Non-rigid Materials Research Articles

The Dilemma of Reproducibility of Gating Isotherms for Flexible MOFs.

Porous materials receive a high level of scientific and technological interest due to their applications in various fields such as adsorption, separation and storage, catalysis, ion exchange, nanotechnology, etc. Gas adsorption is a well-established tool for the characterization of the texture of porous solids. Physisorption isotherms are generally expected to be well reproducible for rigid adsorbents, but this is not always the case for nonrigid (flexible) materials. The presence of a metastability region and sensitivity of the activation barriers to the material's texture often influence the isotherms' run. Here, we address the complexity that arises in terms of reproducibility and sample handling for flexible metal-organic frameworks, with the example of DUT-8(Ni). It belongs to the group of "gate opening" metal-organic frameworks and is a typical representative of the pillared layer compounds. We propose characteristic parameters for the analysis and comparison of adsorption isotherms, showing the "gate opening" step, associated with the adsorption-induced solid-state phase transition. A set of 50 nitrogen physisorption isotherms measured at 77 K were analyzed and correlated with the synthetic and outgassing conditions. The study highlights the importance of accurate descriptions and record-keeping of experimental details and their role in the replication of scientific results.

Chest wall reconstruction in benign and malignant tumors with non-rigid materials: An overview.

Several materials and techniques have been described for the procedure of chest wall reconstruction: the choice of using a technique or a material over another relies mainly on the surgeon's experience as well as thoracic defect localization and dimension, local availability of materials, and costs. From a technical point of view, autologous and alloplastic reconstruction are available, and, in both cases, rigid and non-rigid prostheses are found. Each material has its peculiarities, with advantages and disadvantages; thus, it is mandatory to be confident when planning the intervention to foresee possible complications and minimize them. We have reviewed the literature on chest wall reconstruction in chest wall tumors (both malignant and non malignant) with non-rigid prosthetic materials, focusing on safety outcomes.

Perceived movement of nonrigid motion patterns.

Nonrigid materials such as liquids or smoke deform over time. Little is known about the visual perception of nonrigid motion other than that many motion cues associated with rigid motion perception are not reliable for nonrigid motion. Nonrigid motion patterns lack clear borders and their movement can be inconsistent with the motion of their parts. We developed a novel stimulus that creates a nonrigid vortex motion pattern in a random dot distribution and decouples the movement of the vortex from the first-order motion of the dots. We presented three moving vortices that entailed consecutively fewer motion cues, eliminating occlusion, motion borders, and velocity field gradients in the process. Subjects were well able to report the end position and travel path in all cases, showing that nonrigid motion is perceived through an analysis of the temporal evolution of visual motion patterns and does not require borders or speed differences. Adding a coherent global motion did not hamper perception, but adding local noise did, indicating that the visual system uses mid-level features that are on a local scale. We also found that participants judged the movement of the nonrigid motion patterns slower than a rigid control, revealing that speed perception was based on a combination of motion of the parts and movement of the pattern. We propose that the visual system uses the temporal evolution of a motion pattern for the perception of nonrigid motion and suggest a plausible mechanism based on the curl of the motion field.

Towards robust human-robot mobile co-manipulation for tasks involving the handling of non-rigid materials using sensor-fused force-torque, and skeleton tracking data

Over the past decades, robots have been extensively deployed in multiple industries. More recently, industrial robots have been taken out of their cages, being more present in dynamic, uncertain environments interacting in the vicinity of humans. Traditionally, robots have been mainly developed to perform pre-programmed tasks. However, some tasks are too complex or expensive to be performed by a robotic system alone. One of these hard tasks is the handling of large fibre sheets in composite part production. This paper presents a force-based approach towards human-robot co-manipulation of tasks involving the handling of non-rigid materials, such as composite fibres. Our approach fuses the data of a force-torque sensor and skeleton tracking data from a 2D camera to control the mobile manipulator in an intuitive manner, by using the intelligence of the operator as much as possible. By using this approach, tools such as path planning, high-level task planning, or modelling of objects to be manipulated are not essential to obtain the results. The overall approach is illustrated with a co-manipulation proof-of-concept experiment in which a mobile manipulator robot handles a flexible textile sheet together with a human operator.

