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This paper presents a review of recent advances in modeling and simulation of conventional metal machining processes, which continue to dominate a significant part of all machining processes, and in recent years, the need for predictive models for machining processes has grown in importance in the digital manufacturing age. Significant advances have been made in modeling the mechanics of cutting in conventional machining, driven by industrial need and enabled by rapid advances in computational power. The paper surveys the state-of-the-art in analytical and numerical modeling of conventional metal machining processes with a focus on their ability to predict useful performance attributes including chip geometry, forces, temperatures, tool wear, residual stress, and microstructure. Also included in the review is a discussion of the industrial use of modeling and simulation tools for conventional machining. Additionally, the practical applicability, implementation benefits, and methodological limitations of conventional machining process modeling have been examined. The paper concludes with a summary of future research directions in modeling and simulation of conventional metal machining processes. [ABSTRACT FROM AUTHOR]

The extended meniscus and the intermolecular and capillary forces that govern its behavior and connection to change-of-phase heat transfer have been the subject of an increasing body of research over the past 50 years. We have been fortunate to be at the forefront of this effort starting from the development of a capillary feeder, in Earth's gravity, to stabilize film boiling to running a series of transparent heat pipe experiments aboard the International space station hoping to better understand the role of intermolecular forces in microgravity. The use of ellipsometry and interferometry to highlight the location and state of the vapor-liquid interface have been key to these studies and have helped to uncover many new, interesting, and sometimes unexpected phenomena associated with fluid flow and change-of-phase heat transfer. [ABSTRACT FROM AUTHOR]

Origami is the art of creating a three-dimensional (3D) shape by folding paper. It has drawn much attention from researchers, and the designs that origami has inspired are used in various engineering applications. Most of these designs are based on familiar origami patterns and their known deformations, but origami patterns were originally intended for materials of near-zero thickness, primarily paper. To use the designs in engineering applications, it is necessary to simulate origami in a way that enables designers to explore and understand the designs while taking the thickness of the material to be folded into account. Because origami is primarily a problem in geometric design, this paper develops a geometric simulation for thick origami. The actuation, constraints, and assignment of mountain and valley folds in origami are also incorporated into the geometric formulation. The experimental results show that the proposed method is efficient and accurate. The method can successfully simulate a flat-foldable degree-four vertex, two different action origami, the bistable property of a waterbomb base, and the elasticity of non-rigid origami panels. [ABSTRACT FROM AUTHOR]

Brittleness in paper is one of the primary reasons library books are removed from circulation, digitized, or have their access limited. Yet, paper brittleness is difficult to characterize as it has multiple definitions and no single measurable physical or chemical property associated with it. This study reevaluates the cantilever test as applied to aged papers. In this nondestructive test, the deflection of a strip of paper held horizontally is measured across its length. The deflection data are then fit to nonlinear bending theories assuming large deflection of a cantilever beam under a combined uniform and concentrated load. Fitting the shape of the deflection profiles provides bending and elastic moduli, the bending length, and confirms that the paper sheets respond linearly. The results are compared to those calculated from a simplified single point measurement of the maximum deflection of the cantilevered sample. Young's modulus measured by the cantilever test is lower for paper-based materials than that measured by tensile testing, and the bending modulus was found to correlate with the destructive Massachusetts Institute of Technology (MIT) fold endurance test. [ABSTRACT FROM AUTHOR]

It was an honor to receive the Max Jakob Memorial Award for 2018. This invited paper is based on the award lecture and is written in an informal style expanding on the lecture and focusing on work in which I have been involved over the past 60-years. Early experimental and theoretical work was published only in conference proceedings when this was regarded an alternative to a journal and not, as seems to be the case nowadays, prior to journal publication or for incomplete work. I welcome this opportunity to clarify and make some of this earlier work more readily accessible. My comments on more recent work of others are also included. [ABSTRACT FROM AUTHOR]

Oil-free microturbomachinery (OFT) implements compliant foil bearings because of their minute drag and ability to operate in extreme (high or low) temperature. Prominent to date, bump-foil thrust bearings integrate an underspring thin metal structure that provides resilience and material damping, and while the rotor is airborne, it acts in series with the stiffness and damping of the gas film. The design and manufacturing of foil bearings remain costly as it demands extensive engineering and actual experience. Alternative foil bearing configurations, less costly and easier to manufacture, are highly desirable to enable widespread usage of OFT. This paper details the design and manufacturing of a novel Rayleigh-step metal mesh foil thrust bearing (MMFTB) as well as its testing on a dedicated rig. Metal mesh structures offer significant material structural damping and can be easily procured at a fraction of the cost of a typical bump-foil strip layer. The MMFTB consists of a solid carrier, a number of stacked annular copper mesh sheets (wire diameter = 0.25, 0.3, and 0.41 mm), and a steel top foil (0.127 mm thick) that makes six pads (ID = 50.8 mm, OD =2 ID), each 45 deg in extent. A 3 μm polymer coats each pad, and a photochemical process etches a step 20 μm in height. Static and dynamic load measurements (without rotor speed) demonstrate that the MMFTB has structural stiffness and material damping similar to that of a publicized bump-type foil thrust bearing. A maiden test of the MMFTB with rotor speed of Ω = 15 krpm (~80 m/s at bearing outer diameter (OD)) proved briefly the bearing operation when applying a tiny thrust load. Further tests with ambient air, a rotor speed of 40 krpm (~212 m/s at bearing OD), and a very light load/area <7 kPa failed several of the prototype bearings, all exhibiting significant wear on one or more pads. The source of the failure is the inherent unevenness of the metal mesh stacked substructures, thus causing the pads to bulge toward the rotor collar surface before a load applies. A deficient anchoring method exacerbates the unevenness. Incidentally, a high rotor speed induced large windage that lifted the top foils pushing them against the spinning collar. As the bearing moved toward the rotating collar to begin applying thrust, the local high spots rubbed against the collar before a hydrodynamic wedge could form to separate the surfaces. Without a robust sacrificial coating, metal-to-metal contact quickly disfigured the contacting top foil pads, erasing the etched step, and leading to failure. In concept, and on paper, the mesh sheets and the top foil lay flat against the bearing carrier, giving a false sense of uniformity in the design process. In actuality, a designer must consider the manufactured states of the individual components and how they assemble. A redesign of the bearing intends to overcome the existing flaws (highlighted herein) by incorporating a thicker top foil that is well anchored (to better withstand the effects of windage), a robust sacrificial coating, and a hydrodynamic wedge accomplished via a circumferential taper on each pad. [ABSTRACT FROM AUTHOR]

