Dimensional Variations Research Articles

Computational design synthesis for Fabrication-Aware assembly problems using building objects with dimensional variations

State-of-the-art computational design approaches for designing the assembly of a structure often rely on high-precision building objects, which can be difficult and costly to fabricate. To consider a wider range of construction materials and fabrication processes, e.g. low-cost 3D printing and brick making, this research introduces a novel computational design method for fabrication-aware assembly problems using existing building components with dimensional variations. Given a finite set of building objects, this study develops a statistical preprocessing approach and a modified pattern search with an efficient integer linear programming method to generate the assembly of a target structure, such that it is statically stable and optimized for geometric smoothness and space utilization. In two case studies, the proposed algorithm exhibits significantly better boundary fitting and space utilization compared with a genetic algorithm and a standard pattern search method and lower computational cost compared with a genetic algorithm.

Multi-Objective Mayfly Optimization Algorithm Based on Dimensional Swap Variation for RFID Network Planning

In this study, a multi-objective mayfly optimization algorithm based on dimensional swap variation (DSV-MOMA) is proposed to solve Multi-objective RFID network planning problem (MORNP). The contributions of this work are as following: firstly, improving multi-objective mayfly optimization algorithm (MOMA)’s ability to solve high-dimensional nonlinear optimization problems; secondly, DSV-MOMA is used to solve the MORNP problem, and optimize two and three of the four objective functions simultaneously; lastly, the fuzzy decision mechanism is used to select an optimal solution objectively from pareto optimal solutions. The proposed DSV-MOMA algorithm contributes to having better diversity and convergence when solving high dimensional nonlinear and discontinuous test functions in comparison to other popular metaheuristic algorithms. DSV-MOMA also performs well when dealing with MORNP problems. In most experiments, DSV-MOMA can reduce interference effectively, and obtain satisfactory load balance and power while ensuring a higher coverage.

Alkali-Activated Materials with Pre-Treated Municipal Solid Waste Incinerator Bottom Ash

This study presents the results of an experimental campaign on the use of municipal solid waste incinerator bottom ash (MIBA) and fly ash (FA) as precursors for the production of alkali-activated materials. MIBA was subjected to a pre-treatment stage in response to two issues: high metallic aluminum content, which reacts in a high pH solution, releasing hydrogen; and low amorphous content of silica-, aluminum- and calcium-bearing phases, which translates into a limited formation of reaction products. The proposed pre-treatment stage oxidizes most of the metallic aluminum fraction and compensates for the low reactivity of the material via the formation of additional reactants. Different combinations of MIBA and FA were tried—mass-based ratios of 0/100, 25/75, 50/50, 75/25, and 100/0 for MIBA/FA. Two mix designs of the alkaline activator with sodium hydroxide and sodium silicate were evaluated by varying the Na2O/binder and SiO2/Na2O ratios. These mortars were tested in the fresh and hardened state. The results showed that the pre-treatment stage was effective at stabilizing the dimensional variation of MIBA. Despite the lower reactivity of MIBA, mortars with 50/50 of MIBA/FA presented a maximum 28-day compressive strength of 25.2 MPa, higher than the 5.7 MPa of mortars made with MIBA only.

A New Sparse-Learning Model for Maximum Gap Reduction of Composite Fuselage Assembly

Natural dimensional variabilities of incoming fuselages affect the assembly speed and quality of fuselage joins in composite fuselage assembly processes. Shape control is critical to ensure the quality of composite fuselage assembly. In current practice, the structures are adjusted to the design shape in terms of the loss for further assembly without considering the existing dimensional gap between two structures. Such practice has two limitations: (a) controlling each fuselage to the design shape may not be the optimal shape control strategy in terms of a pair of incoming fuselages with different incoming dimensions; (b) the maximum gap is the key concern during the fuselage assembly process, so the loss of gap after control ought to be considered. This article proposes an optimal shape control methodology via the loss for the composite fuselage assembly process by considering the existing dimensional gap between the incoming pair of fuselages. On the other hand, due to the limitation on the number of available actuators in practice, we face an important problem of finding the best locations for the actuators among many potential locations, which makes the problem a sparse estimation problem. We are the first to solve the optimal shape control in the fuselage assembly process using the model under the framework of sparse estimation, where we use the penalty to control the sparsity of the resulting estimator. From the statistical point of view, this can be formulated as the loss based linear regression, and under some standard assumptions, such as the restricted eigenvalue (RE) conditions, and the light-tailed noise, the nonasymptotic estimation error of the regularized linear model is derived, which meets the upper-bound in the existing literature. Compared to the current practice, the case study shows that our proposed method significantly reduces the maximum gap between two fuselages after shape adjustments.

