This PDF file contains the front matter associated with SPIE Proceedings Volume 10602 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Ford has an extensive history of developing and utilizing smart and innovative materials in its vehicles. In this paper, we present new challenges the automotive industry is facing and explore how intelligent uses of smart materials can help provide solutions. We explore which vehicle attributes may provide most advantageous for the use smart materials, and discuss how smart material have had technical challenges that limit their use. We also look at how smart materials such as gecko inspired adhesion is providing opportunities during the vehicle assembly process by improving manufacturing quality, environmental sustainability, and worker safety. An emerging area for deployment of smart materials may involve autonomous vehicles and mobility solutions, where customer expectations are migrating toward a seamless and adaptive experience leading to new expectations for an enhanced journey. Another area where smart materials are influencing change is interior and exterior design including smart textiles, photochromatic dyes, and thermochromatic materials. The key to advancing smart materials in automotive industry is to capitalize on the smaller niche applications where there will be an advantage over traditional methods. Ford has an extensive history of developing and utilizing smart and innovative materials. Magnetorheological fluids, thermoelectric materials, piezoelectric actuators, and shape memory alloys are all in production. In this paper we present new challenges the automotive industry is facing and explore how intelligent uses of smart materials can help provide solutions. We explore which vehicle attributes may provide most advantageous for the use smart materials, and discuss how smart materials have had technical challenges that limit their use. An emerging area for deployment of smart materials may involve autonomous vehicles and mobility solutions, where customer expectations may require a seamless and adaptive experience for users having various expectations.

Industry 4.0, or the fourth industrial revolution, that blurs the boundaries between the physical and the digital, is underpinned by vast amounts of data collected by sensors that monitor processes and components of smart factories that continuously communicate amongst one another and with the network hubs via the internet of things. Yet, collection of those vast amounts of data, which are inherently imperfect and burdened with uncertainties and noise, entails costs including hardware and software, data storage, processing, interpretation and integration into the decision-making process to name just the few main expenditures. This paper discusses a framework for rationalizing the adoption of (big) data collection for Industry 4.0. The pre-posterior Bayesian decision analysis is used to that end and industrial process evolution with time is conceptualized as a stochastic observable and controllable dynamical system. The chief underlying motivation is to be able to use the collected data in such a way as to derive the most benefit from them by trading off successfully the management of risks pertinent to failure of the monitored processes and/or its components against the cost of data collection, processing and interpretation. This enables formulation of optimization problems for data collection, e.g. for selecting the monitoring system type, topology and/or time of deployment. An illustrative example utilizing monitoring of the operation of an assembly line and optimizing the topology of a monitoring system is provided to illustrate the theoretical concepts.

The Actor model of concurrent computation discretizes a problem into a series of independent units or actors that interact only through the exchange of messages. Without direct coupling between individual components, an Actor-based system is inherently concurrent and fault-tolerant. These traits lend themselves to so-called “Big Data” applications in which the volume of data to analyze requires a distributed multi-system design. For a practical demonstration of the Actor computational model, a system was developed to assist with the automated analysis of Nondestructive Evaluation (NDE) datasets using the open source Myriad Data Reduction Framework. A machine learning model trained to detect damage in two-dimensional slices of C-Scan data was deployed in a streaming data processing pipeline. To demonstrate the flexibility of the Actor model, the pipeline was deployed on a local system and re-deployed as a distributed system without recompiling, reconfiguring, or restarting the running application.

