Within any manufacturing environment, the selection of the production or assembly machines is part of the day to day responsibilities of management. This is especially true when there are multiple types of machines that can be used to perform each assembly or manufacturing process. As a result, it is critical to find the optimal way to select machines when there are multiple related assembly machines available. The objective of this research is to develop and present a model that can provide guidance to management when making machine selection decisions of parallel, non-identical, related electronics assembly machines. A model driven Decision Support System (DSS) is used to solve the problem with the emphasis in optimizing available resources, minimizing production disruption, thus minimizing cost. The variables that affect electronics product costs are considered in detail. The first part of the Decision Support System was developed using Microsoft Excel as an interactive tool. The second part was developed through mathematical modeling with AMPL9 mathematical programming language and the solver CPLEX90 as the optimization tools. The mathematical model minimizes total cost of all products using a similar logic as the shortest processing time (SPT) scheduling rule. This model balances machine workload up to an allowed imbalance factor. The model also considers the impact on the product cost when expediting production. Different scenarios were studied during the sensitivity analysis, including varying the amount of assembled products, the quantity of machines at each assembly process, the imbalance factor, and the coefficient of variation (CV) of the assembly processes. The results show that the higher the CV, the total cost of all products assembled increased due to the complexity of balancing machine workload for a large number of products. Also, when the number of machines increased, given a constant number of products, the total cost of all products assembled increased because it is more difficult to keep the machines balanced. Similar results were obtained when a tighter imbalance factor was used.

Thesis (M.Eng.(Mechanical Engineering))--University of Pretoria, 2004. / Summaries in Afrikaans and English. Includes bibliographical references (leaves 80-82).

Thesis (Ph. D.)--George Washington University, 1988. / Includes bibliographical references (leaves 280-295).

Double rotor machines, appearing in versatile forms and configurations thanks to the great flexibility of having a pair of rotors, are seen in a number of applications. Double rotor machines show promising prospect in the application of advanced hybrid electric vehicle powertrains due to the requirement of dual electro-mechanical ports in such systems. Integrating these powertrain systems with double rotor machines not only brings design freedom of laying out components, but also reduces number of parts and thus improves compactness. The switched reluctance type double rotor machines, offering unique characteristics of having a simple structure and no permanent magnets, are strong candidates for high performance applications. In this thesis, a family of double rotor switched reluctance machine with segmented rotors is proposed and studied. Compared to double rotor switched reluctance machines with a more conventional structure, the proposed designs exhibit potentials of achieving higher compactness and performance. A prototype double rotor machine of the segmented rotor design is constructed and tested to benchmark an existing double rotor switched reluctance machine. The experiment results show that the proposed design is able to achieve the same output with similar or higher efficiency than the benchmark machine, while occupying only about 60% of overall volume. The double segmented rotor switched reluctance machine demonstrates to be a promising double rotor topology and is worth further research. / Thesis / Master of Applied Science (MASc)

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Our motivation for this work is to develop an autonomous robot system that is able to perform autism intervention therapy. Autism spectrum disorder (ASD) is a common type of neurodevelopmental disorder that affects millions of people in the United States alone. The best way of treating ASD and help people with ASD learn new skills is through applied behavior analysis (ABA, i.e. autism intervention therapy). Because of the fact that people with ASD feel less stressful in a predictable and simple environment compared to interacting with other people and autism intervention therapy provided by professional therapists are generally expensive and inaccessible, it would be beneficial to build robots that can perform intervention therapy with children without a therapist/instructor present. In this research, we focus on the task of detecting engagement/disengagement levels of a child in a therapy session as a first step in designing a therapy robot. In this work, we mainly utilize an RGB-D camera, namely the Microsoft Kinect 2.0, to extract kinematic joint data from the therapy session. We also set up a child study with the Kid’s Creek therapy center to recruit children with ASD and record their interactions with a therapist while working on a touch-screen based game on a tablet. After carefully selecting features derived from skeletons’ movements and poses, we showed that our system can produce an accuracy of 97% when detecting engagements and disengagements using cross-validation assessment.

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