Predictive modeling of surface and dimensional features of vapour-smoothened FDM parts using self-adaptive cuckoo search algorithm.

Despite numerous advantages of fused deposition modeling (FDM), the inherent layer-by-layer deposition behavior leads to considerable surface roughness and dimensional variability, limiting its usability for critical applications. This study has been conducted to select optimum parameters of FDM and vapour smoothing (chemical finishing) process to maximize surface finish, hardness, and dimensional accuracy. A self-adaptive cuckoo search algorithm for predictive modelling of surface and dimensional features of vapour-smoothened FDM-printed functional prototypes has been demonstrated. The chemical finishing has been performed on hip prosthesis (benchmark) using hot vapours of acetone (using dedicated experimental set-up). Based upon the selected design of experiment technique, 18 sets of experiments (with three repetitions) were performed by varying six parameters. Afterwards, a self-adaptive cuckoo search algorithm was implemented by formulating five objective functions using regression analysis to select optimum parameters. An excellent functional relationship between output and input parameters has been developed using a self-adaptive cuckoo search algorithm which has successfully found the solution to optimization issues related to different responses. The confirmatory experiments indicated a strong correlation between predicted and actual surface finish measurements, along with hardness and dimensional accuracy.

Biodegradable composites of starch/polyvinyl alcohol/soybean hull (Glycine max L.) produced by thermoplastic injection

AbstractSoybean hull is an agro‐industrial residue produced abundantly both in Brazil and worldwide. It is generated during the dehulling of the grains for oil production. This work aimed to develop materials using low‐cost, abundant, and biodegradable raw materials, as soybean hull, corn starch, polyvinyl alcohol (PVA), and glycerol. Five formulations of thermoplastic starch (TPS) were produced containing between 4% and 19% of ground soybean hull and another five formulations of the same composition but with 6% PVA added. The materials were extruded and injected to produce specimens. They were analyzed by FTIR‐ATR, mechanical tests, mass loss in water, and dimensional variations. The addition of hull to the TPS did not affect the processability of the materials, and the PVA improved the processability. Hull concentration from 8% to 15% increased the tensile strength of the materials, and the PVA reduced them. Soybean hull provided greater dimensional stability to the specimens, especially with PVA and with the highest hull contents (15% and 19%).Content of soy hulls between 8% and 15% acted as a structural reinforcer of TPS materials, forming a composite, as it increased the tensile strength, reduced the elongation, increased the stiffness, and improved the dimensional stability of the materials.

Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation

Variations of constituent fiber and matrix properties and process conditions can cause significant variability in composite parts and affect their performance. The focus of this paper is to establish a new computational framework that can efficiently quantify the uncertainty propagation of parts manufactured through the resin transfer molding (RTM) process. RTM involves a sequence of inter-related processes that span multiple spatial and temporal scales. This calls for a multi-scale analysis for the nominal process, which is computationally complex and intensive. A direct Monte Carlo simulation of uncertainty quantification leads to prohibitive cost. In this research we leverage a sequentially architected multi-response Gaussian process (MRGP) meta-modelling approach to facilitate a hierarchical procedure. This can dramatically reduce the computational cost, and allow us to characterize the process outputs of interest at different scales and at the same time capture the intrinsic correlation amongst these outputs. Moreover, integrating a global sensitivity analysis with the hierarchical MRGP meta-models yields the importance ranking of uncertainty propagation paths. This computational framework provides a quantitative assessment tool of the uncertainties in composite manufacturing. Case study on curing-induced dimensional variability of a curved composite part is conducted for demonstration and validation.