In this study, the surface response to excitation method (SuRE) is investigated using a data-driven method for load monitoring on a laminated composite plate structure. The SuRE method is an emerging approach in ultrasonic wavebased structural health monitoring (SHM) field. In this method, a range of high-frequency, surface-guided waves are excited on the structure using piezoceramic elements. The waves propagate on the structure and interact with internal or surface damages. Initially, a baseline data of the intact structure is created by measuring the frequency transfer function between the excitation and sensing point. The integrity of structure is evaluated by monitoring changes in the frequency spectrums. The SuRE method has effectively been used for a variety of SHM applications including the detection of loose bolts, delamination in composite structures, internal corrosion in pipelines, and load and impact monitoring. Data obtained using the SuRE method was used for identifying the location of the applied load on a laminated composite plate using Support Vector Machine (SVM). A set of two piezoelectric elements were attached on the surface of the plate. A sweep excitation (150-250 kHz) generated surface-guided waves, and the transmitted waves were monitored at the sensory positions. The reference data set was measured simultaneously from the sensors. The plate was subjected to static loads while health monitoring data was being captured using the SuRE method. The confusion matrix indicated that the model classified correctly with up to 99.8% accuracy.

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

This study explored the potential of combining point cloud data (PCD) and 2D/3D PCD algorithms for deformation monitoring of common reinforced concrete (RC) structural elements. The RC specimens tested in the laboratory include three beam (30x50x400 cm) elements and three slab (150X15X350 cm) elements under uniaxial loading. PCDs of each specimen were acquired by 3D laser scanner over the loading processes and used to extract the deformation at each loading step. Deformation monitoring was also accompanied with traditional LVDT instrumentations for later comparison with the PCD ones. Each specimen was loaded with several steps till failure; within each interval the PCDs of RC specimens were scanned. 2D edge extractions and direct 3D surface fittings were used as the PCD algorithms for extracting the deformation information of the specimens at each loading step. By comparing the results with LVDT readings, it was shown that PCD algorithms can enhance the accuracy of laser scanner for deformation monitoring of RC structural elements and give comparable results with LVDTs. It is also shown that the enhanced measuring accuracy is subjected to the PCD algorithms. Nevertheless, this study successfully demonstrated the applicability of 2D/3D PCD algorithms for whole field deformation monitoring of RC structural elements.

Laser-based ultrasound systems are described that utilize the ultrasonic bulk-wave sensing to detect the damages and flaws in the aerospace structures. These systems apply pulse-echo or through transmission methods to detect longitudinal through-the-thickness bulk-waves. These thermoelastic waves are generated using Q-switched laser and non-contact sensing is performed using a laser Doppler vibrometer (LDV). Laser-based raster scanning is performed by either twoaxis translation stage for linear-scanning or galvanometer-based laser mirror scanner for angular-scanning. In all ultrasonic propagation imagers, the ultrasonic data is captured and processed in real-time and the ultrasonic propagation can be visualized during scanning. The scanning speed can go up to 1.8 kHz for two-axis linear translation stage based B-UPIs and 10 kHz for galvanometer-based laser mirror scanners. In contrast with the other available ultrasound systems, these systems have the advantage of high-speed, non-contact, real-time, and non-destructive inspection. In this paper, the description of all bulk-wave ultrasonic imagers (B-UPIs) are presented and their advantages are discussed. Experiments are performed with these system on various structures to proof the integrity of their results. The C-scan results produced from non-dispersive, through-the-thickness, bulk-wave detection show good agreement in detection of structural variances and damage location in all inspected structures. These results show that bulk-wave UPIs can be used for in-situ NDE of engineering structures.

This study aims to assess the feasibility of using a combination of thin elastomer tubes and SMA wires to develop an active catheter. Cardiac catheters have been widely used in investigational and interventional procedures such as angiography, angioplasty, electro- physiology, and endocardial ablation. The commercial models manually steer inside the patient’s body via internally installed pull wires. Active catheters, on the other hand, have the potential to revolutionize surgical procedures because of their computer-controlled and enhanced motion. Shape memory alloys have been used for almost a decade as a trustworthy actuator for biomedical applications. In this work, SMA wires were attached to a small pressurized elastomer tube to realize deflection. The tube was pressurized to maintain a constant stress on the SMA wires. The tip motion via actuation of SMA wires was then measured and reported. The results of this study showed that by adopting an appropriate training process for the SMA wires prior to performing the experiments and adopting an appropriate internal pressure for the elastomer tube, less external loads on SMA wires would be needed for a consistent actuation.

