This paper presents a comprehensive, control-oriented nonlinear physics-based model of a motor-driven aircraft arrestment system. The arrestment system is represented as a set of tightly coupled mechanical, electrical, and geometric subsystems, including the aircraft, arresting cables, shock absorbers, spools, shafts, brakes, and electric motors, each described by explicit dynamic equations. The model explicitly captures dominant arrestment nonlinearities, including asymmetric cable forces, time-varying spool radius, inertia variation due to cable payout, nonlinear spring–damper behavior in the shock absorbers, frictional effects, actuator dynamics, and constraints arising from limited sensor availability. A physically motivated aircraft-on-deck dynamic model is formulated to describe coupled longitudinal and lateral motion during arrestment in the absence of direct aircraft state measurements, enabling force and energy transmission to be modeled consistently. The resulting nonlinear plant is systematically linearized about multiple operating points distributed along the arrestment trajectory, yielding a family of linear time-varying models suitable for analysis and synthesis. A subscale experimental arrestment system is used to validate the modeling framework. Comparisons between simulated and experimental responses show that the model reproduces the dominant arrestment dynamics, including cable tension evolution, torque transmission, and vehicle deceleration, establishing a reliable modeling foundation for subsequent control-oriented studies.

Automating the extraction of mechanical fasteners from end-of-life (EOL) electronic waste is challenging due to unpredictable conditions and unknown fastener locations relative to robotic coordinates. This study develops a system for extracting cross-recessed screws using a Deep Convolutional Neural Network (DCNN) for screw detection, integrated with industrial robot simulation software. The simulation models the tooling, camera, environment, and robot kinematics, enabling real-time control and feedback between the robot and the simulation environment. The system, tested on a robotic platform with custom tooling, including force and torque sensors, aimed to optimize fastener removal. Key performance indicators included the speed and success rate of screw extraction, with success rates ranging from 78 to 89% on the first pass and 100% on the second. The system uses a state-based program design for fastener extraction, with real-time control via a web-socket interface. Despite its potential, the system faces limitations, such as longer cycle times, with single fastener extraction taking over 30 s. These challenges can be mitigated by refining the tooling, DCNN model, and control logic for improved efficiency.

This paper investigates varying the operating conditions of a neural network in a robotic system using a low-cost webcam to achieve optimal settings in order to detect crossed-recess screws on laptops, a necessary step in the realization of automated disassembly systems. A study was performed that varied the lighting conditions, velocity, and number of passes the robot made over the laptop, as well as the network size of a YOLO-v5 neural network. The analysis reveals that specific combinations of operating parameters and neural network configurations can significantly improve detection accuracy. Specifically, the best results for the majority of laptops were obtained when the system ran at medium velocity (10 and 15 mm/s), with a light, and the neural network was run with an extra large network. Additionally, the results show that screw characteristics like the screw hole depth, the presence of a taper in the screw hole, screw hole location, and the color difference between the laptop cover and the screw color impact the system’s overall detection rate, with the most important factor being the depth of the screw.

This paper presents a model-based robust controller and a subscale experimental demonstration for a subscale motor driven aircraft carrier arresting system. Sensors in the system are limited with no sensors on the aircraft being controlled. The uncertainty in the aircraft’s orientation and position, the long mechanical linkages, along with the nonlinear dynamics and limited sensors, result in control design challenges. The control method is based on an adaptive linear quadratic Gaussian/loop transfer recovery control algorithm that regulates >10 MJ of pulsed energy with MW power transients. The design method generates a controller with good tracking performance for the aircraft position and velocity and successfully arrests the aircraft within cable tension bounds.

Many applications in manufacturing, physical therapy, and machining require linear motion stages to move back and forth at specified rates without exceeding the capabilities of the stages. Growing interest has also been in using integrated servo motors for these applications, which would benefit from the integrated nature of the motor, feedback device, and drive electronics in a single package. This paper introduces a methodology for trajectory planning for the reciprocating motion for a linear motion device. The methodology can be implemented on any device, and a simplified version can be applied to the control software for integrated servo motors. The approach develops motion trajectories, such as triangular or trapezoidal, using a dynamic model of the device and the motor’s torque–speed profile characteristics. The simplified version of the methodology was implemented using an integrated servo motor driving a ball screw stage. Tests were conducted at various motion rates and loads. The experimental results show high accuracy between the predicted and measured motion rates, particularly for rates below three cycles per second. These results suggest that the developed methodology can be a valuable tool for predicting the performance of systems that use integrated servo motors for reciprocating motion applications.

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system’s various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems.

