Similar Materials Research Articles

Drop-on-Demand 3D Printing of Programable Magnetic Composites for Soft Robotics

Soft robotics have become increasingly popular as a versatile alternative to traditional robotics. Magnetic composite materials, which respond to external magnetic fields, have attracted significant interest in this field due to their programmable two-way actuation and shape-morphing capabilities. Additive manufacturing (AM), also known as 3D printing, allows for the incorporation of different functional composite materials to create active components for soft robotic systems. However, current AM methods have limitations, especially when it comes to printing smart composite materials with high functional material content. This is a key requirement for enhancing responsiveness to external stimuli. Commonly used AM methods for smart magnetic composites, such as direct ink writing (DIW), confront challenges in achieving discontinuous printing, and enabling multi-material control at the voxel level, while some AM techniques are not suitable for producing composite materials. To address these limitations, we employed high-viscosity drop-on-demand (DoD) jetting and developed versatile programmable magnetic composites filled with micron-sized hard magnetic particles. This method bridges the gap between conventional ink-jetting and DIW, which require inks with viscosities at opposite ends of the spectrum. This high-viscosity DoD jetting enables continuous, discontinuous, and non-contact printing, making it a versatile and effective method for printing functional magnetic composites even with micron-sized fillers. Furthermore, we demonstrated stable magnetic domain programming and two-way shape-morphing actuations of printed structures for soft robotics. In summary, our work highlights high-viscosity DoD jetting as a promising method for printing functional magnetic composites and other similar materials for a wide range of applications.

Luminescence performance, temperature sensing characteristics and Judd-Ofelt theory analysis of Y2MoO6:Er3+/Yb3+/Li+ upconversion phosphors

A series of Y2MoO6:0.01Er3+/0.09Yb3+ upconversion phosphors doped with different concentrations of Li+ ions (0.05, 0.10, 0.15, 0.20, 0.25, and 0.30 mol) were prepared by the high temperature solid phase reaction method, and their physical phase structures and upconversion luminescence were analyzed. At the excitation wavelength of 980 nm, the addition of Li+ sharply increased the upconversion luminescence intensity. With the increase of Li+ ion content, the luminescence intensity first increases and then decreases. When the doping amount reaches 0.25 mol, the luminescence intensity reaches the maximum, and the sample emits green light in the two-photon process. Y2MoO6:0.01Er3+/0.09Yb3+/0.25Li+ exhibits excellent temperature sensing properties. Its absolute sensitivity Sa reaches up to 0.012 K-1, the relative sensitivity Sr is 0.017 K-1 and the measurement error δT is less than 0.3 K at room temperature. Its temperature sensing performance is far superior to similar materials. In addition, Judd-Ofelt theory calculations revealed that in comparison with Y2MoO6:0.01Er3+/0.09Yb3+, its Ω2 increased from 0.56×10-20 cm2 to 3.68×10-20 cm2 , and the spectroscopic quality factor Ω4/Ω6 ratio reached 4.09, which is much higher than that of the other fluorescent materials. The main reason is that Li+ replaces the Y3+ ion lattice site, causing lattice distortion and reducing the lattice symmetry around the active ion. The increase in asymmetry increases the possibility of spontaneous radiative transitions. It is shown that the prepared upconversion luminescent materials have potential applications in the field of solid-state lasers and optical sensing.

Children (and many adults) use perceptual similarity to assess relative impossibility.

People see some impossible events as more impossible than others. For example, walking through a solid wall seems more impossible if it is made of stone rather than wood. Across four experiments, we investigated how children and adults assess the relative impossibility of events, contrasting two kinds of information they may use: perceptual information and causal knowledge. In each experiment, participants were told about a wizard who could magically transform target objects into other things. Participants then assessed which of the two transformation spells would be easier or harder, a spell transforming a target object into a perceptual match (i.e., a similar-looking thing) or one transforming it into a causal match (e.g., an item made of similar materials). In Experiments 1-3, children aged 4-7 mainly thought that transformations into the perceptual match would be easier, though this tendency varied with age. Adults were overall split when choosing which spell would be easier. In Experiment 1, this was because of variations in their judgments across different pairs of spells; in Experiments 2 and 4, the split resulted because different subsets of adults preferred either the perceptual or causal match. Overall, these findings show that children, and many adults, use perceptual reasoning to assess relative impossibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Synthesis Of Biofoam Based On Glucomannan Porang And Polyvinyl Alcohol (PVA) With The Addition of Seaweed Dregs

