Browsing by Author "Knopp, Zachary"

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    Design, fabrication and control of a robotic manipulator for picking and placing plastic parts
    (University of New Brunswick, 2014) Albohaire, Fahad; Wilson, Jacob; Knopp, Zachary; Dubay, Rickey; Dr. Dubay
    This report contains the details of the design and construction of a robotic manipulator for the intelligent controls laboratory at UNB. The task this robotic manipulator needs to perform is to pick up parts that are ejected from a plastic injection molding machine and place them on a table for thermal imaging. A minimum fifteen second part cycle time and a workspace of approximately one meter radius were required. The scope of this project included design, construction and control of the manipulator. A two degree of freedom (DOF) robotic manipulator with a pneumatic gripper was designed and constructed in order to execute this task. The mechanical design included bearing sizing, motor sizing, gripper design, pneumatic design and valves sourcing. The electrical design included motor driver circuit design, voltage regulator circuit design, isolation design and spatial design for an industrial electrical box. Software development was used to control the manipulator and encompassed hardware/software interfacing, path trajectory design, and controller design. The software was designed in National Instruments Lab Windows CVI and control was done using a PCIe-6321 DAQ board. The position of the end effector is controlled by pulse width modulation signals sent to motors with encoder position feedback using a PID controller. A homing initialization process is used when the arm is powered since relative, not absolute, quadrature encoders were used. The pneumatic systems were controlled using solid state relays and digital signals from the DAQ board.
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    Towards image-based control of an industrial potato peeler
    (University of New Brunswick, 2016) Knopp, Zachary; Dubay, Rickey; Shukla, Dhirendra
    Difficult multivariate industrial control problems can be solved by combining standard control theory, adaptive computer vision algorithms and intelligent modeling into an overarching generalized control system. Creating the foundations for such a system, to be implemented on an industrial potato peeler, was the scope of this thesis. Computer vision algorithms that provided quantifiable metrics from the peeling process were developed using data gathered at a potato research center. Experiments were performed controlling the steamtime and pressure of the peeler and the size and seasonality of the potatoes. Thermal signatures and optical videos of peeled potatoes were recorded throughout testing. It was found that smaller potatoes are more difficult to peel and changes in pressure do not correlate with changes in peel efficiency. Recommendations were made for the next steps towards intelligent control of industrial potato peeling processes.