On the dependency of the extent of multiple solution zone around stability lobes on cutting law nonlinearity

In machining vibrations analyses, regenerative chatter stability boundaries aka stability lobes are known to be often accompanied by a multiple solution zone in process parameters space. In that zone the stable steady response coexists with a finite-amplitude oscillatory solution preceding cut interruption. Exploration of the oscillatory behavior requires accounting for finite nominal cut thickness and the condition of the tool exit from cut. In the present work we explore these conditions via a harmonic balancing framework, bringing forward the dependency of the extent of the unstable post-critical chatter domain on the cutting law nonlinearity

Assessing VOIP intelligibility in a low-connectivity environment

Previous work has shown that telecollaboration is a suitable solution for remote assistance of industrial maintenance operations, provided that an audio chat solution is available. There are several reasons why audio chat may not be available: the quality of the available Internet network, both in terms of bandwidth and stability (jittering), but also the presence of too much noise at the site of the operation, which interferes with voice capture. This paper presents a methodology to evaluate the quality provided by an audio chat solution. This methodology is then tested on a specific audio chat solution built on a lossy compression algorithm based on the grouping of successive similar values to overcome the jittering problem and significantly reduce bandwidth requirements. We suggest evaluating the audio quality by assessing the intelligibility of different audio recordings using standard speech therapy methods. Our results suggest that an audio chat can be provided even in a low bandwidth scenario and in a noisy environment, which provides promising insights for the further development of telecollaboration. Moreover, the assessment of audio quality using restitution exercices to evaluate intelligibility, tested on a real use case gives interesting results on the usability of an audio chat solution as well as detailed feedbacks on which part of the altered signal is to be improved

A probabilistic model to consider scale and gradient effects in the prediction of the fatigue life of Inconel 718 for turbine disk application

The aim of this paper is to implement and compare different fatigue post-processing approaches for fatigue life assessment of complex parts. Inconel 718 is taken as an example, as it can exhibit several factors influencing fatigue life, such as mean stress, stress gradient and scale effects. Tests on different specimen geometries to exacerbate these effects were carried out at 550°C. The range of service operating life is between 103 and 106 cycles. A modelling chain was then set up. A structural calculation was performed using an elasto-visco-plastic behavior law to obtain the mechanical fields at cycle stabilize. These values were finally exploited by applying a post-processing treatment approach to predict the fatigue life of the structure. Two main types of post-processing approach were investigated: standard and probabilistic. The way in which the different factors influencing fatigue life are considered, depending on the approach used, was discussed. Finally, the probabilistic volume approach yields better results, thanks to its ability to consider mean stress, stress gradient and scale effects in the proposed formulation

Physics-informed deep homogenization approach for random nanoporous composites with energetic interfaces

This contribution presents a new physics-informed deep homogenization neural network model for identifying local displacement and stress fields, as well as homogenized moduli, of nanocomposites with periodic arrays of porosities under general loading conditions. Notably, it accounts for the surface elasticity effect, utilizing the Gurtin-Murdoch interface theory. First of all, a fully connected neural network model is established that maps the spatial coordinates, passing first through several sinusoidal functions, to the microscopic displacements. The loss function is formulated as the weighted sum of residuals of Navier-Cauchy equations in the bulk domains and the Young-Laplace equations on the energetic surfaces, evaluated on separate sets of collocation points. To more effectively predict stress concentrations inside the microstructures, we introduce fully trainable weights to each collocation point. The capacity and effectiveness of the new homogenization technique for capturing the size-dependent local and global response of nanocomposites with distinct pore sizes and shapes are verified upon extensive comparisons with the finite-element benchmark results, under various loading conditions. New results showcase the proposed theory’s ability to model random distributions of nano-porosities with a high degree of accuracy, a task not easily achievable with alternative techniques except for the specialized finite-element method

Multi-dimensional measurement of mental workload in industrial context: an experiment in the field of helicopter maintenance

Assessing mental workload is essential for optimizing the design of complex systems, particularly in aeronautical maintenance, where operators' activities serve as a crucial safety barrier to ensure optimal system safety levels. One of the roles of human factors in maintainability is, therefore, to anticipate maintenance activities and human behavior from the start of the design cycle. This study pursues a dual objective: firstly, to identify relevant for evaluating mental workload in an industrial maintenance environment, and secondly, to determine which of these indicators correlate with performance degradation. Ten participants performed five maintenance tasks of varying complexity on a helicopter, involving the removal, installation of components and a detailed inspection. Subjective measures (NASA-TLX), performance metrics (completion time), and cardiovascular data (heart rate, heart rate variability) were analyzed. We observed longer completion times and higher NASA-TLX scores for complex maintenance conditions. Regarding cardiovascular data, the results in the time domain of heart rate variability follow a similar trend compared to two other types of measurements. These results will be discussed in depth in this article. This study represents a further step in the multidimensional measurement of mental workload in maintenance within a realistic industrial context

Hybrid homogenization neural networks for periodic composites

A new physics-informed deep homogenization neural network (DHN) framework is proposed to identify the homogenized and local behaviors in periodic heterogeneous microstructures. To achieve this, the displacement field is decomposed into averaged and fluctuating contributions, with the local unit cell solution obtained via neural networks subject to periodic boundary conditions. The periodic microstructures are divided into sub­domains representing the fiber and matrix phases, respectively. A key contribution of the proposed method is the marriage of elasticity solution and physics-informed neural network to each phase of the composite, namely, the fiber phase as a mesh-free component whose fluctuating displacements are expanded using a discrete Fourier transform, and the matrix phase using material points with fluctuating displacements handled through fully connected neural network layers. The interfacial continuity conditions are enforced by minimizing the traction and displacement differences at separate material points along the interface. Transfer learning is exploited further to facilitate training new microstructures from pre-trained geometry. This hybrid formulation inherently satisfies stress equilibrium equations within the fiber, while efficiently handling the periodic boundary conditions of hexagonal and square unit cells via a series of trainable sinusoidal functions. The innovative use of distinct neural network architectures enables accurate and efficient predictions of displacement and stress when discontinuities are present in the solution fields across the interface. We validate the proposed DHN with the finite-element predictions for unidirectional composites comprised of elastic fiber significantly stiffer than the matrix, under various volume fractions and loading conditions