Currently, micro fabrication has gained popularity because of miniaturization and densification of devices. Accordingly, the importance of optical shape measurement to detect processing defects and verify the necessity of re-processing is increasing. On the other hand, conventional optical shape-sensing methods require complicated settings such as strict calibration and liquid immersion, and long examination durations; however, there is a requirement for a rapid examination method. Thus, in this research, we propose an optical shape-measuring method for drilled objects, using the light leakage through holes. Specifically, improved precision can be expected by scanning target holes illuminated by a monochromatic LED from the back with a micro pinhole installed on a high-precision stage, and detecting light using an area camera passing through the pinhole. Images are captured at every scanning step of the stage, and finally, one integrated image is generated. An advantage of this method is that even if the diameter of the pinhole is larger than the minimum step of the stage, the camera can detect the amount of light leakage; hence, a high-precision image can be captured by our method. Moreover, the proposed method reduces the labor required for setup and shortens the examination time because it does not require liquid immersion and strict calibration for each object. Through the experiment, we verified the proposed method using a pinhole having a diameter of 10 μm, and obtained the image of through holes. As future work, the scanning speed could be improved using multi-arrayed micro pinholes.

In this research, a high compact configuration of brake featuring MR fluid (MRB) is proposed. The proposed MRB consists of a rotor with multiple trapezoidal teeth acting at multiple magnetic poles of the brake. Two counter coils are placed on each side-housing of the brake. The inner face of each side-housing also has trapezoidal shape mating with the trapezoidal teeth of the rotor via MRF layer. By applying counter currents to the two coils, a magnetic fluid is generated with magnetic flux going across the MRF layer (MRF duct) between the rotor teeth and their mating poles on the housing. By using multiple poles with trapezoidal shape, a high braking torque of the brake is expected while the size of the brake is still kept to be compacted. After a review of MRB state of the art, configuration of the proposed MRB is presented. The modeling of the actuator is then derived based on Bingham rheological model of MRF and magnetic finite element analysis (FEA). The optimal design of the MRB is then performed in order to minimize the mass of the MRB when braking torque is constrained to be greater than a required value. From the optimal design result, performance characteristics of the actuator is simulated and compared with previously developed MRB.

Crack detection during the manufacturing process of pressed panel products is an important aspect of quality management. Tradition approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the press-forming process. In this study, we performed automated crack detection by integrating two image processing techniques with a multi-view-camera system. The first technique is based on evaluation of the edge lines which are extracted from a percolated object image. This technique could detect a crack without a reference image. Almost all of the edge lines of the panels show smooth variances of angle on the edges. When a crack occurs in panel products, an angle higher than 140 degree by the edge lines would appear, which could be used as an indication of crack presence. Another technique applies local image amplitude mapping (LAM) and compares a test image with the reference image. LAM is used to alleviate the problem associated with that the captured images during the manufacturing stage are not aligned against the reference image. The features created by LAM subtraction between the reference and test image are used to identify a crack. Before crack detection, multi-view images of a panel product are captured using multiple cameras. Afterwards, cracks are detected using both crack detection techniques based on image processing. The proposed technique is demonstrated in an actual manufacturing lines with real panel products. Experimental results clearly show that proposed technique could effectively improve the detection rate and speed for pressed panel products.