Dynamic dot displays reveal material motion network in the human brain

There is growing research interest in the neural mechanisms underlying the recognition of material categories and properties. This research field, however, is relatively more recent and limited compared to investigations of the neural mechanisms underlying object and scene category recognition. Motion is particularly important for the perception of non-rigid materials, but the neural basis of non-rigid material motion remains unexplored. Using fMRI, we investigated which brain regions respond preferentially to material motion versus other types of motion. We introduce a new database of stimuli – dynamic dot materials – that are animations of moving dots that induce vivid percepts of various materials in motion, e.g. flapping cloth, liquid waves, wobbling jelly. Control stimuli were scrambled versions of these same animations and rigid three-dimensional rotating dots. Results showed that isolating material motion properties with dynamic dots (in contrast with other kinds of motion) activates a network of cortical regions in both ventral and dorsal visual pathways, including areas normally associated with the processing of surface properties and shape, and extending to somatosensory and premotor cortices. We suggest that such a widespread preference for material motion is due to strong associations between stimulus properties. For example viewing dots moving in a specific pattern not only elicits percepts of material motion; one perceives a flexible, non-rigid shape, identifies the object as a cloth flapping in the wind, infers the object's weight under gravity, and anticipates how it would feel to reach out and touch the material. These results are a first important step in mapping out the cortical architecture and dynamics in material-related motion processing.

ON PROTOTYPING METHODS TO LEVERAGE NON-RIGID MATERIALS IN THE EARLY STAGES OF ENGINEERING DESIGN

AbstractPrototyping has been shown important to facilitate learning, inform decisions and to communicate ideas in engineering design. However, it is not evident which methods, tools and materials to use, as prototyping is practised differently across development contexts, and stages. In the early stages of design, different choices in prototyping methods, tools and materials all affect prototyping outcome. This paper is focused on prototyping methods in the context of early stages of design and attempts to highlight identified strengths and limitations of using non-rigid materials for prototyping.

The Four-Pole Transfer Matrices of an Elastic Porous Layer

Relations are proposed for the transfer matrices of linear four-poles that relate the pressure and normal velocity components at free and/or impervious boundaries of a planar elastic porous layer. The dependences were obtained within the irrotational model framework, which assumes a negligible effect of shear stresses on the transfer of wave disturbances across the layer. The possibility of using the results for layers of nonrigid porous materials in a wide range of sound-wave angles of incidence is shown.

Heat-free printing method yields metal interconnects

Researchers have exploited the unusual phase-change behavior of a liquid metal to devise heat-free procedures for printing metal patterns on numerous temperature-sensitive materials, including Jell-O (Adv. Funct. Mater. 2019, DOI: 10.1002/adfm.201903687). The team used the methods to make metallic connections for circuits on nonrigid materials, a fabrication step needed for developing wearable electronics and flexible, foldable displays. Electronic devices that bend and stretch like athletic wear could lead to new applications in sports, medicine, and robotics. But traditional circuitry fails when it’s flexed, and nontraditional materials that would be used in flexible electronics, such as thin fabrics, paper, and skin, are incompatible with the high-temperature steps typically used for fabricating circuits. Aiming to bypass those shortcomings, Andrew Martin, Martin Thuo, and coworkers at Iowa State University turned to liquid-metal droplets encapsulated in a nanometer-thin oxide skin. Made of Field’s metal, a bismuth-indium-tin alloy that melts at 62 °C, the droplets

Tactile Sensor for Manipulation of Deformable Object

The variable physical property of deformable objects, which are very flexible, soft and viscoelastic, causes the design of reliable automated handling system relatively difficult. In fact, most of these objects tend to be handled manually during the handling process. Therefore, a new optical tactile sensor for an intelligent handling of the non-rigid materials is presented in this paper. Mathematical modelling and control algorithm are developed and the tactile sensor is calibrated in this research. Based on the results that have been recorded, the surface characterization with the respect to normal force applied to the object is attained. A gripper handling system is used to accommodate variable physical properties of the deformable materials, which are very flexible, soft and viscoelastic. In addition to that, the gripper needs to handle the materials with the minimum deformation so that less distortion, and higher accuracy of manipulation can be achieved. Efficient and accurate modelling of deformations is crucial for grasping analysis.