This paper provides a specific analysis strategy for tensegrity prism units with different complexities and connectivity. Through the nodal coordinate matrix and connectivity matrix, we can establish the equilibrium equation of the structure in the self-equilibrium state, and the equilibrium matrix can be obtained. The Singular Value Decomposition (SVD) method can find the self-equilibrium configuration. The torsional angle formula between the upper and bottom surfaces of the prismatic tensegrity structure, which includes complexity and connectivity, can be obtained through the SVD form-finding method. According to the torsional angle formula of the self-equilibrium configuration, we carry out the mechanical analysis of the single node, and the force density relationship between elements is gained. As one of the standards, the mass is used to evaluate the light structure. This paper also studied the minimal mass of the self-equilibrium tensegrity structure with the same complexity in different connectivity and got the minimal mass calculation formula. A six-bar tensegrity prism unit is investigated in this paper, which shows the feasibility of systematic analysis of prismatic structures. This paper provides a theoretical reference for prismatic tensegrity units. [ABSTRACT FROM AUTHOR]

This paper illustrates how particle size affects the initial yield stress of particle-reinforced composites. A formulation in a closed form is presented to demonstrate the size effect of yielding of the composites. This paper also demonstrates that there is an upper bound and a lower bound for the size-dependent yield stress with the change of particle size. This means that decreasing particle size increases its yield stress up to an upper bound. Similarly, increasing particle size decrease its yield stress up to a lower bound. In this paper the asymptotic homogenization method is used in framework of the Cosserat elasticity. A virtual "unreinforced matrix" is introduced as a reference configuration. As a numerical example, the size effect of yielding of Sicp/Al is predicted. [ABSTRACT FROM AUTHOR]

Considering the load uncertainty and unmodeled dynamics in multicylinder hydraulic systems, this paper proposes a balance control algorithm based on safe reinforcement learning to release the restrictions of classical model-based control methods that depend on fixed gain. In this paper, the hydraulic press is controlled by a trained agent that directly maps the system states to control commands in an end-to-end manner. By introducing an action modifier into the algorithm, the system states are kept within security constraints from the beginning of training, making safe exploration possible. Furthermore, a normalized exponential reward function has been proposed. Compared with a quadratic reward function, the precision is greatly improved under the same training steps. The experiment shows that our algorithm can achieve high precision and fast balance for multicylinder hydraulic presses while being highly robust. To the best of our knowledge, this research is the first to attempt the application of a reinforcement learning algorithm to multi-execution units of hydraulic systems. [ABSTRACT FROM AUTHOR]

Understanding fluid movement in multi-pored materials is vital for energy security and physiology. For instance, shale (a geological material) and bone (a biological material) exhibit multiple pore networks. Double porosity/permeability models provide a mechanics-based approach to describe hydrodynamics in aforesaid porous materials. However, current theoretical results primarily address steady-state response, and their counterparts in the transient regime are still wanting. The chief aim of this paper is to fill this knowledge gap. We present three principal properties--with rigorous mathematical arguments--that the solutions under the double porosity/permeability model satisfy in the transient regime: backward-in-time uniqueness, reciprocity, and a variational principle. We employ the "energy method"--exploiting the physical total kinetic energy of the flowing fluid--to establish the first property and Cauchy-Riemann convolutions to prove the next two. The results reported in this paper--qualitatively describe the dynamics of fluid flow in double-pored media--have (a) theoretical significance, (b) practical applications, and (c) considerable pedagogical value. In particular, these results will benefit practitioners and computational scientists in checking the accuracy of numerical simulators. The backward-in-time uniqueness lays a firm theoretical foundation for pursuing inverse problems in which one predicts the prescribed initial conditions based on data available about the solution at a later instance. [ABSTRACT FROM AUTHOR]

The following paper presents breakthrough experimental results for a new hermetic squeeze film damper (HSFD) concept that is integrally designed within an externally pressurized tilting-pad radial gas bearing support. The flexibly damped gas bearing module was designed for a 7.2 (183 mm) diameter shaft and fabricated using direct metal laser melting (DMLM); also known as additive manufacturing. The bearing and HSFD were sized based on ongoing studies for oil-free supercritical carbon dioxide (sCO2) power turbines in the 8.5 MW-10 MW power range. The development of the new damper concept was motivated by past dynamic testing on HSFD, which generated frequency-dependent stiffness and damping force coefficients. In efforts to eliminate the frequency dependency, a new HSFD architecture was conceived that adds accumulator volumes and a pass-through channel to previously conceived HSFD flow network designs. The other motivation for the work is the need to develop a cost-effective and reliable oil-free bearing technology that is scalable to large power turbomachinery applications. There were several objectives for the following work. The first objective was to successfully design and fabricate a single piece bearing-damper using additive manufacturing, while dimensionally controlling critical design features. The paper discusses the manufacturing steps and shows cut-ups that reveal adequate clearance control capability with internal damper clearances. The second objective was to perform experimental testing with the new HSFD design in efforts to extract stiffness and damping coefficients for excitation frequencies within 20-160 Hz and peak vibration amplitudes between 0.25 mils (6.35 microns) to 1 mil (25.4 microns). The test results for a single HSFD bearing module indicated that the design modifications to the HSFD architecture were successful in eliminating nearly all the frequency dependencies for the stiffness force coefficient. The dynamic tests yielded a stiffness coefficient that varied between 112 klb/in. (19.6 MN/m) and 96 klb/in. (16.8 MN/m). The damping force coefficient however, exhibited relatively more variation with frequency with values residing between 175 lb-s/in. (31 kN-s/m) to 214 lb-s/in. (37 kN-s/m). Finally, the paper advances a three-dimensional fluid-structure interaction (FSI) model using transient finite element analysis (FEA) coupled to a computational fluid dynamics (CFD) model. The FSI analysis performed between 20 Hz and 80 Hz was used to predict the stiffness and damping of the HSFD using a quarter-section model of the damper. The FSI analysis was able to support test results by showing only a 6-7.4% change in the magnitude of force coefficients. Stiffness predictions agree reasonably well with experiments whereas damping is underpredicted. [ABSTRACT FROM AUTHOR]