Biofoam (Biodegradable foam) is an alternative packaging to Styrofoam made from natural raw materials that can be biodegraded in the soil. Biofoam is generally made from 3 constituent materials in the form of main ingredients in the form of starch or other similar materials, plasticizers such as PVA and also fillers in the form of fibers containing cellulose. The purpose of this study was to determine the effect of the combination of porang glucomannan, PVA and seaweed pulp on biofoam and to determine the best formulation and characteristics of the biofoam samples made.. The technique of making biofoam was done using baking technique with 9 different treatments. Each treatment was repeated 3x and observations were made on biofoam structure, mechanical properties testing (tensile strength, elongation and young modulus), water absorption test, and biodegradation test. The results showed that Polyvinyl Alcohol plays a role in the formation of a hollow biofoam structure. The thickness parameter value for each treatment was 0.628-1.939 mm. The tensile strength value of each treatment has a value ranging from 15.989-35.265 N/mm². The elongation value for each treatment ranged from 25.719-76.427%. The young modulus value for each treatment ranged from 0.343-0.896 N/mm². The water absorption value of each treatment obtained values ranging from 35.81-77.12%. And the value of testing biodegradation parameters obtained values ranging from 8.68-32.18%. So that the best treatment obtained using the multiple attribute method is the A3B1 treatment (PVA 15% and the ratio of seaweed pulp concentration to glucomannan 1: 2).

Porous starch-probiotics encapsulation for 3D-printed chocolate

This study aims to utilize 3D printing technology to prepare functional chocolates with high content and viability of probiotics. Firstly, in order to protect probiotics (L. plantarum, lpl) avoiding inactive by melting chocolate syrup, we used raw starches as different amylose/amylopectin ratios (up to ∼30% amylose) for preparing novel porous carriers to encapsulate them. Then the formed pores of porous starches (HPPS1:0,3:1,1:1,1:3,0:1) were identified by SEM and BET, and their ability to encapsulate lpl were compared (with HPPS3:1 showing the best behavior) based on the survival rate and gastrointestinal simulation system. Then HPPS3:1-lpl capsule was applied in 3D printed chocolate, with optimization of adding levels for the shaping property. Probiotic cell viability, rheology, texture, and digestion simulation were also compared. The results showed that the surviving amount of probiotics (107 CFU/g) in HPPS3:1-lpl@choc (add 15% HPPS3:1-lpl to the chocolate) was much higher than that in lpl@choc (102 CFU/g, without microencapsulation protection) after in-vitro digestion test, among which HPPS3:1-lpl@choc15 had the best shaping property. This research provides a new alternative strategy for utilizing porous starch carrier to encapsulate probiotics for melting 3D printing of chocolate (or other similar food materials) as personalized design.

Study on the structure and strata behavior regularity of repeated mining overburden of close-distance shallow buried coal seam group

This paper studies the collapse structure, plastic failure regularity, and overburden movement regularity in the mining process of the Shallowly Buried Coal Seam (SBCS) group in the Yulin area using physical similar material simulation, computer numerical simulation, and theoretical analysis. The objective of the present article is to explore the strata behavior regularity of the SBCS group, taking the engineering geological conditions of Hongliulin Coal Mine as the sample. According to the results, when the lower coal seam of the SBCS group is mined, the initial stress step increases, and the periodic stress step decreases compared to the upper first coal seam. During the mining of the lower coal seam, there is an extreme collapse span in the interlayer rock formation: the process induces the activation of the collapsed roof in the goaf of the upper coal seam, and the overall sinking phenomenon occurs. The dynamic stress is obvious. The roof stress is significant in passing through the upper coal seam's residual coal pillar (CP) into the lower coal seam. The CP is at a certain distance, and the residual CP is violently unstable and sinks the strata behavior regularity. The interlayer rock layer forms an inclined shear staggered fracture zone along the coal wall. The research results provide a theoretical method for controlling mine stress and green and safe mining in the SBCS group of coal production enterprises.

