches and the current state of practice to construct timetables by combining simulation and optimization. The paper proposes a combined simulation-optimization approach for double-track lines, which generalizes previous work to allow full flexibility in the order of trains by including a new and more generic model to predict delays. By utilizing delay data from simulation, the approach can make socioeconomically optimal modifications of a given timetable by minimizing predicted disutility-the weighted sum of scheduled travel time and total predicted delay. In a large simulation experiment on the heavily congested Swedish Western Main Line, it is demonstrated that compared with a real-life, manually constructed timetable, large reductions of delays as well as improvements in punctuality could be obtained for a small cost of marginally longer travel times. The cost of scheduled in-vehicle travel time and mean delay was reduced by 5% on average, representing a large improvement for a highly utilized railway line. Furthermore, a separate scaling experiment indicates that the approach can also be suitable for larger problems.

Autonomous machines are expected to be vastly used at construction sites as they can efficiently perform repetitive and dangerous tasks. However, ensuring the operational safety of such autonomous machines in a highly dynamic environment is challenging. Although autonomous machines usually are equipped with a perception system that permits them to navigate locally, there is a need to share a global view of the construction site to reduce the risk of accidents or errors. A digital twin of the construction site map has the potential of fusing the real-time perception from different sources at the site, such as different autonomous machines working at the construction site, analysing them and sharing the needed information to operate safely and effectively at the site. This paper proposes the adoption of the recently published standard, ISO 23247 digital twin framework for manufacturing, to implement and maintain a dynamic map of construction sites. The proposed framework will enable safe and efficient operation of autonomous machines on construction sites.

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.