Cooperative coordination-mediated multi-component self-assembly of “all-in-one” nanospike theranostic nano-platform for MRI-guided synergistic therapy against breast cancer

Carrier-free multi-component self-assembled nano-systems have attracted widespread attention owing to their easy preparation, high drug-loading efficiency, and excellent therapeutic efficacy. Herein, MnAs-ICG nanospike was generated by self-assembly of indocyanine green (ICG), manganese ions (Mn2+), and arsenate (AsO43−) based on electrostatic and coordination interactions, effectively integrating the bimodal imaging ability of magnetic resonance imaging (MRI) and fluorescence (FL) imaging-guided synergistic therapy of photothermal/chemo/chemodynamic therapy within an “all-in-one” theranostic nano-platform. The as-prepared MnAs-ICG nanospike had a uniform size, well-defined nanospike morphology, and impressive loading capacities. The MnAs-ICG nanospike exhibited sensitive responsiveness to the acidic tumor microenvironment with morphological transformation and dimensional variability, enabling deep penetration into tumor tissue and on-demand release of functional therapeutic components. In vitro and in vivo results revealed that MnAs-ICG nanospike showed synergistic tumor-killing effect, prolonged blood circulation and increased tumor accumulation compared to their individual components, effectively resulting in synergistic therapy of photothermal/chemo/chemodynamic therapy with excellent anti-tumor effect. Taken together, this new strategy might hold great promise for rationally engineering multifunctional theranostic nano-platforms for breast cancer treatment.

RF and linearity distortion performance estimation of dopingless symmetric tunnel FET (DLSTFET)

ABSTRACT This work eradicates the issue of area penalty in logic designing due to unidirectional flow of current in conventional TFET. The dopingless symmetric tunnel FET (DL-STFET) device structure also has immunity towards random dopant fluctuations (RDFs) and trap-assisted tunnelling along with easy fabrication and a lower thermal budget. The device structure has a better drive current and subthreshold swing (SS). Furthermore, the sensitivity of the device with respect to the device dimension variation such as channel/gate length, body thickness and silicon pad thickness has also been studied on device dc, analog/RF and linearity parameters. The device dc parameters are immune towards the device dimensional variations, whereas the analog/RF and linearity parameters are sensitive to device dimension variation. The analog/RF parameters such as , , , , GBW, TGF and TFP have been studied. Furthermore, the linearity parameters such as , , , , and have also been analyzed here. This study provides the direction for designing DL-STFET for better analog/RF performance with least linearity distortions.

Characterization and modeling the hygroscopic behavior of cellulose acetate membranes

Exploiting materials with the ability to respond to the environmental stimuli is experiencing an enormous research interest. In particular, polymers that are sensitive to the changes of humidity levels attract great attention as self-actuators. The sensitivity of these materials to the level of moisture is expressed by their hygroscopic properties, namely, the coefficient of hygroscopic expansion. In this context, this study details the effect of moisture absorption on cellulose acetate membranes, as potential material for humidity-responsive self-actuators. The aim is two-fold. The first deals with the evaluation of the coefficient of hygroscopic expansion (α) through the determination of the absorbed moisture concentration at saturation (Csat) and the relevant moisture absorption induced strain (εhygro). The second assesses the accuracy of a finite element modeling in describing the coupling of moisture absorption in cellulose acetate membranes and the corresponding dimensional variation, using the material properties experimentally measured. The experimentally measured Csat and εhygro resulted a non-linear dependency on relative humidity. Also the coefficient of hygroscopic expansion (α = Csat /εhygro) resulted to have a non-linear dependency on the relative humidity, as well. By this input, numerical simulations were performed for different relative humidity levels, showing accurate description of experimental data.