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

Shatter and splatter: The contribution of motion and surface optics to the perception of non-rigid materials

Shatter and splatter: The contribution of motion and surface optics to the perception of non-rigid materials

Nanostructured functional materials for advanced three-dimensional (3D) solar cells

Advancements in dye-sensitized solar cell (DSSC) technology are occurring at an ever-increasing rate, as the development of novel carbon-based materials, the increasing research into new 3D surface morphologies and cell design, and the focus on the development of new sensitizers and electrolytes have allowed many new possibilities for DSSCs. Solar cells that are three-dimensionally structured offer significant advantages over traditional crystalline / semi-crystalline panels in that they can convert incident photons that strike them at large incident angles, can be flexible / used in applications which require non-rigid materials, and can be substantially cheaper to produce than traditional panels, especially with the replacement of more expensive, traditional electrode materials by carbon materials in the working / counter electrode. The use of carefully selected and engineered sensitizers like quantum dots with these three-dimensionally structured solar cells have seen them achieve ever-increasing power conversion efficiencies, and it's likely that they will soon rival traditional crystalline / semi-crystalline panels for both mass power generation and use in more niche applications such as flexible photovoltaic textile fibers. This review covers DSSCs constructed with several different materials, and the advantages and disadvantages of a variety of cell designs.

Image deformation as a cue to material category judgment

Human observers easily recognize complex natural phenomena, such as flowing water, which often generate highly chaotic dynamic arrays of light on the retina. It has not been clarified how the visual system discerns the source of a fluid flow. Here we show that the magnitude of image deformation caused by light refraction is a critical factor for the visual system to determine the perceptual category of fluid flows. Employing a physics engine, we created computer-rendered scenes of water and hot air flows. For each flow, we manipulated the rendering parameters (distortion factors and the index of refraction) that strongly influence the magnitude of image deformation. The observers rated how strongly they felt impressions of water and hot air in the video clips of the flows. The ratings showed that the water and hot air impressions were positively and negatively related to the magnitude of image deformation. Based on the results, we discuss how the visual system heuristically utilizes image deformation to discern non-rigid materials such as water and hot air flows.

Shape, motion, and optical cues to stiffness of elastic objects.

Nonrigid materials, such as jelly, rubber, or sponge move and deform in distinctive ways depending on their stiffness. Which cues do we use to infer stiffness? We simulated cubes of varying stiffness and optical appearance (e.g., wood, metal, wax, jelly) being subjected to two kinds of deformation: (a) a rigid cylinder pushing downwards into the cube to various extents (shape change, but little motion: shape dominant), (b) a rigid cylinder retracting rapidly from the cube (same initial shapes, differences in motion: motion dominant). Observers rated the apparent softness/hardness of the cubes. In the shape-dominant condition, ratings mainly depended on how deeply the rod penetrated the cube and were almost unaffected by the cube's intrinsic physical properties. In contrast, in the motion-dominant condition, ratings varied systematically with the cube's intrinsic stiffness, and were less influenced by the extent of the perturbation. We find that both results are well predicted by the absolute magnitude of deformation, suggesting that when asked to judge stiffness, observers resort to simple heuristics based on the amount of deformation. Softness ratings for static, unperturbed cubes varied substantially and systematically depending on the optical properties. However, when animated, the ratings were again dominated by the extent of the deformation, and the effect of optical appearance was negligible. Together, our results suggest that to estimate stiffness, the visual system strongly relies on measures of the extent to which an object changes shape in response to forces.

Design and Verification of an Innovative Handling System for Electrodes in Manufacturing Lithium-ion Battery Cells

The automated handling of electrodes for manufacturing lithium-ion battery cells for automotive applications is a bottleneck of the productivity. Current handling methods are hardly efficient enough due to the usage of sequential pick-and-place operations. One possible solution for significantly increasing the productivity of handling electrodes is waiving setting and resetting movements of handling devices through the utilization of a continuous process flow. The continuous process flow is based on the continuous grasping, moving, positioning and orientating of electrodes for the provision of assembling a z-folded electrode-separator-compound for lithium-ion battery cells without setting and resetting movements. The presented design for moving, orientating and positioning electrodes is an integrant of an overall production system for assembling the electrode-separator-compound consisting out of grasping electrodes, unwinding separator and a subsequent continuous z-folding.Subsequent to the analysis of the requirements of an automated handling of non-rigid materials and specifically electrodes, a handling system is conceptually designed, prototypically realized and its performance is verified through various test series. The handling system requires a continuous input of single sheets of electrodes, which are initially not defined in terms of their individual orientation and position as well as their orientation and position in regard to their designated layer within the electrode-separator-compound. The handling system is based on a highly dynamic tactile movement and correction mechanism using several counter-rotating rolls with direct drives and an integrated sensor architecture for the real-time registration of orientation and position. Various test series are realized in which the process requirements of a damage-free handling and the handling accuracy simultaneous to the movement are registered and evaluated.

