This paper presented a MEMS based differential scanning calorimeter (DSC) for biomolecular characterization. In this MEMS based DSC, PDMS (Polydimethylsiloxane) and Flexdyne thin film were used to construct the microfluidic chamber. Polyimide were used to fabricate the flexible substrate and temperature sensitive vanadium oxide was used as the thermistor material. A heating stage was used to heat the sample and reference up at a certain rate. The resolution study and step response characterization indicated the high sensitivity (6.1V/W) of the device. The test with Bovine Serum Albumin (BSA) samples showed clear phase transitions and the data was confirmed to be reasonable by comparing it with the results of commercial DSC’s test. This device used 0.63uL sample amount and could complete the scanning process in 3 minutes, significantly increasing the throughput of the biomolecular thermodynamics study like protein denaturation process compared to the traditional DSC (1 to 2 hours).

Fused Deposition Modeling (FDM), an Additive Manufacturing (AM) technique, is widely used due to its low-cost and open source. Geometry accuracy and strength performance of the printed parts are closely related to inter-layer bonding between adjacent layers and inter-road bonding in the layer. Because of the limit of layer-based AM, the longitudinal tensile strength of the filaments is much higher than the bonding strength between adjacent filaments, which brings anisotropy of the printed part. While CLFDM is devoted to solve this problem and obtain better geometry accuracy and meanwhile decrease build time by virtue of high continuity of filament, reduced stair-step effect, and lesser number of layers, especially when manufacturing thin and curved parts (shells). However, to the best of our knowledge in the aspects of process modeling of CLFDM, available researches focus mainly on simple curved layer, instead of more intricate ones possessing tiny features, which are more common in manufacturing. Therefore, to realize Solid Freeform Fabrication (SFF), this paper researches CLFDM with VEF (simultaneously changing the direction and the dimension of extruded filament according to manufacturing demand of the curved layer), which would be a fundamental study and a foundation for Advanced Design for Additive Manufacturing (ADFAM), slicing and path planning (extruder path generation) in 3D space. To realize slicing and printing with homogeneous and inhomogeneous extruded filament between consecutive layers and within the layer (flat or curved), models of flat layer FDM and CLFDM with VEF are respectively established. Then, the relationships among key process parameters are analyzed. Finally, graphical simulation of the proposed strategy based on a vase is provided to verify its effectiveness and advantages from a theoretical point of view. In general, variable direction of extruded filament along tangential directions of part surface imparts smoother surfaces, instead of rough exterior appearance resulting from stair-step effects. And variable dimension of extruded filament maximizes material extruded to increase build speed wherever allowed and minimizes deposition size for resolution whenever needed, resulting in curved layer surfaces with uneven layer thickness and having tiny features.

The selection of biomimetic prototypes mostly depends on the subjective observation of a designer. This research uses TRIZ to explore some inferential steps in bionic design of the heavy machine tool column. Conflict resolution theory of TRIZ is applied to describe improved and deteriorated parameters and a contradiction matrix is used to obtain recommended inventive principles. A reference table of solutions corresponding to the biological phenomenon and TRIZ solutions is formed to expedite retrieving the biomimetic object. Based on the table, herbaceous hollow stem is selected to imitate column structure. Four kinds of plant are chosen from the biological database. To select the best from four candidates, a bionic ideality evaluation index is proposed based on similarity analysis and ideality evaluation theory in TRIZ. Thus, the bionic effect can be described and compared quantitatively. Bionic configuration is then evolved concerning manufacturing requirements. Size optimization of stiffener thicknesses is implemented finally, and satisfactory results of the lightweight effect is obtained.