Gold-Based Double Perovskite-Related Polymorphs: Low Dimensional with an Ultranarrow Bandgap

Lead-free halide double perovskites (LFDPs) arouse great interest as environmentally friendly substitutes to the state-of-the-art lead halide perovskites. However, most halide LFDPs feature large bandgaps that afford weak visible-light absorption. Herein, we synthesized and systemically studied the single-crystallographic, physical–chemical, band structural, and electronic properties of a series of multidimensional Au-based lead-free hybrid polymorphs, [(NH3CH3)2(AuI4)(AuI2)], [(NH2CHNH2)(AuI4)(AuI2)], [NH3(CH2)nNH3][(AuI4)2] (n = 2, 3), [NH3(CH2)nNH3][(AuI4)(I3)] (n = 4, 5, 6), and [NH3(CH2)nNH3]2[(AuI4)(AuI2)(I3)2] (n = 7, 8, 9). Among these two-dimensional (2D) perovskite-related and zero-dimensional nonperovskite polymorphs, we found an abnormal variation of structural dimensionality with increasing the size of A-site cations. These polymorphs displayed ultrabroad absorption ranges (200–1300 nm) and tunable low indirect bandgaps (0.93–1.18 eV). Combined single-crystallography and Hall effect measurements demonstrate that the I3– conduces to the formation of 2D perovskite structures related by connecting with the (AuI4)− sheets and also the charge transport properties. Consequently, [NH3(CH2)5NH3][(AuI4)I3] and [NH3(CH2)8NH3]2[(AuI4)(AuI2)(I3)2] showed high spectroscopic limited maximum efficiencies of 28.4 and 20.5%, respectively, with a film thickness of 500 nm. Our findings bring the Au-based polymorphs to the forefront of study on lead-free low-bandgap perovskites for optoelectronics and reveal the unique rule of dimensionality engineering in such materials.

Overcoming Variability in Printed RF: A Statistical Method to Designing for Unpredictable Dimensionality

As additively manufactured radio frequency (RF) design expands towards higher frequencies, performance becomes ever more sensitive to print-induced dimensional variations. These slight deviations from design dimensions typically skew RF performance, resulting in low yields or poor device performance. In order to overcome this limitation, RF design paradigms must be developed for non-uniform process and material-specific variations. Therefore, a new generalized approach is developed to explore variation-tolerant designs for printed RF structures. This method evaluates the feature fidelity and S11 performance of micro-dispensed, X-band (8–12 GHz) patch antennas by evaluating the standard deviation in as-printed features, surface roughness, and thickness. It was found that the traditional designs based on optimal impedance matching values did not result in the most robust performance over multiple printing sessions. Rather, performance bounds determined by print deviation could be utilized to improve large-batch S11 results by up to 7 dB. This work demonstrates that establishing the average standard deviation of printed dimensions in any RF printing system and following the formulated design procedure could greatly improve performance over large datasets. As such, the method defined here can be applied to improve large-scale, printed RF yields and enable predictive performance metrics for any given printing method.

Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling.

Additive manufacturing has gained popularity among material scientists, researchers, industries, and end users due to the flexible, low cost, and simple manufacturing process. Among number of techniques, fused deposition modeling (FDM) is the most recognized technology due to easy operation, lower environmental degradation, and portable apparatus. Despite numerous advantages, the limitations of this technique are poor surface finish, dimensional accuracy, and mechanical strength, which must be improved. The present study focuses on the implementation of the genetic algorithm and Taguchi techniques to achieve minimum dimensional variability of FDM parts especially for polymeric biocomposites. The output has been measured using standard testing techniques followed by Taguchi and genetic algorithm analyses. Four response variables were measured and were converted into single variable with combination of different weightages of each response. Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. The advanced optimization and production techniques have yielded promising results which have been validated by advanced algorithms. It was found that layer thickness and orientation angle were significant parameters which influenced the dimensional accuracy whereas best fitness value was 0.377.

A dimensional assessment of small features and lattice structures manufactured by laser powder bed fusion

The understanding of dimensional variations produced by laser powder bed fusion is critical in components with small features and with dimensions close to the inherent limits of the process. In this context, two reference geometries were used: (a) straight walls to quantify dimensional relative error for small features and (b) a latticed neck region of a fatigued specimen (cubic and hexagonal cell design, with design strut sizes of 250 µm, 500 µm, and 1000 µm in cell size). Samples were fabricated out of AISI 316L stainless steel powder with different building orientations. The metrology techniques used were the following: focus variation microscopy, optical microscopy and micro-computed tomography. The straight wall characterization shows that built orientation does not influence dimensional relative error for walls with less than 750 µm. Acceptable dimensional relative errors (~ 2% to ~ 15%) are achieved only in walls with 750 µm in width of more. For lattice structures, the fine struts (250 µm) show a significant level of dimensional relative error (~ 5% to ~ 25%). This additive manufacturing process delivers more consistent dimensions for coarse struts (500 µm), with relative errors between ~ 2% and ~ 4%. All metrology techniques showed the same trends in terms of capturing the dimensional variations for fine and coarse struts.