The goal of this paper is to present a method to design a digital controller for a motor driven aircraft subscale arrestment system of the type used on aircraft carriers. Sensors in the system are limited and there are no sensors on the aircraft being controlled. The uncertainty in the aircraft's orientation and position during arrestment along with the nonlinear dynamics and limited sensors result in control challenges. The focus of this paper is on the arrest phase controller that decelerates the plane along a reference trajectory to bring it to a final stop without steering it astray. It is based on an adaptive linear quadratic Gaussian/loop transfer recovery (LQG/LTR) control algorithm. The controller development uses a multi-input multi-output (MIMO) design technique. This is important for high performance since the system behaviors are coupled and simplifying techniques that ignore this coupling do not have the flexibility to achieve optimal performance. The algorithm developed here generates a good tracking performance for the aircraft position and velocity and successfully arrests the aircraft within bounds. The simulation and experimental results using a subscale arrestment system have shown the effectiveness of the proposed algorithm.

Dynamic modeling of complicated mechanical systems using physical component models created through MATLAB® Simscape™ Multibody™ software has been gaining increasing attention. This paper considers the application of this software to accurately model a belt driven H-frame type XY positioning table. Before this could be accomplished, a method for modeling belt compliance had to be developed, as this capability (at the time of the investigation) was not innately built into the software. The method that was developed involves breaking down system-level belt compliance into components and lumping the resultants as “springs” within the mechanical model for each degree of freedom of the end effector. This approach was used to add compliance to the Multibody model, at which point it was simulated against an Sth-Order State-Space model of the system that was developed and verified in a previous work. The results show that the belt compliance modeling method for the Multibody model presented here is an effective way to do this and provides an option for more accurate physical modeling of compliant members in Multibody which is a viable approach to model complicated physical systems.

Fastener detection is a necessary step for computer vision (CV) based robotic disassembly and servicing applications. Deep learning (DL) provides a robust approach for creating CV models capable of generalizing to diverse visual environments. Such DL CV systems rely on tuning input resolution and mini-batch size parameters to fit the needs of the detection application. This paper provides a method for determining the optimal compromise between input resolution and mini-batch size to determine the highest performance for cross-recessed screw (CRS) detection while utilizing maximum graphics processing unit resources. The Tiny-You Only Look Once v2 (Tiny-YOLO v2) DL object detection system was chosen to evaluate this method. Tiny-YOLO v2 was employed to solve the specialized task of detecting CRS which are highly common in electronic devices. The method used in this paper for CRS detection is meant to lay the ground-work for multi-class fastener detection, as the method is not dependent on the type or number of object classes. An original dataset of 900 images of 12.3 MPx resolution was manually collected and annotated for training. Three additional distinct datasets of 90 images each were manually collected and annotated for testing. It was found an input resolution of 1664 x 1664 pixels paired with a mini-batch size of 16 yielded the highest average precision (AP) among the seven models tested for all three testing datasets. This model scored an AP of 92.60% on the first testing dataset, 99.20% on the second testing dataset, and 98.39% on the third testing dataset.

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.

Electric vehicles (EV) offer an environment friendly solution to transportation and there are predictions for high sales of EVs in future. The most expensive parts of those vehicles are their batteries which need to be recycled after use. Currently, there are major challenges in disassembling and recycling EV batteries due to the large variety of types, sizes and design complexity. This paper provides a brief summary on current studies for the disassembly of EV batteries as well as the assessment of automation potential for EV battery disassembly steps. A 2017 Chevrolet Bolt battery was used to generate a disassembly graph, which shows connections and constraints of all parts and fasteners, and a 46-step disassembly sequence. An automation assessment of the 2017 Chevrolet Bolt battery and Audi Q5 battery was conducted on all the steps to determine, based on two categories, the technical possibility and the necessity of automating a given disassembly step. To score these different steps, which could range from a -100 to 100, an easy-to-use criteria catalog was developed and applied on these batteries. This criteria catalog consisted of a total of ten criteria, five criteria for the technical possibility to automate the step and five criteria for the necessity to automate that step. Disassembly steps that score above 50 for the technical ability to automate and had a positive necessity of automating score are steps that should be automated. The scores generated for both batteries showed that most of the unscrewing operations should be automated while most of the lifting operations should be performed by human workers. The results from the automation assessment of the Audi Q5 battery compared similarly to approaches found in literature but was able to produce more extreme scorings because of the simplified criteria catalog. The work presented in this paper gives an approach to assess the automation potential of a given disassembly step in any EV battery.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Assistive Technology Devices: A Multidisciplinary Course Musa Jouaneh, Ying Sun, Robert Comerford University of Rhode Island Kingston, RI 02881 Abstract This paper reports on a team-based, project-oriented engineering and business course at the University of Rhode Island. The class is a two-semester course sequence that emphasizes invention, innovation and entrepreneurship with focus on the marketing, design, and development of assistive technology devices. Faculty from the Colleges of Engineering and Business Administration guide multi-disciplinary teams through innovative product-oriented design projects in the field of assistive technology. Teams are made up of junior and senior level biomedical engineering, electrical engineering, mechanical engineering and business majors. In the first offering of this course in 2002/2003, a total of 21 students were split into 4 teams. This year, 32 students are split into 5 teams. Teams build prototypes of their designs, prepare business plans, prepare patent applications, and seek commercialization opportunities. The paper discusses course conduct, student assessment, and experiences learned form teaching an interdisciplinary course. This course provides new experiences and capabilities for students that enhance their opportunities of pursuing product design and entrepreneurial activities in their careers. Background The assistive technology (AT) industry is characterized by a diversity of AT devices that have limited market potential. Relatively few of them, unlike wheelchairs and hearing aids, have sustainable markets to support mass production and standard business models for consumer products1,2. For a majority of AT needs, a question often arises about the technological and financial feasibility of proposed devices. Engineers who develop AT devices should consider both cost-effectiveness, by using off-the-shelf components, and maximizing market potential, by utilizing universal design. A new AT device that does not have sufficient market potential to launch a small business on its own may have to be transferred by licensing, sale or joint venturing to an existing firm that possesses the appropriate manufacturing capability and asset base. Thus, assistive technology presents a unique set of marketing and financial challenges for engineers and business people, not to mention the technical challenges, that demand tight collaboration among them to deliver successful products with short times-to-market. “Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright 2004, American Society for Engineering Education”

