Hierarchical diamond plate-lattice metamaterial: machine-learning co-optimisation of sound absorption and mechanics

How to automate the aggregation of a large number of dynamic systems and components into a model of a feasible design? This design must meet prescribed performance, cost, and time constraints and optimizes typical design criteria for dynamic systems, such as time constant, maximum overshoot, settling time, or absorbed power. In attempting to search for an approach to this automation problem, this paper studies some of the needs of computer systems that may effectively act as an innovation generator for dynamic systems. Potentially, novel concepts of dynamic subsystems can be automatically searched for and automatically generated by a digital computer. Human intervention may then be limited to defining the constraints. We distinguish two classes of design automation: The fixed and the variable design automation problem. In the first, the components of a system are arranged in a fixed sequence, and the computer explores alternate versions of components in order to optimize a prescribed design criteria and, satisfy manufacturing, cost, or durability constraints. The second class, called here variable structure, the computer program attempts to find the structure that connects various components such that their final assembly optimizes a certain design criteria while satisfying a set of design and manufacturing constraints. In order to efficiently automate the generation of assemblies of multi-energy-domain dynamic systems, ideal modeling components, such as bond graph elements, are used as basic building blocks. Bond graphs are mapped to a canonical form, such as linear graphs, which lend themselves to graph theoretical methods. Graph theoretical methods can then used to automatically generate possible linear graph combinations that consist of a given number of nodes or points. The nodes, in this case, correspond to the idealized modeling elements which are then assembled to produce the final design. Design constraints, cast in graph theoretic context, are applied to the large set of graphs that have are automatically generated in order to produce a limited number of meaningful models of designs.

Nanofibers, with their lightweight structure and superior sound absorption, are promising materials for noise control in automotive and architectural applications. However, due to the complex porous structure of nanofibers, established acoustic models often fail to accurately quantify the microstructure’s influence on sound absorption characteristics, resulting in substantial prediction errors. To determine the sound absorption characteristics of nanofibers, an equivalent fiber network model was developed using the multiscale finite element analysis (MFEA) method based on SEM images of nanofibers. The slip boundary condition (SBC) was then applied to calculate the microstructural parameters necessary for macroscopic characterization. The sound absorption coefficients of nanofibers were characterized using three acoustic models, and the results were compared with the experimental data. The predictions of the Limp frame model agreed well with the experimental data within the 500–6400 Hz frequency range. Through use of the multiscale model developed in this study, a deterministic relationship between microstructure and acoustic properties was established, revealing that the inertial interactions between sound waves and the nanofiber skeleton, as well as the slip boundary effect at the nanofiber surfaces, are among the primary mechanisms contributing to the flow resistance and superior sound absorption performance of nanofibers.

This paper reports the influence of nanosilica on friction and sound absorption responses of concrete for application in rigid pavements. The paper also discusses the feasibility of applying nano-lotus leaf as a coating for concrete material. Previous research on friction and sound absorption characteristics of concrete pavement primarily emphasized creating different surface textures through macrotexture modifications. The microstructure can also have a significant effect on surface texture, friction, and sound absorption characteristics of concrete. This research studied the friction and sound absorption properties of concrete on the basis of microtexture modification that used nanomaterials such as nano-silica and nano-lotus leaf. Several laboratory concretes were produced by using different proportions of nanosilica, which partially replaced cement by weight. The fresh concretes were tested for workability, wet density, and air content. The hardened concretes were tested for compressive strength, friction, and sound absorption. The British pendulum test was used to determine the friction number. An impedance tube was used to determine the sound absorption coefficient. Preliminary results indicate that nanosilica can increase friction and sound absorption of concrete pavements. In addition, the results show that nano-lotus leaf can be applied as a coating on concrete material for rigid pavements to improve retention of their friction properties during the wet season.