Efficacy of 3D dynamic image analysis for characterising the morphology of natural sands

Two-dimensional (2D) dynamic image analysis (DIA) is gaining acceptance in geotechnical engineering research. Three-dimensional (3D) DIA extracts features from 8–12 projections of a particle and thus it is believed to verge on the true particle morphology. DIA is fast, efficient and convenient for characterising thousands of particles quickly; nevertheless, it captures shapes that are fundamentally different from the 3D morphologies reconstructed using micro-computed tomography (μCT). In DIA, particle features are interpreted using external images of a particle, which fail to account for differences in imaging perspectives. In addition, 2D and 3D shape descriptors are influenced by differences in dimensionality projection owing to variations in definition, dimensionality and perspectives of the particle images employed, which causes them to differ from their 3D counterparts. In this study, the sand particle size and shape descriptors obtained using both DIA and μCT are compared for three natural sands having wide granulometries. Three-dimensional DIA offers significant advantages in terms of efficiency, while providing adequate representation of Feret dimensions, sphericity and convexity. However, the study demonstrates that 3D roundness is difficult to characterise using DIA and that shape measurements of complex irregular calcareous sands obtained from 3D DIA are not comparable to those obtained using μCT.

Autoclave Treatment of Sisal Fiber and Its Effect on Fiber Properties and on the Pull-out Behavior from Cementitious Matrix

ABSTRACT Natural fibers as reinforcement of cementitious matrices have become a material with greater toughness, strength, and sustainability for the construction industry. However, their use can generate some inconveniences due to the high-water absorption of the natural fiber, which affects the production and durability of composites. In this work, the autoclave treatment of sisal fibers was carried out in order to determine time and pressure values that result in a lower water absorption of the fibers. The fibers were exposed to 1.0 or 1.5 atm of vapor pressure for 30, 60 and 90 min and their structures were evaluated by X-ray diffraction, differential scanning calorimetry and scanning electron microscope. The effect of treatment on fiber dimensional variation, fiber mechanical properties, and fiber-matrix bond was investigated. Composites reinforced with treated fibers were produced and subjected to bending tests. The results indicate that the autoclave treatment with 30 min and 1.5 atm reduced the water absorption from 134% to 90% and the dimensional variation from 63% to 18%. However, structural modification of the fibers reduced the fiber’s tensile strength and bond strength. Composites reinforced with treated fibers showed lower flexural strength and toughness than those reinforced with natural fibers.

Isomorphic 2D/3D Objects and Saccadic Characteristics in Mental Rotation

Mental rotation (MR) is an important aspect of cognitive processing in gaming since transformation and manipulation of visuospatial information are necessary in order to execute a gaming task. This study provides insights on saccadic characteristics in gaming task performance that involves 2D and 3D isomorphic objects with varying angular disparity. Healthy participants (N = 60) performed MR gaming task. Each participant was tested individually in an acoustic treated lab environment. Gaze behavior data of all participants were recorded during task execution and analyzed to find the changes in spatiotemporal characteristics of saccades associated with the variation in angular disparity and dimensionality. There were four groups with unique combination of angular disparity and dimensionality, each with fifteen participants randomly assigned. Results indicate that the spatial characteristics of the object affect the temporal aspect of saccade (duration), whereas the spatial aspect of the saccade (amplitude) is influenced by the objects’ dimension. A longer saccade duration indicates a prolonged suppression of spatial information processing during the MR tasks with objects at convex range angular disparities. Therefore, the MR tasks with convex angular disparity become more complex to process compared to the tasks with reflex angular disparity. MR process is faster and more accurate with 3D objects compared to the 2D objects. There is an interaction between angular disparity and dimensionality in terms of mental demand, such that the MR processing with 2D objects in reflex angular disparity was more mental demanding than that of convex angular disparity; however, this trend was absent in case of 3D objects. Hence, during the MR task, the longer saccade duration implies that the tasks with convex angular disparities become comparatively more challenging. Also, the lower saccadic amplitude for 2D objects indicates difficulties in processing due to deficient visual features. The findings could help in framing the computer-based game (or videogame) concerning MR abilities for training or rehabilitation purposes.