Study on the evolution of fractures in overlying strata during repeated mining of coal seams at extremely close distances

In particular, the secondary development of overlying strata fractures can easily lead to the upper goaf, resulting in gas and water gathered in the goaf entering the working face of the lower coal seam through the overlying strata fractures, threatening the safety of coal mine production. Security risks may arise. To further understand the caving and evolution law of overlying strata during repeated mining in extremely close distance coal seam down mining, 9# coal and 10# coal in the Nanyaotou Coal Industry were used as the engineering background. The caving characteristics and fracture evolution law of overlying strata during single and repeated mining were analyzed through similar material simulation tests. Based on fractal geometry theory, the relationship between the advancing distance of the working face and the fractal dimension of the overlying strata fracture is established to reflect the changing trend of fracture development. The calculation formula is derived from the tensile rate of rock strata to predict the development height of water-conducting fractures. The results show that the overlying strata failure structure is mainly a “hinged structure” and a “step structure,” which respectively promotes and inhibits the development of overlying strata fractures. Repeated mining causes mining-induced fractures in the lower coal seam to pass through the goaf of the upper coal seam and develop more vigorously in the upper coal seam, and the fractal dimension can effectively reflect the development of overlying strata fractures. The height of the water-conducting fracture zone increases in four stages: incubation, gradual increase, further gradual increase, and stability, eventually stopping development under the influence of the key layer (thick mudstone) bearing the load above. The development height of water-conducting fractures predicted by on-site water injection measurement is similar to that predicted by simulation experiments and theoretical calculations, verifying the feasibility of predicting the development height of water-conducting fractures through simulation tests and theoretical analysis. This study provides a reference for coal seam mining under similar conditions.

Explaining the spread in measurement of PDMS elastic properties: influence of test method and curing protocol.

Accuracy in the measurement of mechanical properties is essential for precision engineering and for the interrogation of composition-property relationships. Conventional methods of mechanical testing, such as uniaxial tension, compression, and nanoindentation, provide highly repeatable and reliable results for stiff materials, for which they were originally developed. However, when applied to the characterization of soft and biological materials, the same cannot be said, and the spread of reported properties of similar materials is vast. Polydimethylsiloxane (PDMS), commonly obtained from Dow as SYLGARD 184, is a ubiquitous such material, which has been integral to the rapid development of biocompatible microfluidic devices and flexible electronics in recent decades. However, reported shear moduli of this material range over 2 orders of magnitude for similar chemical compositions. Taking advantage of the increased mechanical scrutiny afforded to SYLGARD 184 in recent years, we combine both published and new experimental data obtained using 9 mechanical test methods. A statistical analysis then elucidates the significant bias induced by the test method itself, and distinguishes this bias from the influence of curing protocols on the mechanical properties. The goal of this work is thus two-fold: (i) it provides a quantitative understanding of the different factors that influence reported properties of this particular material, and (ii) it serves as a cautionary tale. As researchers in the field of mechanics strive to quantify the properties of increasingly complex soft and biological materials, converging on a standardized measurement of PDMS is a necessary first step.

Reversible Molecule Interactions Enable Ultrastretchable and Recyclable Ionogels for Wearable Piezoionic Sensors.

Ionogel-based piezoionic sensors feel motions and strains like human skin relying on reversible ion migrations under external mechanical stimulus and are of great importance to artificial intelligence. However, conventional ion-conductive polymers behave with degraded electrical and mechanical properties after thousands of strain cycles, and the discarded materials and devices become electronic wastes as well. Here, we develop ultrastretchable ionogels with superior electrical properties via the mediation of metal-organic frameworks, whose properties are attributed to reversible molecule interactions inside the material system. Ionogels present excellent mechanical properties with breaking elongation as high as 850%, exceeding most previously reported similar materials, and the high conductivity enables further application in sensor devices. In addition, our ionogels display superior recyclability because of the reversible physical and chemical interactions inside material molecules, which are eco-friendly to the environment. As a result, the ionogel-based piezoionic sensors deliver high sensitivity, flexibility, cyclic stability, and signal reliability, which are of great significance to wearable applications in human-motion detections such as throat vibration, facial expression, joint mobility, and finger movement. Our study paves the way for ultrastretchable and eco-friendly ionogel design for flexible electrochemical devices.