This paper presents a novel real-time kinematic simulation algorithm for planar N-bar linkage mechanisms, both single- and multi-degrees-of-freedom, comprising revolute and/or prismatic joints and actuators. A key feature of this algorithm is a reinterpretation technique that transforms prismatic elements into a combination of revolute joint and links. This gives rise to a unified system of geometric constraints and a general-purpose solver which adapts to the complexity of the mechanism. The solver requires only two types of methods--fast dyadic decomposition and relatively slower optimization-based--to simulate all types of planar mechanisms. From an implementation point of view, this algorithm simplifies programming without requiring handling of different types of mechanisms. This versatile algorithm can handle serial, parallel, and hybrid planar mechanisms with varying degrees-of-freedom and joint types. Additionally, this paper presents an estimation of simulation time and structural complexity, shedding light on computational demands. Demonstrative examples showcase the practicality of this method. [ABSTRACT FROM AUTHOR]

Design space exploration and margin analysis can inform critical decisions early in engineering design, helping to handle the uncertainties of early design while ensuring design performance. In practice, the complexity of many products makes such decision-making challenging. This paper addresses the challenge with a new design framework that relies on the margin value method to evaluate sets of concepts that are combinatorially generated from an enhanced function-means tree. The basis for concept comparison is the margin value in each design alternative. The margin value method is expanded to address a broad class of design problems by using surrogate models and novel metrics for evaluating different conceptual alternatives. Visualization tools are introduced to support the evaluations. The efficacy of the framework is demonstrated using the design of a structural aero-engine component involving simulation models and uncertain load specifications. Overall, this paper shows how design concepts can be compared objectively and distilled to a set of alternatives that would retain their values throughout product development. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for designing estimators to achieve consensus in uncertain multi-agent systems (MAS), even when various fault conditions are present and communication is assumed to be undirected and connected. The method includes an adaptive fault detection technique to detect faults and a unique adaptation in the unscented Kalman filter (UKF) to adjust noise covariance matrices and reconstruct uncertain states in the MAS is proposed in the framework of Q-learning. Additionally, it involves training neural network internal parameters using previous measurements. A Chebyshev neural network (CNN) is employed to model the uncertain plant, and a hyperbolic tangent-based robust control term is used to mitigate neural network approximation errors. This novel approach is known as reinforced UKF (RUKF). The paper also discusses the asymptotic stability of the proposed method and presents numerical simulations to demonstrate its effectiveness with reduced computational load. [ABSTRACT FROM AUTHOR]

In this paper, we address the following question: How can multi-robot self-organizing systems be designed so that they show the desired behavior and are able to perform tasks specified by the designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper, we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. The proposed framework consists of two stages, namely, preliminary design followed by design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to explore the design space and identify satisfactory solutions considering several performance indicators. Surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in the preliminary design. These surrogate models represent the goals of the cDSP. Our focus in this paper is to describe the framework. A multi-robot box-pushing problem is used as an example to test the framework's efficacy. This framework is general and can be extended to design other multi-robot self-organizing systems. [ABSTRACT FROM AUTHOR]

Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80% in using only 10% of the graphs, and iteratively built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25% of the graphs. [ABSTRACT FROM AUTHOR]

Many design problems involve reasoning about points in high-dimensional space. A common strategy is to first embed these high-dimensional points into a low-dimensional latent space. We propose that a good embedding should be isometric--i.e., preserving the geodesic distance between points on the data manifold in the latent space. However, enforcing isometry is non-trivial for common neural embedding models such as autoencoders. Moreover, while theoretically appealing, it is unclear to what extent is enforcing isometry necessary for a given design analysis. This paper answers these questions by constructing an isometric embedding via an isometric autoencoder, which we employ to analyze an inverse airfoil design problem. Specifically, the paper describes how to train an isometric autoencoder and demonstrates its usefulness compared to non-isometric autoencoders on the UIUC airfoil dataset. Our ablation study illustrates that enforcing isometry is necessary for accurately discovering clusters through the latent space. We also show how isometric autoencoders can uncover pathologies in typical gradient-based shape optimization solvers through an analysis on the SU2-optimized airfoil dataset, wherein we find an over-reliance of the gradient solver on the angle of attack. Overall, this paper motivates the use of isometry constraints in neural embedding models, particularly in cases where researchers or designers intend to use distance-based analysis measures to analyze designs within the latent space. While this work focuses on airfoil design as an illustrative example, it applies to any domain where analyzing isometric design or data embeddings would be useful. [ABSTRACT FROM AUTHOR]

Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Second, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network, to learn from both scalar vector graphics' curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens' location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury versus economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet's advantage of being a more explainable style-capturing agent. [ABSTRACT FROM AUTHOR]

Models of long-term product innovation depict the trajectory of products through an evolutionary selection metaphor in which product designs converge toward a dominant design. The product innovation literature favors trajectory descriptions based on the physical architecture of products while neglecting to account for the functional architecture. This paper offers a new way to explain the life cycle of product innovation by identifying motifs that describe a product's functions. Functional motifs are recurrent function blocks across multiple generations of designs for a product. A collection of functional motifs defines the functional architecture of the product. Using some key examples from innovations in sewing machines, the paper illustrates the occurrence of motifs as the basis for detecting the emergence of a dominant design. Patents related to the sewing machine over 177 years are analyzed to identify functional motifs characterizing the evolution and convergence toward a dominant design. Results show that motifs do not change over long periods once a dominant design emerges, even though components continue to change. This observation confirms a view of dominant designs as a technological frame but refutes the notion that design no longer matters in the era of incremental change. These motifs refine our understanding of how designs evolve along a particular path over the course of product innovation. [ABSTRACT FROM AUTHOR]

In engineering-design problems, usually, there are multiple goals with different units, continuous and discrete variables, nonlinear equations, nonconvex equations, and coupled decisions. Ideally, all goals' target are reached simultaneously within the feasible space. However, the optimal solution may not be available. To deal with all those complexities, a modeling strategy named "satisficing" was proposed in the 1980s. The satisficing strategy allows designers to find "good enough" but may not be optimal solutions. In this paper, we review the publications applying the satisficing strategy on engineering-design problems, and categorize the methods regarding the design stages they manage. We define the methods dealing with all four design stages--formulation, approximation, solution, and evaluation--as the whole process satisficing methods. We review the publications using the whole process satisficing strategy in great detail. In the past 30 years, the whole process satisficing strategy has been improved and applied to a wide variety of engineering-design problems, based on which derived methods, concepts, and platforms are developed. We generalize the specialties, advantages, and scope of applications of the methods in the whole process satisficing strategy. We expect this paper provides information on when and how designers may apply satisficing for their problems. [ABSTRACT FROM AUTHOR]

In this paper, a technique is developed for determining the nonstationary response statistics of linear oscillators endowed with fractional derivative elements. Notably, fractional operators are particularly effective in modeling solid mechanics problems as they offer the option of influencing both the elasticity and the energy dissipation capacity of the system. In this paper, particular attention is devoted to the case of fractional derivatives of rational order that approximates reasonably well any real order model. The oscillators are subjected to stationary stochastic excitations, and the pertinent nonstationary response statistical moments are determined by first introducing a finite number of oscillator response related states; this is afforded by the rational number order of the fractional operator. Next, the technique involves proceeding to treating the problem in the Laplace transform domain. This leads to multiple convolution integrals determined by representing the transfer function of the oscillator in a partial fraction form by a pole-residue formulation. In this manner, the response evolutionary power spectral density of the fractional oscillator is derived in a closed form, while nonstationary second-order statistics can be obtained by mundane numerical integration in the frequency domain. Applications to oscillators comprising one or two fractional derivative elements are presented, considering the case of a white noise excitation and of a random process possessing the classical Kanai-Tajimi spectrum. Reliability of the developed technique is assessed by juxtaposing its analytical results with pertinent Monte Carlo simulation data. [ABSTRACT FROM AUTHOR]

Robotic devices are capable of reducing the physical burden on rehabilitation therapists and providing training programs of good repeatability, high efficiency, and high precision. When designing the kinematic structure for rehabilitation robots, there has been a growing interest toward one-degree-of-freedom (DOF) end-effector mechanisms due to their simpler structure and less complicated control algorithms. Compared with current one-DOF mechanism designs that are mainly customized for multi-joint robotic training, spherical coupled serial chain (SCSC) mechanisms are proposed in this paper to specifically deliver the single-joint robotic training, which is of equal importance to the effective physical recovery. Using kinematic mapping theory, the end-effector motion of SCSC mechanisms can be naturally transformed to two trigonometric curves composed of finite Fourier series in two separate planes of the image space. This novel formulation helps to establish an analytical and direct relationship between the design parameters of the SCSC mechanism and the harmonic parameters of the image-space representation of the task rehabilitation motion. The result is a simple and effective method for kinematic synthesis of SCSC mechanisms for generation of single-joint motion with an arbitrary number of spherical poses. An example of designing SCSC mechanisms for shoulder-joint rehabilitation is presented at the end of this paper to illustrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper develops a method to integrate user knowledge into the optimization process by simultaneously modelling feasible design space and optimizing an objective function. In engineering, feasible design space is a constraint similar to those in optimization problems. However, not all constraints can be explicitly written as mathematical functions. This includes manufacturing concerns, ergonomic issues, complex geometric considerations, or exploring material options for a particular application. There needs to be a way to integrate designer knowledge into the design process and, preferably, use that to guide an optimization problem. In this research, these constraints are modeled using classification surrogate models and incorporated with Bayesian optimization. By suggesting design options to a user and allowing them to box off areas of feasible and infeasible designs, the method models both the feasible design space and an objective function probability of new design targets that are more optimal and have a high probability of being feasible. This proposed method is first proven with test optimization problems to show viability then is extended to include user feedback. This paper shows that by allowing users to box off areas of feasible and infeasible designs, it can effectively guide the optimization process to a feasible solution. [ABSTRACT FROM AUTHOR]