Feature modeling for configurable and adaptable modular buildings

Current design approaches for new buildings do not sufficiently plan for or adapt to changing conditions that could be used to extend the useful life of buildings, as part of a circular economy. While notable advances have emerged for using BIM-based configurators to improve building design and project execution, there is a need expand such configurators to look at how buildings can be adapted and re-configured across their lifecycle. This paper develops and demonstrates an innovative feature modeling approach for configuring and adapting modular buildings. This study uses BIM for structuring intricate feature relationships of specific design aspects in terms of product circularity. The design aspects considered are structural design, dimensional variation control, and disassembly planning design. Feature parameter maps, which are a general constraint relation representation, are implemented to describe the data models since they are an efficient way to visualize feature elements and interdependencies, to avoid the creation of redundant information, and to improve data structure consistency. The application of the proposed methodology is validated with a functional demonstration of a conceptual design and optimization for a single module that is meant to be part of a modular construction project. The product model was synthesized in a parametric BIM environment for iterative configuration, analysis of results, and final optimization of the single module assembly. The demonstration case study shows that BIM can be adapted to assist on modeling specific design aspects for modular buildings and to create design alternatives. Also, the method shows a considerable benefit that the designer can produce diverse accurate design alternatives within a reduced amount of time.

A Real-Time Synchrophasor Data Compression Method Using Singular Value Decomposition

The proliferation of phasor measurement units (PMUs) presents new challenges in archiving and processing large amounts of synchrophasor data which necessitates advanced data compression methods. This paper proposes a singular value decomposition (SVD)-based method for compression of synchrophasor data, including magnitude, phase-angle, and complex phasor. The proposed method includes a dimensionality evaluation and reduction technique and a real-time progressive partitioning algorithm. The proposed dimensionality reduction technique employs the measurement uncertainty of PMUs and introduces a threshold criterion on the signal-to-noise ratio (SNR) of SVD modes. Singular modes with high SNR are retained, and those dominated by measurement error are discarded to achieve a high compression ratio (CR) while preserving the critical information with adequate accuracy. The proposed progressive partitioning separates the data corresponding to normal and disturbance conditions by monitoring the dimensionality variations in real-time. The partitions containing the data of similar dimensionality are separately compressed to further improve the accuracy and CR. The performance of the proposed method is evaluated and benchmarked against state-of-the-art methods using both field and simulated PMU data. The results show that the proposed method provides high CR while accurately preserving the critical information of events and disturbances.

Computational analysis of welding radial deformation of typical pressure cylindrical shell with ring stiffener

We report on the evaluation of the radial deformation caused by the welding process on a typical pressure cylindrical shell with ring stiffeners. Welding experiments on butt and fillet welding were conducted beforehand, and temperature-dependent material properties were considered. The out-of-plane welding distortion and residual stress were measured using a three-coordinate measuring machine and X-ray diffraction, respectively. Effective thermal elastic plastic FE analysis based on parallel computation technology was performed to examine the mechanical behaviors. Computed results were validated by comparison with experimental measurements. Welding inherent deformations of the examined butt and fillet joints were then evaluated by integrating computed plastic strains. The elastic FE analysis, with welding inherent deformation as mechanical loading, was employed to examine the dimensional variation of the considered pressure cylindrical shell. The influence of ring stiffener on welding radial deformation was also discussed. Moreover, the application of welding sequence modification and inverse deformation was numerically examined with the proposed elastic FE analysis to reduce the welding radial deformation and enhance the fabrication accuracy. • Thermal and mechanical behaviors of welding experiments were holistically examined with advanced measurement approach. • TEP FE analysis was employed to represent welding response, and computed results have good agreement with measurement. • Ring stiffener has significant influence on dimensional accuracy by elastic FE analysis with welding inherent deformation. • Welding sequence and inverse deformation have significant effect to prevent radial deformation of pressure cylindrical shell.