National Educators' Workshop: Update 1989 Standard Experiments in Engineering Materials Science and Technology

Presented here is a collection of experiments presented and demonstrated at the National Educators' Workshop: Update 89, held October 17 to 19, 1989 at the National Aeronautics and Space Administration, Hampton, Virginia. The experiments related to the nature and properties of engineering materials and provided information to assist in teaching about materials in the education community

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

National Educators' Workshop. Update 92: Standard Experiments in Engineering Materials Science and Technology

This document contains a collection of experiments presented and demonstrated at the workshop. The experiments related to the nature and properties of engineering materials and provided information to assist in teaching about materials in the education community

Optimization of Extrusion Blow Molding Processes using Soft Computing and Taguchi's Method

The objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural\u2013Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchis experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.Peer reviewed: NoNRC publication: Ye