Pouring Skills with Planning and Learning Modeled from Human Demonstrations

We explore how to represent, plan and learn robot pouring. This is a case study of a complex task that has many variations and involves manipulating non-rigid materials such as liquids and granular substances. Variations of pouring we consider are the type of pouring (such as pouring into a glass or spreading a sauce on an object), material, container shapes, initial poses of containers and target amounts. The robot learns to select appropriate behaviors from a library of skills, such as tipping, shaking and tapping, to pour a range of materials from a variety of containers. The robot also learns to select behavioral parameters. Planning methods are used to adapt skills for some variations such as initial poses of containers. We show using simulation and experiments on a PR2 robot that our pouring behavior model is able to plan and learn to handle a wide variety of pouring tasks. This case study is a step towards enabling humanoid robots to perform tasks of daily living.

Gripping Device for Heavy and Deformable Materials Handling: Concept, Design, Selection and Test

Automated handling of heavy deformable goods, due to their challenging material properties, sets a benchmark for handling sacks, such as bags of fabrics, synthetic packings, papers filled with coffee, cereals and plastic pellets. The paper focuses on the handling of jute coffee sacks.Nowadays the manipulation of jute coffee sacks is performed manually, because of the lack of specific grippers able to grasp this type of object. Hence, a set of concepts have been designed and developed to test the functionality of new devices, specifically intended to be used for the grasping of jute coffee sacks. Grippers have then been ranked through a Multiple Attribute Decision Making (MADM) method, in order to identify the best gripping tools for the handling of non-rigid materials. Finally, one of the most promising grippers has been designed and tested on real scale jute coffee sacks.

Reinforcement learning of a motor skill for wearing a T-shirt using topology coordinates

This article focuses on learning motor skills for anthropomorphic robots that must interact with non-rigid materials to perform such tasks as wearing clothes, turning socks inside out, and applying bandages. We propose a novel reinforcement learning framework for learning motor skills that interact with non-rigid materials. Our learning framework focuses on the topological relationship between the configuration of the robot and the non-rigid material based on the consideration that most details of the material (e.g. wrinkles) are not important for performing the motor tasks. This focus allows us to define the task performance and provide reward signals based on a low-dimensional variable, i.e. topology coordinates, in a real environment using reliable sensors. We constructed an experimental setting with an anthropomorphic dual-arm robot and a tailor-made T-shirt for it. To demonstrate the feasibility of our framework, a robot performed a T-shirt wearing task, whose goal was to put both of its arms into the corresponding sleeves of the T-shirt. The robot acquired sequential movements that put both of its arms into the T-shirt.

Effect of Substrate Flexibility on the Pressure Distribution and Lifting Force Generated by a Bernoulli Gripper

This paper presents the modeling and analysis of the pressure distribution and lifting force generated by a Bernoulli gripper when handling flexible substrates such as thin silicon wafers. A Bernoulli gripper is essentially a radial airflow nozzle used to handle large and small, rigid and nonrigid materials by creating a low pressure region or vacuum between the gripper and material. Previous studies on Bernoulli gripping have analyzed the pressure distribution and lifting force for handling thick substrates that undergo negligible deformation. Since the lifting force produced by the gripper is a function of the gap between the handled object and the gripper, any deformation of the substrate will influence the gap and consequently the pressure distribution and lifting force. In this paper, the effect of substrate (thin silicon wafer) flexibility on the equilibrium wafer deformation, radial pressure distribution and lifting force is modeled and analyzed using a combination of computational fluid dynamics (CFD) modeling and finite element analysis. The equilibrium wafer deformation for different air flow rates is compared with experimental data and is shown to be in good agreement. In addition, the effect of wafer deformation on the pressure and lifting force are shown to be significant at higher volumetric airflow rates. The modeling and analysis approach presented in this paper is particularly useful for evaluating the effect of gripper variables on the handling stresses generated in thin silicon wafers and for gripper design optimization.

The FNN Quilting Process Deformation Prediction Model

It is easy to have deformation that non-rigid materials is on high-speed processing, so this paper introduces the fuzzy neural networks combine with computer vision measurement technology to control this process. Based on the traditional PID control, increasing a fuzzy neural network predictor for pre-processing of trajectory compensation. Established a fuzzy neural network deformation prediction model of the single needle quilting, and simulated. Experimental and simulation results show that: error compensation which based on fuzzy neural network, have a good real-time, allow fast and accurate automated processing of quilting.