Before coming to Kenesaw State University, Dr. McFall lived abroad for more than ten years. His international experiences began with a study abroad for his entire undergraduate senior year at the Luleå University of Technology in Sweden 50 miles south of the Arctic Circle. After graduating with his B.S. in Mechanical Engineering from Virginia Tech, his international travels continued during masters studies at MIT with an appointment at the Japan Atomic Energy Research Institute in Japan. His work there involved heat transfer in the superconducting magnet systems for the International Thermonuclear Experimental Reactor project.

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' 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' 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.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract A Study of Learning Styles and Team Performance Musa K. Jouaneh1 Department of Mechanical Engineering & Applied Mechanics University of Rhode Island Kingston, RI 02881 Abstract 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. Introduction Methods of forming student teams in project-oriented courses are an area of active research and a topic that generates a lot of discussions among faculty members. Many faculty members at different institutions [1, 2] have reported that team functionality improves if team members were selected based on their learning style. There are many techniques available to classify learning styles such as Kolb's Learning Style Inventory [3-4], and the Myers-Briggs inventory [5]. 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, was correlated with their learning styles as measured by the Brain Dominance Model. To measure the learning style of each student, the Brain Works (Braintest) program [6], developed by Synergistic Learning Incorporated, was used 1 Current Address: Dept. of Mechanical Engineering, The Petroleum Institute, Abu Dhabi, United Arab Emirates. “Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright  2005, American Society for Engineering Education”

A robotic system was designed to both secure and remove the cap from a plastic bottle using force sensing resistors to register the completion of the tasks. The system was based around a UR5 robotic arm for which a gripping device was designed and manufactured. Part of the gripping system consisted of a dynamic force sensing component. The two force sensors were positioned to sense tension and torsion between the gripper and robot in order to detect when the cap was completely removed or secured. Electronics were used to control the gripper and to receive information from the sensors. A program was developed to control the position of the robotic arm, open and close the gripper, receive sensor readings, and use the readings to initiate commands for the robot and gripper. The system was designed, built, tested, and redesigned over the course of a semester. The final product successfully secured and removed the cap from the bottle and stopped when the cap was completely secured or removed.

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's 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's 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.

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's case became darker produced the best results. In this brief, the SE screwdriver was able to successfully remove 96.5% of the screws.

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' 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's 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 (<;2 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.

This paper discusses the design and characterization of a prototype disassembly tool that was designed to handle a family of electronic devices whose plastic, cantilever snap-fit covers house AA or AAA batteries. The tool was designed with the ability to release the snap-fit covers and the batteries contained inside. The tool design is based on the use of a force sensing tool tip that utilizes three force sensing resistors (FSRs) for force feedback. Two FSRs were used to measure horizontal forces applied to the tool tip while the third FSR was used to measure forces along a direction normal to the tool tip. The tool tip is used to push and lift up the snap-fit cover as well as the spring-loaded batteries. By using the conductance of the FSR sensors, a linear model of the FSR output was calibrated to the force applied to the FSR. The disassembly tool was mounted on a three-axis translational motion robot, and the robot was programmed to perform disassembly operations. Sensor feedback from the FSRs was used to control the movement of the tool during these operations. The results showed that the robot was able to successfully use the disassembly tool to perform the necessary operations to remove the device's snap-fit cover and batteries. Force readings recorded from the FSRs indicated that the disassembly tool was able to react to force interactions at the disassembly tool tip such as a missing part or misaligned part. The use of FSRs resulted in a low-cost, flexible disassembly tool.