The smartphones of today have come a long way from its first inception. Technologically they are highly advanced and can be put to various uses that could not be dreamt of, a decade back. There are instruments now out in the market which when combined with smartphones can become a powerful tool to conduct nondestructive evaluations. Yes, that’s right, NDE. These instruments can be applied to solve simple household issues. For example, detecting heat leaks using a detachable Infrared camera with the cellphone. In this presentation, results from android adaptable NDE devices and from standard NDE devices will be compared. The devices to be used are: • Eddy Smart - a transducer is attached to the phone that makes eddy current measurement on the specimen and stores data on the phone. • FLIR ONE Thermal Imaging - an Infrared camera that is attached to a smartphone through USB jack and relay The devices to be used are: • Eddy Smart - a transducer is attached to the phone that makes eddy current measurement on the specimen and stores data on the phone. • FLIR ONE Thermal Imaging - an Infrared camera that is attached to a smartphone through USB jack and relays heat images to the phone. • PCUS Pocket – This device is the size of a hard disk but makes accurate ultrasonic measurements at par with the standard devices, when connected to a laptop/PC. Test samples will be developed to quantify the performance of these low-cost systems. A comparison of the results from standard and smart, portable devices validate the performance of the new devices. It determines the areas where the technology needs to be further developed. Advantages and limitations of the android adaptable systems with respect to nondestructive testing will be discussed. Acknowledgement: The authors greatly acknowledge the support from Fraunhofer IKTS, Berlin and the staff and faculty at Centre for Nondestructive Evaluation (CNDE), Ames. References: 1. https://walabot.com/ 2. http://www.flir.com/flirone/content/?id=81730 3. http://www.eddycation.de/41265.html

Currently available methods and systems for testing components for survival and performance under shock loading suffer from several shortcomings for use to simulate high-G acceleration events with relatively long duration. Such events include most munitions firing and target impact, vehicular accidents, drops from relatively high heights, air drops, impact between machine components, and other similar events. In this paper, a new class of shock testing machines are presented that can be used to subject components to be tested to high-G acceleration pulses of prescribed amplitudes and relatively long durations. The machines provide for highly repeatable testing of components. The components are mounted on an open platform for ease of instrumentation and video recording of their dynamic behavior during shock loading tests.

In industry, the AC motor is widely used because of low price, power availability, low cost maintenance. The main disadvantages of AC motors compared to DC motors are difficulty in speed and torque control, requiring expensive controllers with complex control algorithms. This is the basic limitations in the widespread adoption of AC motor systems for industrial automation. One feasible solution for AC motor control is using MRF (magneto-rheological fluid) based clutches (shortly called MR clutches) Although there have been many studies on MR clutches, most of these clutches used traditional configuration with coils wound on the middle cylindrical part and a compotator is used to supply power to the coils. Therefore, this type of MR clutches possesses many disadvantages such as high friction and unstable applied current due to commutator, complex structure which causes difficulty in manufacture, assembly, and maintenance. In addition, the bottleneck problem of magnetic field is also a challenging issue. In this research, we will develop a new type of MR clutches that overcomes the abovementioned disadvantages of traditional MR clutches and more suitable for application in controlling of AC motor. Besides, in this study, speed and torque control system for AC motors using developed MR clutches is designed and experimental validated.

This paper presents the comparison study of wavenumber-based defect detection performance in full field laser scanning techniques. Two types of wave excitation are used for damage detection; guided waves and standing waves. A piezoelectric actuator is mounted on surface of the thin plate to generate guided and standing waves with a single excitation frequency. Subsequent responses on each grid point are measured using a Laser doppler vibrometer (LDV) with a mirror tilting device. Full field wave image is then generated from the measured wave signals. After the laser scanning, wavenumber based processing is applied to the measurements to generate two types of full wave field images and to detect structural damage. Three wavenumber based signal processing are applied to the wave filed images to estimate damage size and depth, including the Local wavenumber mapping, Acoustic Wavenumber Spectroscopy, 2D wavelet based wavenumber spectroscopy. For the comparison of these two techniques, several experiments are performed on thin walled structures with several different types of damage, including corrosion in an aluminum plate and debonding on composite plates. This paper outlines pros and cons of these two excitation techniques in terms of several parameters, including damage sensitivity, processing time and their applicability.