Despite of its tremendous merits in producing parts with complex geometry and functionally graded materials, additive manufacturing (AM) is inherently an energy expensive process. Prior studies have shown that process parameters, such as printing resolution, printing speed, and printing temperature, are correlated to energy consumption per part. Moreover, part geometric accuracy is another major focus in AM research, and extensive studies have shown that the geometric accuracy of final parts is highly dependent on those process parameters as well. Though both energy consumption and part geometric accuracy heavily depend on the process parameters in AM processes, jointly considering the dual outputs in AM process is not fully investigated. The proposed study aims to obtain a quantitative understanding of the impact of these process parameters on AM energy consumption given part quality requirements, such as geometric accuracy. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical regression analysis technologies, the study quantifies the correlation between AM process parameters and energy consumption as well as the final geometric accuracy measure. An optimization framework is proposed to minimize the energy consumption per part. The Kuhn-Tucker non-linear optimization algorithm is used to identify the optimal process parameters. This study is of significance to AM energy consumption in terms of jointly considering energy consumption and final part geometric accuracy in the optimization framework.

MEMS diaphragms utilizing an embedded cantilever array to sense pressure were investigated in an effort to detect blast-induced loads that potentially cause mild traumatic brain injury (mTBI). Overhanging cantilever beams of varying length sequentially contacted convexly deformed diaphragms during pressure loading to enable binned binary threshold pressure sensing. Diaphragms and overhanging cantilever beam arrays photolithographically defined using double-layer SOI-DRIE processes were designed to minimize footprint and maximize sensing resolution while avoiding diaphragm fracture. Analytic models based on classical plate theory were correlated with a FEA model to guide design decisions by predicting deflection behavior of circular diaphragms under uniformly distributed pressure loads. Fabricated sensors experimentally tested under static conditions revealed good agreement with modeling, and indicated such a sensor may be well-suited for integration into a future wearable device capable of sensing potentially mTBI-causing blasts using minimal power.

In this study, a three-dimensional bioscaffold printer was developed to fabricate biocompatible scaffolds from water-soluble materials for application in cell studies. A gelatin/sodium alginate solution was used to produce the scaffolds via a fused deposition modelling (FDM) printing method using the modified 3D printer. Modifications and improvements to the material feeding system, printing head, and printing platform were made, with additional optimization of the printing parameters, such as the feed rate, printing rate, and printing head size to investigate the precision and accuracy of two-dimensional and 3D bioscaffold printing. In addition to modifications of the feeding system from the original solid to the new liquid state material, a heating probe and coil were added to maintain the liquid phase. The printing nozzle was also altered to allow for the feed material and a cross-linking agent to mix prior to printing; enabling cross-linked scaffolds to be produced. Furthermore, the printing surface was integrated with a filter to allow for excess fluid to drain from the scaffold after printing and cross-linking. The results of this study revealed that the optimal printing parameters for producing a 2D 15.3 mm × 15.3 mm square was with a printing head-platform distance of 4 mm, material feed rate of 5 mL/min, printing rate of 35 mm/s and a printing head diameter of 0.4 mm. In addition, it was found that the printing speed and the printed image size and resolution are correlated, as such, the smallest dimensions able to be printed is 10.3 mm × 10.3 mm, with a line width of 1 mm. In regards to 3D scaffolds, the printed scaffolds had dimensions of 20 mm × 20.15 mm with a height of 7.5 mm; which were found to support the growth of mouse fibroblast cells.

The smallest forming unit in two-photon photopolymerization (TPP) micro-manufacturing technology is the voxel, the appearance of which resembles a spheroid. Traditional TPP micro-manufacturing is planned using the minor-axis dimension of a spheroid, which is smaller than its major-axis, thus, the spatial resolution can achieve submicron level. TPP can be used to manufacture microstructures with complex shapes. However, such fine spatial resolution inevitably lowers the overall manufacturing speed. For a microstructure with a height of hundred micrometers, the prolonged manufacturing time substantially increases the risk of manufacturing failure. Whereas typical methods use the minor-axis dimension for manufacturing planning, this study developed a novel major-axis planning (MAP) method that uses the longest dimension of the voxel. In this study, the MAP was realized in a 4-axis micro-manufacturing system (i.e., a rotation axis was added to the 3-axis motion stage). Specifically, a specially designed L-type glass substrate was first placed on the rotation axis and was rotated 90°, rendering the working plane parallel to laser beams. Subsequently, horizontal laser scanning was performed, during which the laser focus moved from the working plane horizontally, to polymerize a high-aspect-ratio structure. The commercial polymer OrmoComp was used with the MAP; only 10 s was required to fabricate a microstructure that had a height of 100 μm and an aspect ratio of 17. This study verified that TPP micro-manufacturing on a voxel’s major axis can fabricate microstructures. Moreover, the L-type glass substrate can be controlled programmably to rotate an L-type glass substrate for 4-axis TPP micro-manufacturing in the future.

