Integrating a Smartphone-Based Vibration Experiment into an Engineering Course

A smartphone coupled with a low-cost physical system can be used to conduct a meaningful at-home engineering experiment that provides an environment for experiential and personalized learning. The objective of this study is to improve students\u27 understanding of the response of a dynamic system through integrating an at-home experiment into a lecture-only class using a smartphone as the measurement system. The paper reports on the use of the linear acceleration sensor in smartphones to conduct an at-home experiment to measure the vibration characteristics of a cantilever beam in a junior-level, systems dynamic course. All students in the class were provided with a spring steel beam and a C-shaped clamp. The students mounted their own phone at the end of the beam, and an app was used to record the acceleration of the beam for three different beam lengths. From the experimental data, the students were asked to determine the damped natural frequency of the beam and compare it to theory. The study was performed over three years with a total of 302 students. Data analysis of the short pre and post quiz conducted with the experiment showed that the at-home experiment had a positive effect on students\u27 understanding of key concepts. Furthermore, written and verbal comments from the students showed that the students valued the learning they got from performing this experiment

A study of learning styles and team performance

This paper reports on a study that was performed over a 4-year long period in which the performance of undergraduate mechanical engineering students on a team project, enrolled in a senior mechanical systems course at the University of Rhode Island, was correlated with their learning styles as measured by the Brain Dominance Model. To measure the learning style of each student, the Brain Works program, developed by Synergistic Learning Incorporated, was used in this study due to its ease of administration and explanation of results. The students were asked to report to the instructor the two numbers that the program generated: one is a left/right brain measure and the other, an auditory/visual measure. In the first two years of this study, the 4-5 members of each team were grouped based on their learning styles score with the objective of forming teams with members whose scores are in three or more different quadrants of the left/visual plane. In the last two years, the teams were formed randomly, but the students were asked to report their learning styles scores. Data was also collected on the performance of each student in the course and in the team project. To determine if the learning styles have any correlation to the performance of the team, a correlation analysis was performed on combination of many variables some of which are exam grade, project grade, and composite learning score for the team. The results show that the competence level of the team as measured by the exam grade has the most influence on the team performance, while the learning style makeup of the team has a less pronounced effect

Semi-automated system for assembly of insulated corner faux bricks

Insulated faux brick (IFB) is an improvement on traditional faux brick that aims to further reduce weight and installation time while providing insulation characteristics beyond traditional brick. Current production of insulated faux corner bricks relies on human workers to apply adhesive and press together two end pieces until the adhesive has dried. This is a time-consuming step which is not easily scalable to higher production rates. This research explored potential automated solutions to meet the ever-increasing demand. Specifically, the concept discussed in this paper uses a modular array of joining units that can be repeated to achieve the desired scale of production. This concept was verified through the construction of a beta prototype containing two joining units and a low-cost glue dispensing system to service both units. The prototype was operated continuously with an average throughput greater than two bricks per minute and with similar quality to the manual process. Based on a critical path analysis, a production system with eight joining units based on the same modular architecture has the potential for a threefold increase in production rate per worker

Input shaping using finite impulse response filters

The use of input shapers in control system design helps to decrease the overshoot and the settling time of underdamped dynamic systems. This paper discusses the use of Finite Impulse Response (FIR) Filters as input shapers for under-damped systems. Three specific FER filters were studied. These include: the boxcar, the triangle, and the half sinusoid. The three filter responses were compared to a normal step response, as well as a step response with a low-pass filter as a pre-filter. The comparisons were performed on a 2nd order system simulated in Matlab/Simulink simulation software package as well as on a piezo-actuated fast responding stage. The data shows that the FIR filter can significantly improve the response of the system both in simulation and experimentally. The triangular FER filter was particularly effective in the actual hardware implementation. © 2006 IEEE

Using the Soar Cognitive Architecture to Remove Screws from Different Laptop Models

This paper investigates an approach that uses the cognitive architecture Soar to improve the performance of an automated robotic system, which uses a combination of vision and force sensing to remove screws from laptop cases. Soar\u27s long-term memory module, semantic memory, was used to remember pieces of information regarding laptop models and screw holes. The system was trained with multiple laptop models and the method in which Soar was used to facilitate the removal of screws was varied to determine the best performance of the system. In all the cases, Soar could determine the correct laptop model and in what orientation it was placed in the system. Soar was also used to remember what circle locations that were explored contained screws and what circles did not. Remembering the locations of the holes decreased a trial time by over 60%. The system performed the best when the number of training trials used to explore circle locations was limited, as this decreased the total trial time by over 10% for most of the laptop models and orientations. Note to Practitioners - Although the amount of discarded electronic waste in the world is rapidly increasing, efficient methods that can handle this in an automated non-destructive fashion have not been developed. Screws are a common fastener used on electronic products, such as laptops, and must be removed during nondestructive methods. In this paper, we focus on using the cognitive architecture Soar to facilitate the disassembly sequence of removing these screws from the back of laptops. Soar is able to differentiate between different models of laptops and store the locations of screws for these models leading to an improvement of the disassembly time when the same laptop model is used. Currently, this paper only uses one of Soar\u27s long-term memory modules (semantic memory) and a screwdriver tool. However, this paper can be extended to use multiple tools by using different features available in Soar such as other long-term memory modules and substates