Inflatable technologies have proven useful in consumer goods as well as in more recent applications including civil structures, aerospace, medical, and robotics. However, inflatable technologies are typically lacking in their ability to provide rigid structural support. Particle jamming improves upon this by providing structures which are normally flexible and moldable but become rigid when air is removed. Because these are based on an airtight bladder filled with loose particles, they always occupy the full volume of its rigid state, even when not rigidized. More recent developments in layer jamming have created thin, compact rigidizing surfaces replacing the loose volume of particles with thinly layered surface materials. Work in this area has been applied to several specific applications with positive results but have not generally provided the broader understanding of the rigidization performance as a function of design parameters required for directly adapting layer rigidization technology to other applications. This paper presents a parametric design study of a new layer jamming vacuum rigidization architecture: tile-based vacuum rigidization. This form of rigidization is based on layers of tiles contained within a thin vacuum bladder which can be bent, rolled, or otherwise compactly stowed, but when deployed flat, can be vacuumed and form a large, flat, rigid plate capable of supporting large forces both localized and distributed over the surface. The general architecture and operation detailing rigidization and compliance mechanisms is introduced. To quantitatively characterize the rigidization behavior, prototypes rigidization surfaces are fabricated and an experimental technique is developed based on a 3-point bending test. Performance evaluation metrics are developed to describe the stiffness, load-bearing capacity, and internal slippage of tested prototypes. A set of experimental parametric studies are performed to better understand the impact of variations in geometric design parameters, operating parameters, and architectural variations on the performance evaluation metrics. The results of this study bring insight into the rigidization behavior of this architecture, and provide design guidelines and expose tradeoffs to form the basis for the design of tile-based rigidization surfaces for a wide range of applications.

There is a need for high-performance force sensors capable of operating at frequencies near DC while producing a minimal mass penalty. Example application areas include steering wheel sensors, powertrain torque sensors, robotic arms, and minimally invasive surgery. The beta crystallographic phase polyvinylidene fluoride (PVDF) films are suitable for this purpose owing to their large piezoelectric constant. Unlike conventional capacitive sensors, beta crystallographic phase PVDF films exhibit a broad linear range and can potentially be designed to operate without complex electronics or signal processing. A fundamental challenge that prevents the implementation of PVDF in certain high-performance applications is their inability to measure static signals, which results from their first-order electrical impedance. Charge readout algorithms have been implemented which address this issue only partially, as they often require integration of the output signal to obtain the applied force profile, resulting in signal drift and signal processing complexities. In this paper, we propose a straightforward real time drift compensation strategy that is applicable to high output impedance PVDF films. This strategy makes it possible to utilize long sample times with a minimal loss of accuracy; our measurements show that the static output remains within 5% of the original value during half-hour measurements. The sensitivity and full-scale range are shown to be determined by the feedback capacitance of the charge amplifier. A linear model of the PVDF sensor system is developed and validated against experimental measurements, along with benchmark tests against a commercial load cell.

MEMS piezoelectric energy harvester (PEH) has been widely designed in cantilever beam style because of ease of fabrication and effective to generate large strain and output power. There are already several studies on tapered beam shapes to improve the overall performance of energy harvested. In this paper, we investigate cantilever beam type PEH in rectangular, trapezoidal and triangle shapes, and the devices are limited to the area smaller than 1cm × 1 cm for better flexibility in applications. The power output and the life time of each shape of devices are fabricated and characterized. The output power are tested with optimal resistance loads, and the output power are 145.3 μW, 125.3 μW and 107.8 μW for triangle, trapezoidal and rectangular shapes of devices respectively under excitation of 0.5g acceleration vibration level in the resonant frequency of the transducer. The tip displacements of the 3 devices are 3.05 mm, 2.66 mm, and 2.44 mm for triangular, trapezoidal and rectangular shape devices, respectively. To study the lifetime and durability issue, triangular and rectangular devices are excited under 0.2g to 1g for 24 hours. The resonant frequency shifting, tip displacement and open circuit voltage changing are monitored will be detailed in the paper.