3D/4D printing offers significant flexibility in manufacturing complex structures with a diverse range of mechanical responses, while also posing critical needs in tackling challenging inverse design problems. The rapidly developing machine learning (ML) approach offers new opportunities and has attracted significant interest in the field. In this perspective paper, we highlight recent advancements in utilizing ML for designing printed structures with desired mechanical responses. First, we provide an overview of common forward and inverse problems, relevant types of structures, and design space and responses in 3D/4D printing. Second, we review recent works that have employed a variety of ML approaches for the inverse design of different mechanical responses, ranging from structural properties to active shape changes. Finally, we briefly discuss the main challenges, summarize existing and potential ML approaches, and extend the discussion to broader design problems in the field of 3D/4D printing. This paper is expected to provide foundational guides and insights into the application of ML for 3D/4D printing design. [ABSTRACT FROM AUTHOR]

This paper discusses a study performed by SoftInWay as part of a Phase II SBIR project funded by NASA. In contrast with the Phase I project (Burlaka and Moroz, 2023, "Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence," ASME J. Eng. Gas Turbines Power, 145(1), p. 011001) where three discrete compressors were considered, the Phase II study was focused on addressing the problem of axial compressor long development time and cost with the use of AI models capable of predicting the geometry and performance of various multistage axial compressors with multiple variable vanes. The applicability of the AI models to various compressors enables the opportunity to avoid iterations between engine cycle analysis and compressor design. In this paper, automated compressor design and performance generation workflows are described. The approach for autonomous selection of the architectures and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values and representations of typical geometry and performance predictions are given. The utilization of the ML models in engine cycle analysis is discussed. [ABSTRACT FROM AUTHOR]

Subsea centrifugal compressors commonly using labyrinth seals (LS) and pocket damper seals (PDS) must withstand the flow of oil in gas mixtures with a liquid volume fraction (LVF) up to 5%. This paper presents experimental results for the leakage and rotordynamic coefficients of a uniform clearance PDS and a similar size LS both supplied with wet gas. The shaft diameter D = 127 mm and the seal axial length L = 48 mm. In the tests, the seals' pressure ratio (inlet/exit) = 2.5, and the shaft speed reaches 5.25 krpm (surface speed = 35 m/s). The LS has a 17% larger radial clearance (Cr, LS = 0.230 mm > Cr, PDS = 0.196 mm) due to a manufacturing error. This paper extends prior work by Torres et al. (2022, "A Stepped Shaft Labyrinth Seal versus a Pocket Damper Seal: Leakage and Dynamic Force Coefficients Under Wet Gas Operation," ASME J. Eng. Gas Turbines Power, 145(1), p. 011006) for the same two seal types with a stepped clearance configuration that promotes damping. When supplied with a dry gas, the LS leaks more because of its larger clearance. A loss coefficient (cd) is a fraction of the physical clearance that characterizes a seal's effectiveness in reducing leakage. The cd of both seals is nearly identical for operation with pure gas; the small differences are well within the experimental uncertainty. For operation with wet gas, the PDS cd decreases as the LVF increases whereas the LS cd increases, thus indicating the PDS is more effective to restrict wet gas leakage. When operating with pure gas, the direct stiffness (K) and effective damping (Ceff) of both seals are small in magnitude (K < 0.5 MN/m, Ceff < 2 kN-s/m) and often less than the experimental uncertainty. For wet gas operation and with shaft speed = 3 krpm and 5.25 krpm, the PDS produces Ceff < 0 for whirl frequencies below 50 Hz. In contrast, the LS Ceff > 0, although small in magnitude. The experimental results under wet gas operation are similar for operation with LVF equal to 3% and 5%. Unexpected low-frequency broadband motions appear when supplying the PDS with a wet gas. Although small in amplitude (<5 µm), the motions increase in severity as the mixture inlet LVF and shaft speed increase. The motions are entirely absent for tests with the LS. Experiments in which the mixture is drawn from the PDS cavities rule out liquid accumulation as the cause of the observed motions. The current test results serve as a reference for turbomachinery design engineers and aid in the validation of analytical predictive tools. [ABSTRACT FROM AUTHOR]

Current U.S. government policy seeks to achieve a completely carbon-free economy by 2050, with a carbon-free electricity sector by 2035 (per executive orders #14008 and #14057). To address these goals, the U.S. Department of Energy is evaluating strategies and technologies that support the production, utilization, transport, and storage of hydrogen (via initiatives such as Department of Energy's (DOE) Energy Earthshot--Hydrogen and various DOE funding opportunity announcements). A carbon-free fuel such as hydrogen cannot be overvalued in a dynamic electric energy sector seeking to decarbonize. One of the most important technologies needed to achieve the goal of a carbon-free electricity sector is a 100% hydrogen-fueled gas turbine. Accommodating hydrogen-based fuels has been a key goal for various original engine manufacturers (OEMs) for many years, but much more research and development (R&D) is needed. The purpose of this paper is to highlight the current state-of-the-art of hydrogen turbine technology, especially regarding nitrogen oxide (NOX) emissions compared to natural gas-fueled turbines. NOX is the primary criteria pollutant from thermally driven combustion turbines and should be controlled to levels that are equivalent to or below existing standards (as reported "existing standards" for hydrogen-fueled gas turbines may need to be rebaselined). This paper will provide an overview of hydrogen as a fuel and various NOX emissions control techniques that are relevant for hydrogen-based fuels. A conclusion from this overview is that, with some level of R&D, NOX emissions from hydrogen-fueled gas turbines can be controlled to levels similar to those produced by state-of-the-art (SOTA) natural gas-fueled combustion turbines while remaining competitive in terms of performance and efficiency. [ABSTRACT FROM AUTHOR]