The sound absorbing performance of the sintered fibrous metallic materials is investigated by employing a dynamic flow resistivity based model, in which the porous material is modeled as randomly distributed parallel fibers specified by two basic physical parameters: fiber diameter and porosity. A self-consistent Brinkman approach is applied to the calculation of the dynamic resistivity of flow perpendicular to the cylindrical fibers. Based on the solved flow resistivity, the sound absorption of single layer fibrous material can be obtained by adopting the available empirical equations. Moreover, the recursion formulas of surface impedance are applied to the calculation of the sound absorption coefficient of multi-layer fibrous materials. Experimental measurements are conducted to validate the proposed model, with good agreement achieved between model predictions and tested data. Numerical calculations with the proposed model are subsequently performed to quantify the influences of fiber diameter, porosity and backed air gap on sound absorption of uniform (single-layer) fibrous materials. Results show that the sound absorption increases with porosity at higher frequencies but decreases with porosity at lower frequencies. The sound absorption also decreases with fiber diameter at higher frequencies but increases at lower frequencies. The sound absorption resonance is shifted to lower frequencies with air gap. For multi-layer fibrous materials, gradient distributions of both fiber diameter and porosity are introduced and their effects on sound absorption are assessed. It is found that increasing the porosity and fiber diameter variation improves sound absorption in the low frequency range. The model provides the possibility to tailor the sound absorption capability of the sintered fibrous materials by optimizing the gradient distributions of key physical parameters.

High-strength, fully porous TPMS lattice structures fabricated via additive manufacturing offer exceptional potential for lightweight and multifunctional applications in engineering and biomedical fields. While prior studies have examined the influence of individual morphological features on mechanical performance, a systematic approach that considers the interdependence of design parameters, manufacturing constraints, and application-specific requirements remains lacking. This study introduces a computational framework to optimize the mechanical performance of gyroid-based TPMS lattices using finite element analysis (FEA) and single-objective Bayesian optimization. Lattice structures were modeled in nTopology by morphological parameters, and their compressive mechanical response was evaluated in ABAQUS using the Johnson-Cook material model to capture the nonlinear behavior of PEEK. An initial mechanical response map was generated from FEA results to explore structure–property relationships and served as the foundation for Bayesian optimization (BO). The BO algorithm iteratively refined this model to identify optimal morphological parameters that maximize the effective modulus within defined design and manufacturability constraints. This framework demonstrates the effectiveness of probabilistic design strategies in efficiently navigating complex design spaces. The findings underscore the synergistic role of morphological parameters in governing mechanical properties and offer a scalable methodology for the rational design of high-performance architected materials via additive manufacturing.

The objective of this paper is to investigate specific methodologies for conceptual design and to establish a computational framework for an intelligent CAD system in a concurrent engineering environment. The main idea in developing such a system is to help designers in conceiving better design ideas within given sets of design, manufacturing, and assembly constraints. It is, therefore, essential to integrate intelligently diverse knowledge sources from different fields of manufacturing (e.g., design, process planning, assembly, inspection). When an object creation process is complete, the system analyzes the structure in consultation with its intelligent computational modules (“local experts”) to make sure that no functional, geometric design or manufacturing constraints are invalidated and to suggest an alternative better design, whenever possible. The paper also discusses the implementation of a prototype system for automated fixture design in the proposed concurrent engineering environment.

Design-for-manufacturing (DFM) concepts have traditionally focused on design simplification; this is highly effective for relatively simple, mass-produced products, but tends to be too restrictive for more complex designs. Effort in recent decades has focused on creating methods for generating and imposing specific, process-derived technical manufacturability constraints for some common problems. This paper presents an overview of the problem and its design implications, a discussion of the nature of the manufacturability constraints, and a survey of the existing approaches and methods for generating/enforcing the minimally restrictive manufacturability constraints within several design domains. Four major design perspectives were included in the study, including the system design (top-down), the product design (bottom-up), the manufacturing process-dominant approach (specific process required), and the part-redesign approach. Manufacturability constraints within four design levels were explored as well, ranging from macro-scale to sub-micro-scale design. Very little previous work was found in many areas but it is clear from the existing literature that the problem and a general solution to it are very important to explore further in future DFM and design automation work.