Control system experiments at home

Most mechanical engineering curricula include courses in system dynamics, controls, mechatronics, and vibrations, but these courses often do not have a laboratory component. Even if there is such a component, laboratory access is often limited, and thus there is a need to increase students\u27 laboratory experience. This paper addresses the development and testing of instructional material in the form of take-home hardware kits and software that can be used to perform control system experiments at home. The students are given a compact, low-cost hardware kit and software with which they can perform an experiment at home using only their PC/laptop. The kit consists of three components. These are: a hardware interface board which interfaces with the student\u27s PC and with the experiment hardware, a Windows-based user interface program that the students download to their computer, and the experimental setup. Five experiments have been developed. Here we report on two of these experiments that involve control systems: a DC motor/tachometer system and a heater/temperature sensor system. Administration of the kits in several mechanical engineering courses has shown that the kits were effective in improving student understanding of key concepts. © 2011 IEEE

Control systems take-home experiments [focus on education]

Most electrical and mechanical engineering curricula include courses in controls and mechatronics, with mechanical engineering curricula also including system dynamics and vibrations courses. The laboratory component for these courses is often limited and involves using a limited number of experimental setups. At many institutions, the laboratories associated with these courses are not taken in the same semester, preventing students from practicing the concepts learned in the lecture in a timely manner. Even in laboratory courses that are offered in the same semester as the lecture courses, in many cases it is not possible to synchronize the concepts covered in the lecture with the laboratory exercises since there are usually only a few lab setups for each experiment. © 2012 IEEE

System dynamics experimentation at home

Most Mechanical Engineering curricula include courses in system dynamics, controls, mechatronics, and vibrations. At most schools, these courses do not have a laboratory component. Even at schools that have such a component, laboratory access is often limited, and thus there is a need to increase students\u27 laboratory experience. This paper addresses the development and initial testing of instructional material in the form of take-home software and hardware kits that can be used to perform laboratory experiments and measurements at home to illustrate system dynamics concepts. Rather than having students perform an experiment in the university laboratory, the students are given a compact, low cost software and hardware kit with which they can perform an experiment at home using only their PC. The kits are designed so that the experiments can be conducted on a provided experimental setup such as a DC motor/tachometer system or can be used to perform dynamic measurements on engineering systems that are available at home such as motor powered devices and heating/cooling systems. The take-home kit consists of three components. The first component is a hardware interface board that is built around a PIC18F4550 microcontroller which interfaces with the student\u27s PC and with the experiment hardware. The second component is a Windows based user interface program that is loaded on the student\u27s PC and is used to run the experiment and collect data. The third component is the actual experimental setup or the sensor system to perform the measurement. Fifty five kits have been fabricated to perform five different experiments. Two of these experiments were tested in two courses in the mechanical engineering department at the University of Rhode Island. The paper discusses the design of the kit components, the details of the experiments, as well the initial experiences gained from using this new approach for laboratory experimentation. Copyright © 2010 by ASME

Characterization of Different Microsoft Kinect Sensor Models

This experimental study investigates the performance of three different models of the Microsoft Kinect sensor using the OpenNI driver from Primesense. The accuracy, repeatability, and resolution of the different Kinect models\u27 abilities to determine the distance to a planar target was explored. An ANOVA analysis was performed to determine if the model of the Kinect, the operating temperature, or their interaction were significant factors in the Kinect\u27s ability to determine the distance to the target. Different sized gauge blocks were also used to test how well a Kinect could reconstruct precise objects. Machinist blocks were used to examine how well the Kinect could reconstruct objects setup on an angle and determine the location of the center of a hole. All the Kinect models were able to determine the location of a target with a low standard deviation (\u3c2 mm). At close distances, the resolutions of all the Kinect models were 1 mm. Through the ANOVA analysis, the best performing Kinect at close distances was the Kinect model 1414, and at farther distances was the Kinect model 1473. The internal temperature of the Kinect sensor had an effect on the distance reported by the sensor. Using different correction factors, the Kinect was able to determine the volume of a gauge block and the angles machinist blocks were setup at, with under a 10% error

A System Combining Force and Vision Sensing for Automated Screw Removal on Laptops

This brief investigates the performance of an automated robotic system, which uses a combination of vision and force sensing to remove screws from the back of laptops. This robotic system uses two webcams, one that is fixed over the robot and the other mounted on the robot, as well as a sensor-equipped (SE) screwdriver. Experimental studies were conducted to test the performance of the SE screwdriver and vision system. The parameters that were varied included the internal brightness settings on the webcams, the method in which the workspace was illuminated, and color of the laptop case. A localized light source and higher brightness setting as the laptop\u27s case became darker produced the best results. In this brief, the SE screwdriver was able to successfully remove 96.5% of the screws.Note to Practitioners-The amount of discarded electronic waste (e-waste) is increasing rapidly, yet efficient, nondestructive, automated methods to handle the waste have not been developed. Many e-waste products such as laptops use fasteners that need to be removed. In this brief, we focus on removing screws from laptops in a nondestructive manner in order to not damage the laptop, so its parts can be recycled. Due to the vast amounts of laptop models, it is necessary to create a method that will automatically recognize the locations of these fasteners. This brief presents a prototype robotic system that integrates force and vision sensing to automatically locate and remove screws from various models of laptops. The methodology presented in this brief is applicable to other e-waste products with a casing attached by screws. A current limitation of this brief is the robotic system that has to investigate all potential hole locations found by the vision system, although some of these locations may not correspond to valid screw locations. This brief can be extended to include a memory feature that will remember the locations of the screws for cases with similar laptops that are handled by the system to improve the processing time