Construction and experimental verification of wave velocity model for source location in goaf overlying rock strata

The geological structure of the goaf overlying rock is complex, a consequence of coal mining that has modified the original stratified structure of the sedimentary strata. To enhance the accuracy of microseismic source location in such intricate geological formations, a wave velocity model for the “three zones” goaf was constructed based on natural divisions within the strata using Snell's law and assuming a homogeneous medium. The model took into account the effects of rock deformation and fracture development, enabling the derivation of formulas for microseismic wave propagation path and travel time calculation. Additionally, the concept of equivalent wave velocity was defined. An indoor simulation test using similar materials was conducted to establish a geological model of the goaf. By comparing the errors between the theoretical and measured values of equivalent wave velocity, assessing the locating effects before and after implementing the wave velocity model of the goaf, and verifying the feasibility of the model, it was demonstrated that establishing a wave velocity model based on the characteristics of the strata structure was crucial for improving the accuracy of the microseismic source location. Notably, as the propagation path of microseismic waves in the goaf increased, the equivalent wave velocity decreased. The wave velocity structure in the goaf exhibited nonuniformity, with the relative error between the theoretical and measured values of equivalent wave velocity being limited to 10 %. The incorporation of this established wave velocity model into the location method resulted in a substantial 58.57 % increase in locating accuracy.

Peningkatan Kemampuan Analisis Struktur Teks Laporan Hasil Observasi (LHO) dengan Metode Colaborative Learning

This study aims to improve the ability to analyze the text structure of the Report of Observation Results (LHO) in class X TITL 1 SMK Negeri 5 Surabaya through the application of collaborative learning methods. This research was conducted in two cycles. In cycle 1, most groups scored below the Minimum Completion Criteria (KKM). However, after improvements were made in cycle 2 by adding differentiation of the learning process according to students' learning modalities, there was a significant increase in scores. The results showed an increase in students' scores by 20% to 50%, with all groups successfully exceeding the KKM. Group 6 experienced the highest increase, from 50 to 100. This finding shows that the collaborative learning method is effective in improving the ability to analyze the structure of LHO text. The application of this method achieved maximum results, so it can be recommended as a learning strategy for similar materials in the future.

Prediction of resilient modulus and critical dynamic stress of recycled aggregates: Experimental study and machine learning methods

This paper aimed to investigate the feasibility of partially or completely replacing natural aggregates with recycled aggregates from construction and demolition wastes for low-carbon-emission use as coarse-grained embankment fill materials. The laboratory specimens were prepared by blending natural and recycled aggregates at varying proportions, and a series of laboratory repeated load triaxial compression tests were carried out to study the effects of material index properties and dynamic stress states on the resilient modulus and permanent strain characteristics. Based on the experimental results and by considering the main influencing parameters of the resilient modulus and permanent deformation, an artificial neural network (ANN) prediction model with optimal architecture was developed and optimized by the particle swarm optimization (PSO) algorithm, and its performance and accuracy were verified by supplementary analyses. A shakedown state classification method was proposed based on the unsupervised clustering algorithm, and a prediction model of critical dynamic stress was established based on the machine learning (ML) method and the shakedown state classification results. The research results indicate that the stress state has a greater influence on the resilient modulus and permanent deformation characteristics than other factors, and the shear stress ratio has a significant effect on the shakedown state. The resilient modulus and critical dynamic stress of such specimens vary linearly with confining pressure. The improved PSO-ANN prediction model exhibits high prediction accuracy and robustness, superior to several other commonly used ML regression prediction algorithms. The resilient modulus and critical dynamic stress prediction methods based on ML algorithms can provide technical guidance and theoretical basis for the design and in-service maintenance of similar unbound granular materials.