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

This paper proposes a method to resolve trade-off problems between functionalities, which hinders unconventional improvement of a product. As products have become increasingly complex, it becomes difficult to grasp the whole aspects of a product. In order to resolve trade-off problems of a complex product, it is required to model the product in an appropriate form and to gather knowledge of experts in each domain. Although there have been several models to tackle with this issue, modelling still poses difficulties due to lack of clear guideline. This paper classified models into three types: function-based, cognition-based and physics-based. Then, their roles and description guidelines are clarified. As a function-based model depicts functionality of a product in a rather simple description, it is employed to specify significant tradeoffs. A cognition-based model depicts the designers’ recognition of physical phenomena while a physics-based model rigorously depicts the physical phenomena. A cognition-based model is appropriate for ideation while physics-based model contributes to objectivity of a model. This paper proposes complimentary modelling and use of cognition-based and physics-based models. To support ideation of solution to the trade-offs, TRIZ is applied. The proposed method is demonstrated and validated by the case study of Continuously Variable Transmission.

Different from most traditional manufacturing processes, the productivity of additive manufacturing (AM) is independent of the geometric complexity from the object to be built. This characteristic opens up tremendous potentials to realize mass customization. However, the AM-specific parts, such as customized products, need to be represented by millions of triangle meshes. Moreover, a large number of sliced layers are needed with the increased resolution of AM machines. These together pose a fundamental challenge in slice generation. The slicing procedure for a single customized model can take tens of minutes or even hours to complete, and the time consumption becomes more prominent in the context of mass customization. We propose a new slicing paradigm which capitalizes upon the similarities among customized models, and it reuses information obtained from the template model slicing. The idea of information reuse is implemented at several different levels depending on variations between the customized model and the template model. Experimental results show that the proposed slicing paradigm can significantly reduce the time consumption on slicing process, and ultimately fulfill mass customization enabled by AM.

This paper presents a gradient-based multi-component topology optimization (MTO) method for structures assembled from components made by powder bed additive manufacturing. It is built upon our previous work on the continuously-relaxed MTO framework utilizing the concept of fractional component membership. The previous attempt on the integration of the relaxed MTO framework with additive manufacturing constraints, however, suffered from numerical instability for larger size problems, limiting its application to 2D low-resolution examples. To overcome this difficulty, this paper proposes an improved MTO formulation based on a design field regularization and a nonlinear projection of component membership variables, with a focus on powder bed additive manufacturing. For each component, constraints on the maximum allowable build volume (i.e., length, width, and height), the elimination of enclosed voids, and the minimum printable feature size are imposed during the simultaneous optimization of the overall base topology and component partitioning. The scalability of the new MTO formulation is demonstrated by a few 2D examples with much higher resolution than previously reported, and the first reported 3D example of MTO.

The objective of this research is to investigate the performance of a solid model similarity assessment method. This method is used to assess the similarity of tessellated solid models, where the tessellated geometry is in the form of triangles — specifically, the method compares STL files. A histogram of (triangle) tessellation areas is generated for each solid model being compared. The difference in the histograms of two solid models indicates their dissimilarity. The performance of the solid model similarity assessment method is evaluated by varying tessellation resolutions, and by varying histogram bin sizes. The solid model similarity assessment method is also compared to methods from literature. The comprehensive testing was performed using 867 solid models from the Engineering Shape Benchmark. It is found that the method was robust in its sensitivity to histogram bin sizes, and robust in its sensitivity to tessellation resolution. It is found that for small retrieval sizes, precision is relatively high. It is also found that this method outperformed methods from literature when comparing models that are rectangular, flat, thin, and/or cubic. Additionally, shortcomings of this method and related future work is identified.