BackgroundProviding coral reef systems with the greatest chance of survival requires effective assessment and monitoring to guide management at a range of scales from community to government. The development of rapid monitoring approaches amenable to collection at community level, yet recognised by policymakers, remains a challenge. Technologies can increase the scope of data collection. Two promising visual and audio approaches are (i) 3D habitat models, generated through photogrammetry from video footage, providing assessment of coral cover structural metrics and (ii) audio, from which acoustic indices shown to correlate to vertebrate and invertebrate diversity, can be extracted.MethodsWe collected audio and video imagery using low cost underwater cameras (GoPro Hero7™) from 34 reef samples from West Papua (Indonesia). Using photogrammetry one camera was used to generate 3D models of 4 m2 reef, the other was used to estimate fish abundance and collect audio to generate acoustic indices. We investigated relationships between acoustic metrics, fish abundance/diversity/functional groups, live coral cover and reef structural metrics.ResultsGeneralized linear modelling identified significant but weak correlations between live coral cover and structural metrics extracted from 3D models and stronger relationships between live coral and fish abundance. Acoustic indices correlated to fish abundance, species richness and reef functional metrics associated with overfishing and algal control. Acoustic Evenness (1,200–11,000 Hz) and Root Mean Square RMS (100–1,200 Hz) were the best individual predictors overall suggesting traditional bioacoustic indices, providing information on sound energy and the variability in sound levels in specific frequency bands, can contribute to reef assessment.ConclusionAcoustics and 3D modelling contribute to low-cost, rapid reef assessment tools, amenable to community-level data collection, and generate information for coral reef management. Future work should explore whether 3D models of standardised transects and acoustic indices generated from low cost underwater cameras can replicate or support ‘gold standard’ reef assessment methodologies recognised by policy makers in marine management.

A new fabrication method of designed metamaterial based on a 3D-printed structure for underwater sound absorption applications

An acoustic absorption structure of a double-layer porous metal material with air layers is proposed. The Johnson-Champoux-Allard (JCA) model combined with the transfer matrix method (TMM) was used to establish the theoretical calculation model of the sound absorption coefficient (SAC). Meanwhile, the SAC between 500 and 6300 Hz were measured with an impedance tube. The errors between the theoretical and experimental values were compared to illustrate the good predictability of the theoretical model within the inverse estimations of the transport properties. The effects of the material placement order, material thickness, and cavity depth on the sound absorption performance from 200 to 5000 Hz were analyzed using the theoretical model. Further, a multi-objective function genetic algorithm was used to optimize the porous material's thickness and SAC to obtain an acoustic structure with a smaller thickness and higher sound absorption. A series of optimal solutions were obtained for acoustic structures with a total thickness of less than 70 mm. When the total thickness of the foam metal was 33.57 mm, the average SAC reached 0.853, which was significantly lower than the total thickness of the previous experiments. The multi-objective function genetic algorithm can provide a reliable solution for the optimal design of most sound-absorbing structures.

Traditional design-for-manufacturability (DFM) strategies focus on efficiency and design simplification and tend to be too restrictive for optimization-based design methods; recent advances in manufacturing technologies have opened up many new and exciting design options, but it is necessary to have a wide design space in order to take advantage of these benefits. A simple but effective approach for restricting the design space to designs that are guaranteed to be manufacturable is needed. However, this should leave intact as much of the design space as possible. Work has been done in this area for some specific domains, but a general method for accomplishing this has not yet been refined. This article presents an exploration of this problem and a developed framework for mapping practical manufacturing knowledge into mathematical manufacturability constraints in mechanical design problem formulations. The steps for completing this mapping and the enforcing of the constraints are discussed and demonstrated. Three case studies (a milled heat exchanger fin, a 3-D printed topologically optimized beam, and a pulley requiring a hybrid additive–subtractive process for production) were completed to demonstrate the concepts; these included problem formulation, generation and enforcement of the manufacturability constraints, and fabrication of the resulting designs with and without explicit manufacturability constraints.

This study examines the characteristic parameters required for non-circular-hole microperforated panels (MPPs) to achieve sound absorption performance comparable to that of conventional circular-hole MPPs. Through numerical analysis, the flow resistivity and perforation ratio were found to be key parameters influencing the absorption characteristics of MPPs with square and equilateral triangular holes. The results indicate that for square-hole MPPs, matching either the flow resistivity alone or both the flow resistivity and perforation ratio to those of circular-hole MPPs leads to similar sound absorption characteristics. In contrast, for equilateral triangular-hole MPPs, both the above parameters must be matched to ensure comparable performance. Furthermore, this study explores MPPs incorporating a combination of circular and non-circular holes. It was confirmed that by appropriately matching the flow resistivity and perforation ratio, such mixed-hole MPPs can achieve sound absorption characteristics similar to those of MPPs composed solely of circular holes. These findings contribute to the broader design possibilities of MPPs, providing a foundation for optimising hole geometries in practical applications where manufacturing constraints or aesthetic considerations may necessitate non-circular hole patterns.