Effect of friction pressure on microstructure and mechanical properties of friction welded joint between alumina and 1100 aluminum alloy

Friction welding (FW) is a solid-state joining process that has received considerable attention in recent years due to its efficacy in joining similar and dissimilar materials. In this experimental study, dissimilar joints of alumina (Al2O3) ceramic and 1100 aluminum alloy were produced by friction welding. The objective was to investigate the impact of welding parameters on the mechanical and structural properties of the welded joints. Welding processes were conducted using a continuous drive friction welding (CDFW) machine, under different friction pressures (20, 34 and 45 MPa), whereas other welding parameters are kept constant. The strength and mechanical properties of the welded Joints were assessed by means of a three-point bending test and Vickers micro-hardness measurements. Furthermore, the microstructure of the Joints was examined using optical microscopy (OM) and scanning electron microscopy (SEM). The results showed that friction pressure has a significant effect on the structural and mechanical properties of the joints.

Research on prediction method of coal mining surface subsidence based on MMF optimization model

Coal seam mining causes fracture and movement of overlying strata in goaf, and endangers the safety of surface structures and underground pipelines. Based on the engineering geological conditions of 22,122 working face in Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. a similar material model test of mining overburden rock was carried out. The subsidence of overburden rock was obtained through the full-section strain data of distributed optical fiber technology, and the characteristics of mining surface subsidence were studied. The Weibull model was used to adjust the mathematical form of the first half of the surface subsidence curve via the MMF function. On this basis, the prediction model of coal seam mining surface subsidence was established, and the parameters of the prediction model of surface subsidence were determined. The test results show that with the advancement of coal seam mining, the fit goodness of the surface subsidence prediction curve based on the MMF optimization model reaches 0.987. Compared with the measured values, the relative error of the surface subsidence prediction model is reduced to less than 10%. The model displays good prediction accuracy. The time required for settlement stability in the prediction model is positively correlated with parameter a and negatively correlated with parameter b. The research results can be further extended to the prediction of overburden “three zones” subsidence, and provide a scientific basis for the evaluation of surface subsidence compression potential in coal mine goaf.

Multi-objective optimization design of a sewage pump based on non-dominated sorting genetic algorithm III

Accumulation of sanitary refuse, such as flexible cloth-like structures or the so-called rags, inflows through sewage pumps are prone to tangling, ultimately leading to clogging and wear. To prevent this, the ability of sewage pumps to handle wet wipes, rags, and similar flexible materials is a key feature that must be considered as the pumps are designed. Therefore, this paper proposed a multi-objective optimization strategy based on the fluid–structure interaction simulation, Support Vector Regression (SVR), and non-dominated sorting genetic algorithm III (NSGA-III). First, the values of the optimization objectives were obtained by a Coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) approach. Then, SVR was utilized to establish an approximate model between the design variables and the optimization objectives. The NSGA-III was applied to search the Pareto front. Finally, the improved impeller model was selected by adopting technique for order preference by similarity to an ideal solution (TOPSIS) with entropy weight. The results show that the multi-objective optimization method is suitable for the optimization design of sewage pumps. Comparing the numerical calculations of the original pump and the optimized pump, the results show that the optimized head and efficiency increased by 9.7% and 7.13%, respectively. The optimized pump improves the passage rate of the rag and effectively improves the clogging behavior. The wear amount of the optimized pump is significantly reduced by 32.54%.

WITHDRAWN: Characterization of soil response in mining with grouting into overburden bed-separation based on similar material simulation

Revealing the response laws of soils under various mining conditions is of significance for the scientific protection of farmland in mining areas. To avoid the interference from the spatial heterogeneity of natural soil on the research results, similar simulation experiments were employed. Two models for soil responses under mining with and without grouting into overburden bed-separation were designed, and monitoring was conducted for soil quality factors (moisture content, silt and clay content, and bulk density) and surface deformation factors (subsidence, inclined, and horizontal deformation). The results indicate that it can significantly control surface subsidence in mining with grouting into overburden bed-separation, the maximum subsidence value was reduced by 66.67%, the average subsidence value of each measuring point in the main section was reduced by 78.92%, and the subsidence area was reduced by 34.68%. The coefficient of variation of soil quality factors in the mining-affected area was obviously higher than that in the control area and the initial value before mining. Compared with mining without grouting, the variation coefficient of soil quality factor under mining with grouting was reduced by 2.71-6.79%. Soil quality factors and surface deformation factors show a significant quadratic function relationship, among which horizontal deformation has the greatest influence on soil quality factors, followed by subsidence and inclination. Therefore, it can effectively control surface subsidence and protect cultivated land when mining with grouting into overburden bed-separation.