Bioprinting using algae cells has many potential applications including tissue engineering, environmental engineering, contaminant removal from water, and establishing space habitats. In extrusion-based bioprinting, bioink needs to be crosslinked after being extruded from the nozzle for printed constructs to first achieve and then maintain adequate shape fidelity. Crosslinking methods used in reported studies on algae-contained bioinks include both photo-crosslinking and ionic crosslinking. This paper reports a preliminary study where the coaxial nozzle-based in situ ionic crosslinking method was used in bioprinting of algae cells without additional crosslinking of printed samples for the first time. In comparison with photo-crosslinking, in situ ionic crosslinking can minimize bioink preparation time and complexity, eliminate cells' exposure to ultraviolet radiation, and reduce the number of post-printing steps. In this preliminary study, the bioink was an alginate solution containing algae (Chlorella vulgaris) cells, and the crosslinking solution was a calcium chloride solution. The coaxial nozzle had two nozzles: inner and outer nozzles. In printing, the bioink was delivered through the outer nozzle while the crosslinking solution was delivered through the inner nozzle. The shape of the printed samples was a square block with dimensions of 30 × 30 × 10 mm. It was observed that, 9 days after printing, the algae cells grew within the printed samples, and the samples could keep their shapes relatively well. Many knowledge gaps exist regarding the effects of input variables in bioprinting of algae cells using this method. This paper discusses future research directions to fill these knowledge gaps. [ABSTRACT FROM AUTHOR]

Design education has traditionally relied heavily on physical integration as it involves a lot of hands-on work, group critiques, and collaborative projects, but the COVID-19 pandemic has fundamentally shifted the way teaching is done, which resulted in many institutions adapting to remote teaching and learning environments. This has created challenges for design educators who have had to find ways to evaluate students' progress in the absence of in-person interactions. In this paper, we are proposing a dashboard visualization approach that helps educators monitor the progression of the entire class of students using artificial intelligence (AI) by tracking a time-based evolution of a design statement. This approach uses various natural language processing (NLP) models to produce stock-like charts, which represent students' and student groups' progression through a series of divergence and convergence phases. These charts become a form of design artifact that allows educator(s) to gain a bird's-eye view of the class and react to groups that may require assistance; at the same time, it becomes a qualitative means of evaluation and comparison across students and groups. Toward the end, this paper also showcases a web-based platform that is publicly available using such methodology, a case study that applied so methodology and recommendations of future works possible. [ABSTRACT FROM AUTHOR]

Digital tools for sketching, such as tablets, have become popular for streamlining design work and keeping a large quantity of sketches in one place. However, their impact on design creativity, novelty, and concept evolution is not yet well understood. Here, we present a controlled human subjects study that assesses the influence of tablets (iPads) on concept novelty and evolution in the context of an engineering design concept generation exercise. We expect that iPad use will not influence concept novelty due to its similar speed of use as pen and paper sketching. We expect to see different patterns in concept evolution between the two types of tools, namely, that iPad users will demonstrate more iteration on a concept (concept evolution) than pen and paper users due to the fact that iPad features make it easy to copy and paste previous sketches and then modify them. We find that the tool used is not correlated with concept novelty. Additionally, we find no strong differences in overall concept evolution quantities between the two tools, though we see that iPad sketches exhibited more cases of consecutive concept evolution than nonconsecutive whereas paper and pen sketches showed an equal amount of both consecutive and nonconsecutive concept evolution. Results indicate that overall, iPads may not significantly inhibit designers' creative skills and thus could be a reasonable replacement for pen and paper sketching, which has implications for both design education and practice. [ABSTRACT FROM AUTHOR]

In the coming years, decentralized power generation systems with renewables are expected to take a leading role, and micro gas turbines will serve as backup sources to compensate for times of low inputs from other sources. In order to deal with the unpredictable energy inputs from renewables, the micro gas turbine must be capable of running under varying load conditions and making fast transitions between them. The operation of a micro gas turbine in an integrated microgrid (MG) has the potential to reduce operational costs and ensure the delivery of demanded heat and power to consumers. This paper investigates the operation of a micro gas turbine in a MG, serving as a supplementary power source for a municipal building. The building's required energy is initially provided by wind turbine power, and the micro gas turbine serves as a backup source during times of wind power deficiency. The micro gas turbine can operate using a natural gas/hydrogen fuel blend ranging from zero to 100% hydrogen. Furthermore, a water electrolyzer with a hydrogen tank is available to operate as a storage system within the MG. The study's results demonstrate the economic and environmental benefits of using hydrogen storage and optimizing operational planning in the MG. The primary objective of the paper is to highlight the feasibility and benefits of employing micro gas turbines and hydrogen storage systems within a MG as a renewable energy backup power source. [ABSTRACT FROM AUTHOR]

Origami structures, which were inspired by traditional paper folding arts, have been applied for engineering problems for the last two decades. Origami-based thin-wall tubes have been extensively investigated under axial loadings. However, less has been done with radial stiffness as one of the critical mechanical properties of a tubular structure working under lateral loadings. In this study, the radial stiffness of novel thin-wall tubular structures based on origami patterns have been studied with compression tests and finite element analysis (FEA) simulations. The results show that the radial stiffness of an origami-inspired tube can achieve about 27.1 times that of a circular tube with the same circumcircle diameter (100 mm), height (60 mm), and wall thickness (2 mm). Yoshimura, Kresling, and modified Yoshimura patterns are selected as the basic frames, upon which the influences of different design parameters are tested and discussed. Given that the weight can vary due to different designs, the stiffness-to-weight ratio is also calculated. The origami-inspired tubular structures with superior stiffness performances are obtained and can be extended to crashworthy structures, functional structures, and stiffness enhancement with low structural weight. [ABSTRACT FROM AUTHOR]