Wire arc additive manufacturing (WAAM) has emerged as a useful option for large-scale metal additive manufacturing. It has gained widespread use in the aerospace industry and other applications that require large and complex custom parts. The process is a combination of a precise control system and a welding process, typically GTAW or MIG welding. It is a member of the directed energy deposition (DED) family of AM processes. Compared with many metal AM processes, it is relatively simple and cost-effective but almost always requires a significant amount of postprocessing before parts can be used. The AM-based nature of the process and its unique mechanics make designing parts for it challenging, particularly in cases where the designer does not have much technical experience with the process. Therefore, a way to obtain the process constraints in a way that is useful in design without over-restricting the design space is needed. This project explores a technique for mapping realistic manufacturability constraints from the process directly to the designed parts, which is useful for several different types of design problems. During this work, important manufacturability constraints were identified by directly mapping the process mechanics. The results of this work will be very useful in formulating design problems in which the final part will be fabricated using WAAM. An illustrative case study was developed to demonstrate the method.

Deepwater oil and gas facilities typically encounter on an average up to 5% annual production losses due to unplanned downtime, conservatively estimated at billions of dollars impact for the industry. The existing toolkit and systems in place are not always adequate to identify and predict abnormal events that could lead towards unplanned facility shutdown. The interaction amongst process sub-systems and disturbances that propagate across these sub-systems with changing operating conditions are hard to predict without a fit-for-purpose model (or a digital twin). The focus of current work is on deepwater facility having several oil export pipeline pumps in parallel and several gas compressors in series. The alarm database showed records of several unplanned shutdown events around these critical equipements that resulted in undesirable outcomes such as production deferment, complete facility shutdown, loss of sales volumes and increased operational costs. In this work, an intelligent prognostic solution is proposed using machine learning (ML) framework for automatic prediction of impending facility downtime, and identification of key causative process variables. A systematic workflow was developed to identify, cleanse and process real time data for both model training and prediction. Several ML methods were evaluated; anomaly detection based on Principal Component Analysis (PCA) and Autoencoder (AE) algorithms were found performing better for the type of data available for the deepwater facility. The ML framework also supported analysis of underlying downtime causes to propose suitable mitigation steps. Knowledge based on physical understanding of the process was used to select each sub-system boundary and sensor list on which ML model was trained. These models were then cross-validated to test the accuracy of trained models. Finally, the alarm database was used to confirm the accuracy of the machine leaning models and identify root-causes for unplanned shutdowns. If the operating condition changes over time, the anomaly detection based ML models were setup to adapt to changing conditions by automatic model updates, resulting in significant reduction in false alarms. The adaptive ML models, when applied to one of the sub-system (with 30 different sensor data), predicted 24 unplanned events in 6 months of period, while when applied to another sub-system (with 40 sensor data), predicted only 6 unplanned downtime events. Several predictions were found as early as 30 mins to 2 hours, providing adequate early warning to take proactive actions. Case studies shown in the paper present diagnostic charts and identified early indicators were found in agreement with pre-alarms generated by existing alarm system, thus validating the ML solution. Current toolkit available to identify anomalous process behavior is limited to exception based surveillance with fixed min-max limits on each sensor data. Therefore, proposed adaptive ML solution has shown potential to revolutionize the topside process surveillance. This paper also describes how the ML framework can be scaled for a sustainable solution that provides prediction every minute, keeps the model evergreen utilizing cloud-based model deployment platform to train, predict and trigger automatic model updates and also span multiple process systems and facilities. Finally, we present directions for future work, where the current model can keep predicting various events and over time when sufficient events are collected, more advanced machine learning methods based on supervised ML can be developed and deployed.

Carpeting is one of the most efficient approaches to provide both the acoustic and thermal insulation, and improve the comfort of building inhabitants. In the current research work, the acoustic and thermal properties of handmade carpets as one of the commonly used textile products in several countries, especially in Middle-East countries, have been investigated. Aiming to find the optimum weaving condition to maximize the insulation performance of the handmade wool carpets, the weaving variables of knot type, knot density, and pile height were changed. By performing a set of experiments according to the Response Surface Methodology (RSM), the sound absorption coefficient and thermal resistance values were measured for each sample. The statistical analysis showed that the sound absorption and thermal properties of carpets were appropriately described with the linear model. The pile height was the most effective factor for the both sound absorption and thermal resistance responses. Knot density was marginally significant, while the knot type had a negligible contribution. Increasing the height of the pile and knot density led to an increase in sound absorption and thermal resistance. The optimization procedure suggested that within the experimental range, the best acoustic and thermal insulation, can be obtained at knot density of 35 (per 7 cm), and the pile height of 3 cm.