Mobilization of DNAPL lenses in heterogeneous aquifers using shear-thinning PEO polymers: Experimental and numerical study

Polymer solution injection has emerged as a promising method for the remediation of NAPL (non-aqueous phase liquids)-contaminated aquifers. This technique enhances recovery efficiency by modifying viscous forces, stabilizing the displacement front, and minimizing channeling effects. However, there remains a significant gap in understanding the behavior of polymer solutions, particularly those with different molecular weights (MW), for mobilizing DNAPL (dense non-aqueous phase liquids) trapped in heterogeneous aquifers, especially within low-permeability layers. In this study, we address this gap by investigating the mobilization of DNAPL lenses confined by low-permeability layers through the injection of polyethylene oxide (PEO) polymers of varying MW. PEO solutions with MW of 5 M (million) and 8 Mg/mol displayed shear-thinning behavior for shear rates of 0.01 to 100 s-1, while the 1 Mg/mol solution showed shear-thinning below 10 s-1 and Newtonian behavior above. PEO solutions in porous media exhibit Newtonian behavior at low-to-moderate shear rates for all MWs, likely due to confinement-limited entanglement. Adsorption studies found non-significant PEO adsorption on soil surfaces, likely due to its large molecular size. Post-flushing of PEO-saturated columns with water led to notable permeability reductions attributed to viscous fingering. Column tests indicated a decrease of the residual DNAPL saturation with the capillary number (Ca), more sharply in low permeability soils. 2D cell tests identified three stages of DNAPL mobilization: initial stabilization, sharp recovery increase upon PEO arrival, and a final stabilization at residual saturation. The duration of each transition was found to be influenced by concentration. Numerical simulations accurately mirrored these stages and provided additional insights into PEO viscosity distribution and DNAPL mobilization patterns in heterogeneous media. The results highlighted that higher injection rates promote mobilization from the two low permeability layers surrounding the DNAPL bank from both sides and the upper zone, while lower rates mainly drive mobilization from the upper side. Using numerical simulations the performance of PEO injection on displacement of DNAPL in multiple lenses and various position of recovery points was evaluated

Implementation of the sliding-mode controller on overhead cranes via the deadbeat method

This paper addresses the controller design for the overhead cranes. To this end, a nonlinear model of the overhead cranes is considered, and a continuous-time sliding-mode controller is designed for such a model, ensuring global robust stability. Subsequently, the deadbeat implementation method is employed to obtain a discrete-time equivalent of the designed controller, which is a crucial step to implement any continuous-time controller on digital processors. Comparative analyses based on numerical simulations show that the developed sliding-mode controller shows several advantages over the forward Euler discretization method, which is widely used in the literature

Prevailing effect of residual stresses and defects on the fatigue strength of net-shape parts produced with Laser Powder Bed Fusion (L-PBF) 316L stainless steel

Financement région Pays de la LoireLaser Powder Bed Fusion (L-PBF) additive manufacturing enables the production of complex-shaped parts with high mechanical resistance. Such components exhibit a multi-scale microstructure, internal, sub-surface, and surface defects, a rough surface finish and a residual stress gradient in the as-built net-shape condition. All of these factors can alter the fatigue behaviour. This study aims to improve the understanding of the combined effect of various surface parameters on the fatigue behaviour of L-PBF 316L stainless steel by conducting an extensive experimental campaign. Uni-axial fatigue tests were carried out on six batches having an as-built or heat-treated microstructure and a net-shape, pre-corroded net-shape, single-defect net-shape or polished surface condition. Results were compared with data from the literature: as-built polished, pre-corroded polished, or single-defect polished specimens. Studied defects were process-induced (e.g. lack of fusion, spatter, gas pore) and artificial (i.e. corrosion pit, electric discharge machined defect). Their sizes ranged from 10 to 700 μm. The residual stresses gradients were characterized by X-ray diffraction. A Kitagawa–Takahashi diagram was used to illustrate the effects of the various parameters on the fatigue behaviour. Residual stresses and defects were the most influential factors on the fatigue strength of net-shape specimens over surface condition and sub-surface microstructure

Ontology-driven LLM framework for knowledge graph in smart buildings

The rapid data growth in smart building environments requires advanced tools to integrate, interpret, and utilize this information effectively. Smart buildings generate vast and heterogeneous data streams, including sensor readings, occupancy metrics, and environmental conditions, which are critical for optimizing energy efficiency, enhancing occupant comfort, and enabling predictive maintenance. However, the lack of a structured approach to automatically connect and contextualize these data sources limits the insights that can be derived. To address these challenges, this paper presents a framework for assessing data in a knowledge graph by automatically retrieving entities and establishing relationships from diverse data sources, incorporating metadata standards and time-series data relevant to smart buildings. A standard ontology from the domain is used to drive the experiment, enabling the automatic construction of a semantic graph-based model for a real-world smart building environment. The framework s objective is to ensure information is comprehensible as a preliminary step to intelligent decision-making in data-driven smart buildings, enabling applications like fault detection, performance measurement, and energy auditing. The proposed approach explores the potential of large language models (LLMs) to automate data integration, reducing reliance on experts. This paper addresses existing literature gaps on metadata mapping and lays the groundwork for future advancements in digital twin technologies for smart building applications