Fluid injection is a common practice in the oil and gas industry found in many applications such as waterflooding and disposal of produced fluids. Maintaining high injection rates is crucial to guarantee the economic success of these projects; however, there are geomechanical risks and difficulties involved in this process that may threat the viability of fluid injection projects. Near wellbore reduction of permeability due to pore plugging, formation failure, out of zone injection, sand production, and local compaction are challenging the effectiveness of the injection process. Due to these complications, modeling and simulation has been used as an effective tool to assess injectors' performance; however, different problems have yet to be addressed. In this paper, we review some of these challenges and the solutions that have been proposed as a primary step to understand mechanisms affecting well performance. [ABSTRACT FROM AUTHOR]

Over the past few decades, folding paper has extended beyond the origami deployable applications to reach the engineering field. Nevertheless, mechanical information about paper behavior is still lacking, especially during folding/unfolding. This article proposes an approach to characterize the paper fold behavior in order to extract the material data that will be needed for the simulation of folding and to go a step further the single kinematics of origami mechanisms. The model developed herein from simple experiments for the fold behavior relies on a macroscopic local hinge with a nonlinear torsional spring. Though validated with only straight folds, the model is still applicable in the case of curved folds thanks to the locality principle of the mechanical behavior. The influence of both the folding angle and the fold length is extracted automatically from a set of experimental values exhibiting a deterministic behavior and a variability due to the folding process. The goal is also to propose a methodology that may extend the simple case of the paper crease, or even the case of thin material sheets, and may be adapted to other identification problems. [ABSTRACT FROM AUTHOR]

Variational integrators play a pivotal role in the simulation and control of constrained mechanical systems. Current Lagrange multiplier-free variational integrators for such systems are concise but inevitably face issues with parameterization singularities, hindering global integration. To tackle this problem, this paper proposes a novel method for constructing variational integrators on manifolds without introducing Lagrange multipliers, offering the benefit of avoiding singularities. Our approach unfolds in three key steps: (1) the local parameterization of configuration space; (2) the formulation of forced discrete Euler-Lagrange equations on manifolds; and (3) the construction and implementation of high-order variational integrators. Numerical tests are conducted for both conservative and forced mechanical systems, demonstrating the excellent global energy behavior of the proposed variational integrators. [ABSTRACT FROM AUTHOR]

In this paper, we develop a nonlinear mixed finite element method for flexoelectric semiconductors and analyze the mechanically tuned redistributions of free carriers and electric currents through flexoelectric polarization in typical structures. We first present a macroscopic theory for flexoelectric semiconductors by combining flexoelectricity and nonlinear drift-diffusion theory. To use C0 continuous elements, we derive an incremental constrained weak form by introducing Langrage multipliers, in which the kinematic constraints between the displacement and its gradient are guaranteed. Based on the weak form, we established a mixed C0 continuous nine-node quadrilateral finite element as well as an iterative process for solving nonlinear boundary-value problems. The accuracy and convergence of the proposed element are validated by comparing linear finite element method results against analytical solutions for the bending of a beam. Finally, the nonlinear element method is applied to more complex problems, such as a circular ring, a plate with a hole, and an isosceles trapezoid. Results indicate that mechanical loads and doping levels have distinct influences on electric properties. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of integral sliding mode disturbance observer (ISDOB)-based preview repetitive control (PRC) for a class of nonlinear systems subject to external disturbances and nonlinearity. First, an ISDOB-based PRC law is presented to estimate and compensate the exogenous disturbances and nonlinearity. Next, by using a new equality constraint, a continuous-discrete two-dimensional (2D) system is established. Then, the stability of the combined controller-observer system is derived on the basis of a Lyapunov analysis, 2D system theory, and the singular-value-decomposition technique. By solving an linear matrix inequality (LMI), the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, a class of adaptive schemes has been developed for rejecting sinusoidal output disturbances with unknown frequencies, phases, and amplitudes. These adaptive schemes require an accurate model of the affected system's dynamics to function, and the controller performance degrades as the modeling error increases. While linear time-invariant (LTI) dynamics can adequately model the dynamics of some systems, a time-varying model of the system dynamics is more appropriate in certain scenarios. This paper proposes a robust adaptive scheme for rejecting disturbances that affect the output of a system described by a known linear time-varying model. The disturbances are described by an unknown number of sinusoidal terms with unknown time-varying frequencies. Analysis is done in ideal case where the disturbance frequencies are perfectly known to provide conditions guaranteeing the existence of a controller and a corresponding set of parameters which reject the disturbance. Analysis is then done in the more realistic adaptive scenario where the disturbance frequencies are unknown, and a robust adaptive scheme for selecting controller parameters which reject the disturbance is proposed. Performance of the controller with respect to the speed of variation in the plant model parameters is assessed via simulation. [ABSTRACT FROM AUTHOR]

Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively. [ABSTRACT FROM AUTHOR]

In many nonlinear systems, the observability of the system is dependent on its state and control input. Thus, incorporating observability into a control scheme can enhance an observer's ability to recover accurate estimates of unmeasured states, minimize estimation error, and ultimately, allow the original control objective to be achieved. The accommodation of observability, however, may conflict with the original control goal at times. In this paper, we propose the use of control barrier functions (CBFs) to enforce observability and thereby facilitate the convergence of the state estimate to the true state while accommodating the original control objectives. Motivated by practical applications for autonomous robots operating in global positioning system-denied environments, we focus on the problem of target tracking for a unicycle model when only the distance to the target is measured. The proposed approach is compared in simulation with a model predictive control (MPC) approach that treats an observability-related metric as part of the cost function, where several different options for the observability metric are explored. It is found that the CBF-based approach achieves control and estimation performance that is comparable to that of the MPC approach, but with significantly less computational complexity. These findings are further experimentally verified in range-based target tracking with a swimming robotic fish. [ABSTRACT FROM AUTHOR]

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology. [ABSTRACT FROM AUTHOR]