Recent developments in imaging systems have seen the implementation of a radar matched-filtering approach. The goal of the imaging system is to obtain information about an unknown object embedded in the system, by controlling the parameters of the input and measuring the response to the known input. The main merit of using matched filtering in imaging systems is the improvement of Signal to Noise Ratio (SNR). However, the correlation process used in matched filtering may result in a loss of resolution. One way to compensate for lost resolution is via pulse compression. Linear frequency modulated sinusoidal waveforms (chirps) have the property of pulse compression after correlation. Hence, both SNR and resolution can be enhanced by matched-filtering and pulse compression with a chirp. However, the theory behind the effect of chirp parameters on resolution is still not clear. In this paper, a one-dimensional theory of matched-filter imaging with a pulse compressed linear frequency modulated sinusoidal chirp is developed. The effect of the chirp parameters on the corresponding signal is investigated, and guidelines for choosing the chirp parameters for resolution considerations are given based on the developed theory and simulations. The results showed that by manipulating the center frequency, bandwidth, and duration of the chirp, the resolution can be easily enhanced.

Maintenance of production facilities is important in various industries. Since production facilities degrade their original functions during their long life-cycle, it is necessary to periodically make deterioration diagnosis and renovate to restore functions. In recent years, the terrestrial laser scanner allows us to capture dense point-clouds from large production facilities. Point-based virtual environment is promising for supporting maintenance of production facilities. In this paper, we discuss the deterioration diagnosis of production facilities based on point-clouds when the original shapes of production facilities are unknown. As an example of production facilities, we consider the blast furnace, which is mainly used to produce metals from molten materials. We classify deterioration on the blast furnace as wearing, scaffolding, and cracks, and automatically detect them from point-clouds. In our method, the normal wall shape is estimated by fitting low-resolution B-spline surfaces to point-clouds, and deterioration is detected as the difference between the reference surface and the point-clouds. While wearing and scaffolding regions are relatively large, cracks are thin lines. In order to detect different scales of deterioration, we introduce the reference surfaces with multiple resolutions. In our experiments, the three types of deterioration could be successfully detected from dense point-clouds.

Optimization of water use relies on accurate measurement of water status of crops. Stem water potential (SWP) has become one of the most popular methods to monitor the water status of almond trees. However, it needs to take twice visit and at least thirty minutes to obtain one measurement, which makes it very difficult to understand the water status information in the orchard level. Unmanned aerial vehicle (UAV) based remote sensing promises to deliver reliable and precise field-scale information more efficiently by providing multispectral higher-resolution images with much lower cost and higher flexibility. This paper aims to extract almond water status from UAV-based multispectral images via building the correlation between SWP and vegetation indices. Different from the traditional method that focuses on normalized difference vegetation index (NDVI) means, higher-order moments of non-normalized canopy distribution descriptors were discussed to model SWP measurements. Results showed that the proposed methods performed better than traditional NDVI mean.

Linear and bilinear time-frequency distributions (TFDs) have been employed in planetary gearbox fault diagnosis. For linear TFDs, there is a trade-off between the time localization and frequency resolution and the spectrogram may not have correct energy marginals. For bilinear TFDs, they cannot be interpreted as an energy distribution because of the existence of possible negative values even though they are designed for energy density representation. To overcome these shortcomings, TFDs based on copula theory have been reported in the literature. In this paper, we analyze two simulated data sets using linear TFD and copula-based TFD. The results show that the constructed copula-based TFD has desirable properties of being positive, free from cross-term interference, having high time-frequency resolution and matching well with true marginals. The copula-based TFD is also able to locate fault-induced impulses by vertical lines over a certain frequency range in the time-frequency domain. Consequently, this study confirms the advantages of the copula-based TFD as an energy distribution and its capability in fault detection for planetary gearboxes.