Life cycle assessment of mycelium based composite acoustic insulation panels

Mycelium-based composites (MBC) have gained attention in research and industry as environmentally favorable substitutes for materials in various applications, including packaging inserts, textiles, automotive and even electronics. As construction materials, MBCs show promising characteristics as fully bio-based thermoacoustic insulation. To investigate the potential environmental impacts of the production, use and end-of-life of MBC acoustic insulation panels and their mounting system, we conducted a cradle-to-grave life cycle assessment (LCA) based on real-world production planning data and supplementary literature data. Life cycle modules and system boundaries were chosen following EN 15804. A life cycle impact assessment was carried out based on select impact categories from the Environmental Footprint (EF 3.1) framework. Uncertainties and sensitivities were examined based on a Monte-Carlo simulation and contribution to variance. Energy use for incubation and drying during the manufacturing stage is the most important contributor in most impact categories, including global warming potential. This is further underlined by the large sensitivity of the results to uncertainties related to energy-use. Overall, the manufacturing stage is the dominant life-cycle stage in terms of potential environmental impacts. Generally, our results agree with literature values of LCA studies on similar materials, when normalized for mass and volume. Tracing biogenic carbon throughout the product system shows an early release of over 10 % of the stored biogenic carbon during the manufacturing stage as opposed to a release of all remaining stored carbon during disposal and energetic recycling.

Enhancing sustainable food packaging design: A machine learning approach to predict ventilated corrugated paperboard strength

In the food packaging industry, ventilated corrugated paperboard boxes are crucial for sustainable transport of fresh products. While these boxes' ventilation holes advance air circulation, they also impact the material's compression or buckling strength. Variations in hole geometry and location affecting this strength are explored, considering the composite material, multi-layered structure. Traditional mechanical analyses, which often require simplifications, may not fully capture this complexity, leading to less accurate predictions of the paperboard's strength. To address these challenges, a machine learning (ML) approach was utilized, employing the Light Gradient Boosting Machine (LGBM) to develop a predictive model for the buckling strength of corrugated paperboard boxes with ventilation cutouts. This physics-informed ML model, trained on a compression dataset resulting from experimental tests for plates with a single cutout in three shapes and Finite Element Method (FEM) simulations for plates with various patterns of circular cutouts, provides highly accurate estimates of the plates' buckling strength. It achieved 91.7% accuracy on experimental data and 94.68% on FEM simulation data, showcasing its reliability. A new tool for predicting the buckling strength of corrugated paperboard is provided by this research, along with insights that can inform the design of more sustainable packaging solutions. Furthermore, the methodology and findings have broader applications, potentially benefiting sectors like aerospace and construction, where similar structural materials are used.

Non-destructive identification of iron oxide crystals in 15th century A.D. Chinese imperial red overglaze decoration

Red overglaze decoration is one of the earliest and most important colorful overglaze decorations in China, which had a remarkable influence on the successful firing and development of polychrome overglaze porcelains. The uncontrollability of red overglaze decoration led to a certain proportion of defective porcelain products in the Ming Dynasty (1368–1644 CE). To investigate and identify the coloring effect of iron oxide crystals in red overglaze decoration, three groups of samples with different color appearance were non-destructively analyzed and observed by OM, spectrophotometer, XRF, Raman spectroscopy, XRD and SEM. The results show that the hue of red overglaze decoration was caused by the different crystal forms of iron oxide. Hematite (α-Fe2O3) displays red color, while maghemite (γ-Fe2O3) and magnetite (Fe3O4) yield orange-red and dark red colors. With the similar raw materials and manufacturing processes, the presence of maghemite and magnetite are relatedto subtle differences in processing raw materials and firing them.

Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework.

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process.