This paper describes an efficient method of manufacturing thin-walled metal tubes. The proposed method is an expansion drawing and it consists of two stages, flaring and drawing, during which high thickness reduction is achieved because the tube being processed is subjected to tensile stress in both the circumferential and axial directions. The optimal plug half-angle for various materials is investigated using a finite element analysis and experiments, revealing the plug shape with the highest expansion limit and the highest thickness reduction ratio. To investigate the deformation characteristics of different materials during the expansion drawing process, the strain in tubes formed using different materials was measured experimentally using a square grid on the outer surface of the tubes. In addition, a finite element analysis using hypothetical material properties revealed that the work-hardening exponent and friction coefficient affect tube deformation. The results of this work explain the influences of material properties on experimentally observed phenomena. Materials with a large work-hardening exponent are less prone to thinning during forming and have a greater forming limit. On the other hand, the larger the friction coefficient between the plug and the mother tube, the larger the deformation in the axial direction was, and then the thickness reduction increased due to biaxial tension. [ABSTRACT FROM AUTHOR]

In practical semiconductor processes, the defect analysis for wafer map is a critical step for improving product quality and yield. These defect patterns can provide important process information so that the process engineers can identify the key cause of process anomalies. However, in supervised learning, the manual annotation for wafer maps is an extremely exhausting task, and it can also induce misjudgment when a long-term operation is implemented. Toward this end, this paper proposes a new auto-labeling system based on ensemble classification. The noted VGG16 model is used in ensemble learning as the building block to train the classifier via a limited number of labeled data. Through the model being trained, the auto-labeling procedure is executed to annotate abundant unlabeled data. Therefore, the classification performances between the models trained by supervised and semi-supervised learning can be compared. In addition, the gradient-weighted class activation mapping (Grad-CAM) is also adopted to analyze and verify the quality of auto-labeling by visual inspection. Based on the experimental results, the proposed auto-label system can return a satisfactory classification performance, and then, the manual labeling operation can be drastically reduced. The classification performance for wafer defect patterns can be further assured as the auto-labeled data are given with corresponding confidence scores of specific defect patterns being identified in this study. [ABSTRACT FROM AUTHOR]

This paper introduces a novel wafer-edge quality inspection method based on analysis of curved-edge diffractive fringe patterns, which occur when light is incident and diffracts around the wafer edge. The proposed method aims to identify various defect modes at the wafer edges, including particles, chipping, scratches, thin-film deposition, and hybrid defect cases. The diffraction patterns formed behind the wafer edge are influenced by various factors, including the edge geometry, topography, and the presence of defects. In this study, edge diffractive fringe patterns were obtained from two approaches: (1) a single photodiode collected curved-edge interferometric fringe patterns by scanning the wafer edge and (2) an imaging device coupled with an objective lens captured the fringe image. The first approach allowed the wafer apex characterization, while the second approach enabled simultaneous localization and characterization of wafer quality along two bevels and apex directions. The collected fringe patterns were analyzed by both statistical feature extraction and wavelet transform; corresponding features were also evaluated through logarithm approximation. In sum, both proposed wafer-edge inspection methods can effectively characterize various wafer-edge defect modes. Their potential lies in their applicability to online wafer metrology and inspection applications, thereby contributing to the advancement of wafer manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper develops a theoretical basis and a systematic process for resolving all inertia forces along generalized coordinates from the overall energy equation of a dynamical system. The theory is developed for natural systems with scleronomic constraints, where the potential energy is independent of generalized velocities. The process involves expansion of the energy equation, and specifically a special expansion of the kinetic energy term, from which the inertia forces emerge. The expansion uses fundamental kinematic identities of the phase space. It is also guided by insights drawn from the structure of the Hamiltonian function. The resulting equation has the structure of the D'Alembert's equation but involving generalized coordinates, from which the Lagrange's equations of motion are obtained. The expansion process elucidates how certain inertia forces manifest in the energy equation as composite terms that must be accurately resolved along different generalized coordinates. The process uses only the system energy equation, and neither the Hamiltonian nor the Lagrangian function are required. Extension of this theory to non-autonomous and non-holonomic systems is an area of future research. [ABSTRACT FROM AUTHOR]

The stability of a current-carrying beam in an external magnetic field is studied in this paper. The beam rests on periodic supports and is modeled as an Euler-Bernoulli elastic beam. Based on the linearized stability equation and propagator transfer matrix approach for a multi-span finite-length beam, the stability equation and solutions of the several boundary value problems are obtained. While the Floquet-Bloch theory is widely used in the dynamic problem of phononic and photonic structure conditions, its application to the solution of infinite length beam static stability problem is novel. The stability values of the periodic infinite beam model are in very good agreement with one of the finite-length multi-span beams. [ABSTRACT FROM AUTHOR]

The post-buckling behavior of a beam that is subjected to lateral constraints is of relevance to a range of medical and engineering applications, such as endoscopic examination of internal organs, the insertion of a guidewire into an artery in-stent procedures, root growth, deep-drilling, and more. In this paper, we address a disconnect between the existing literature and the reality of these systems, in which the lateral constraints are flexible and experience nonlinear deformations. As a step towards bridging this gap, we consider a beam undergoing planar deformations that is laterally constrained by a nonlinear springy wall, i.e., a wall that is laterally pushed by the beam against a nonlinear spring. Based on a simplified mathematical model, we obtain closed-form analytical solutions, which provide valuable insights and intuition. For example, we show that important features of the behavior, such as the transition from point contact to line contact and switching to the next mode, are dictated solely by a non-dimensional force, regardless of all other parameters of the system, and that the full description of the behavior is possible by means of two non-dimensional quantities that describe the relative stiffness of the nonlinear spring compared to that of the beam. The results also highlight the fundamental differences between the behavior with a stiffening spring or with a softening spring, such as the number of attainable modes and the monotonicity of the overall force-displacement relation. These results are then validated by experiments. [ABSTRACT FROM AUTHOR]