Novel piezoelectric-based self-powered impulse detecting switches are presented. The switches are designed to detect shock loading events resulting in acceleration or deceleration above prescribed levels and durations. The prescribed acceleration level and duration thresholds are adjustable. They are provided with false trigger protection logic. The impulse switches are provided with electronic and logic circuitry to detect prescribed impulse events and reject events such as high amplitude but short duration shocks, and transportation vibration and similar low amplitude and relatively long duration events. They can be mounted directly onto electronics circuit boards, thereby significantly simplifying the electrical and electronic circuitry, simplifying the assembly process and total cost, significantly reducing the occupied volume, and in some applications eliminating the need for physical wiring to and from the impulse switches. The design of prototypes and testing under realistic conditions are presented.

The national measurement standards from the point of view of traceability of the results of measurement in additive manufacturing in Ukraine are considered in the paper. The metrological characteristics of the national primary measurement standards in the field of geometric, temperature, optical-physical and time-frequency measurements, which took part in international comparisons within COOMET projects, are presented. The accurate geometric, temperature, optical-physical and time-frequency measurements are the key ones in controlling the quality of additive manufacturing. The use of advanced CAD/CAE/CAM systems allows to simulate the process of additive manufacturing at each stage. In accordance with the areas of the technology of additive manufacturing, the ways of improving the national measurement standards of Ukraine for the growing needs of metrology of additive manufacturing are considered.

Failures detection of rotating machine elements, such as gears, is an important issue. The purpose of this study was to try to solve this issue by printing conductive ink on gears to manufacture condition-monitoring sensors. In this work, three types of crack detection sensor were designed and the sprayed conductive ink was directly sintered on polyimide (PI) - coated polyamide (PA) 66 gears by laser. The result showed that it was possible to produce narrow circuit lines of the conductive ink including Ag by laser sintering technique and the complex shape sensors on the lateral side of the PA66 gears, module 1.0 mm and tooth number 48. A preliminary operation test was carried out for investigation of the function of the sensors. As a result of the test, the sensors printed in this work should be effective for detecting cracks at tooth root of the gears and will allow for the development of better equipment and detection techniques for health monitoring of gears.

Many additive manufacturing (AM) systems are based on laser technology. The advantage of laser technology is that it provides a high-intensity and high-collimation energy beam that can be controlled. Since AM requires that the material on each layer has to be solid or connected to the previous one, the energy of laser radiation is exactly the needed technical tool for the processing of the material. AM uses two types of laser processing: cutting and heating. One of the most popular (common) types of measurements in the field of laser metrology is the control of the energy parameters of the sources of laser radiation. At present, calorimeters provide the highest accuracy of absolute measurements of laser radiation in the power range from several watts to tens of kilowatts. The main elements that determine the accuracy of reproduction, maintenance and transfer of the unit of laser power are the primary measuring converters (PMCs), which are the part of the equipment of the national primary measurement standards of Ukraine. A significant contribution to the uncertainty budget of the primary measuring calorimetric converter is the unbalanced replacement of laser radiation by the heat flux that calibrates this converter. The heterogeneous internal structure of the calorimetric primary converter, the nonlinearity of processes occurring in it, and the multifactorial process of its calibration substantially complicate the development of primary measuring converters. The purpose of this paper is to simulate the thermal field of the primary converter for maximum reduction of the uncertainty of calibration. The presented research is a part of the scientific work that NSC “Institute of Metrology” carries out under CООМЕТ and EMPIRE projects. The modeling was performed in the academic version of ANSYS.

Ship structures are subjected to corrosion inevitably in service. Existed image-based methods are influenced by the noises in images because they recognize corrosion by extracting features. In this paper, a novel method of image-based corrosion recognition for ship steel structures is proposed. The method utilizes convolutional neural networks (CNN) and will not be affected by noises in images. A CNN used to recognize corrosion was designed through fine-turning an existing CNN architecture and trained by datasets built using lots of images. Combining the trained CNN classifier with a sliding window technique, the corrosion zone in an image can be recognized.