A methodology is proposed for uncertainty quantification to accurately predict the mechanical response of lattice structures fabricated by additive manufacturing. Effective structural properties of the lattice structures are characterized using a multi-level stochastic upscaling process that propagates the quantified uncertainties at strut level to the lattice structure level. To obtain realistic simulation models for the stochastic upscaling process, high resolution finite element models of individual struts were reconstructed from the micro-CT scan images of lattice structures which are fabricated by selective laser melting. The upscaling process facilitates obtaining of the homogenized strut properties of the lattice structure to reduce the computational cost of the detailed simulation model for the lattice structure. Bayesian Information Criterion is utilized to quantify the uncertainties with parametric distributions based on the statistical data obtained from the reconstructed strut models. A systematic validation approach that can minimize the experimental cost is also utilized to assess the predictive capability of the stochastic upscaling method used at strut level and lattice structure level. In comparison with physical compression tests, the proposed methodology of linking the uncertainty quantification with multi-level stochastic upscaling method enabled an accurate prediction of the elastic behavior of the lattice structure by accounting for the uncertainties introduced by the additive manufacturing process.

In this paper the functional redundancy of a spherical parallel manipulator performing a pointing task is exploited to optimize its posture upon minimizing dynamic index. A general method to derive the inertia matrix reduced to the mobile platform via screw theory is presented. This matrix encases geometrical and inertial information of all the bodies and it allows a simple computation of dynamic indices due to its feature of being dependent only on the pose of the robot. The indices are used to compute the objective function of the optimization problem, while the orientation of the pointing task constitutes the constraint equations. The posture-optimization is used as a redundancy-resolution and it is extended to any pointing direction. Optimal postural maps are obtained and then used to drive the optimal planning of pointing trajectories by using Bézier curves. To this aim, a higher level optimization problem than previous one is solved and inverse dynamic simulations are conducted to verify the results.

During road travel, obstacles can impede productivity or durability for many different vehicles and render discomfort or injuries for the people within. Using remote sensing techniques, information from the surroundings can be acquired and analysed to identify obstacles ahead. The subsequent analysis can create a decision support for how the vehicle or driver should act upon encountered obstacles, through either autonomous control, guidance to the driver or a combination of both. In this paper, an experimental setup was created to mimic an obstacle in the shape of a speed bump on a flat road. An RGB-D camera was used to acquire information while travelling towards the speed bump. Afterwards, the acquired information was analysed by an estimation of the normal vector for each point in a 2D depth map. The resulting data from the experiments had sufficient resolution, speed and quality to retrieve proper identify obstacles or targets indoors with an accuracy of 2%. Obstacles were measured and identified in less than 20 ms where processing time mainly comprised data transfer from the USB-bus. The obstacle identification can be used to e.g. actively control the vehicle suspension, send feedback to the driver about obstacles ahead or optimise speed and direction for autonomous vehicles.

The objective of this research is to model the geometric variability of the glenoid (the “socket” component of the “ball and socket” connection of the shoulder joint) of the scapula. The model must capture the observed variability with sufficient resolution such that it informs operative and design decisions. This required the quantification of variability in landmark locations and relevant bone geometry. Landmarks were placed on the existing glenoid meshes, such that they provided enough information to represent the geometry, while being consistent across each glenoid. Additionally, the surface geometry of the glenoid vault was modeled. This required the application of existing mathematical and statistical modeling approaches, including geometric fitting, radial basis functions, and principal component analysis. The landmark identification process represented the glenoid in new manner. The work was validated against existing approaches and CT scans from 42 patients. A range of information on shoulder geometries can assist with preoperative planning, as well as implant design, for Total Shoulder Arthroplasty (TSA). Principal component analysis (PCA) was used to quantify the variability of shape across the glenoid landmarks, and synthesize new glenoid models. The process of creation of these shoulder geometries may possibly be useful for the study of other joints. The models created will help surgeons and engineers to understand the effects of osteoarthritis on bone geometry, as well as the range of variability present in healthy shoulders.