Eric Schulken-Portfolio [11-8-16]

Eric Schulken -- Engineering Portfolio

Simulation, Drafting, GD&T, 3D Modeling, Technical Writing, User Design, Automation

Current as of Fall 2016

Table of Contents: Vehicle Path Planning Simulation…………………………………………………………………………….1 Pick and Place Gripper Design……………………………………………………………..………………….2 Automation Cell Layout………………………………………………………………………………...……….3 MRP Scheduling Tool ……………………………………………………………………………….…………..4 Image Analysis……………………………………………………………………………………………………….5 Writing Sample……………………………………………………………………………………………….……..6

Eric Schulken Engineering Portfolio

Figure 1: A three degree of freedom vehicle dynamics model was simulated in Matlab in order to test and evaluate path planning methods. The vehicle perceives its surroundings through a simulated two dimensional laser scan (red + markers in top plot) and attempts to avoid obstacles while traveling to a series of waypoints (red * markers in top plot). Resulting inputs and behavior are displayed in the bottom series of plots.

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Figure 2: Images and drawing of dual function gripper fingers designed in Solidworks. A prototype set was 3D printed and tested, successfully picking and placing lenses without damaging them.

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Figure 3: CAD mock-up of automation cell for lens production. Features included are corona treatment, spray coating, infrared drying, UV curing, laser cutting, laser etching, and packaging

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Figure 4: Screenshot of materials requirement planning tool built from scratch in Excel. Features include production schedule, due dates, projected inventory levels, inventory conflict alerts, and controls for easy manipulation.

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Figure5: Novel method for contaminant surface area coverage estimation using comparators of known values. Comparators were validated using an ASTM standard for phase constituent area measurement. Results showed the estimation method yielded similar accuracy and less variation between samples than the ASTM method.

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Objective 5: Data Processing Methods Context for Writing Sample: The purpose of this project was to explore methods for measuring an individual rower’s power output in a boat. For testing, accelerometers were placed on the handle and body of an erg (indoor rowing machine). This portion of the report details the methods developed to translate those acceleration measurements into rower power, using the erg’s display as a control. Introduction:

The data processing algorithm developed in Objective 3 was modified to account for the constraints of testing on the erg. Acceleration and velocity of the handle were used in place of the same values for the oar blade. Boat velocity was estimated by overlaying the reciprocating motion of the erg on the stroke average speeds displayed by the erg (Figure 1 ). Times for which force and power calculations were valid (start and end times for the drive) were determined both by the locations of extrema on a graph of handle position relative to the erg, and by visual inspection of of a graph of raw handle acceleration, with positive results found for both methods. A substantial amount of drift was observed in the acceleration measurements and had to be filtered out.

Figure 1: Flowchart showing how applied propulsive force and power are calculated from instantaneous acceleration measurements.

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Force Calculation: From Objective 3, we know that the instantaneous force during the drive can be

represented as (the subscript can be read as: of the handle relative to the erg):,H E

(6),∝ α F Oar + V B2

or in the case of the erg: (7)a VF = c1 H ,E + c2

2H ,E

Rather than calculate the constants and from the parameters of the system, they c1 c2 were solved for by minimizing the sum of the squares of the differences between calculated and observed stroke average power values. Drift Correction:

Without filtering, drift in the acceleration measurements meant that any force or power calculations were soon thrown off. A suitable filtering scheme was developed based on those described in [2]. The filtering took two steps. The first involved large scale correction by fitting a 4th degree polynomial to position data obtained by twice integrating raw acceleration data. Values of the derivative of the polynomial fit were subtracted from velocity data to generate a first velocity correction--C1 (Figure 2) . C1 velocity data were then integrated to generate C1 position data. The second step required that a cubic spline be fitted through the local maxima of the C1 position data, and the spline values subtracted from the C1 data to generate C2 position data, which were then differentiated once to generate C2 velocity data and again to generate C2 acceleration data. The spline correction relies on the assumption that the handle changes direction at the same location each stroke. Though it is possible to correct the drift without this assumption, the process requires wavelet analysis, which is outside of the immediate scope of this project. The polynomial fits were evaluated in Matlab, while the spline fits (Figure 3 ) were evaluated using a custom Excel function [1].

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Figure 5: Flowchart showing full drift correction algorithm.

Stroke Average Values:

Time for the test run was divided into chunks representative of a single stroke. Transition from one stroke to the next was always placed in the recovery phase of the stroke. Stroke average power was calculated by averaging all non-zero instantaneous power values for a given stroke (force--and therefore power--values outside of the window defined by the catch and finish times were set to zero). These power values were then used with those recorded from the erg’s display for the least sum of squares calculation of the constants for equation (7).

References: [1] “Cubic Splines.” Newton Excel Bach , 2009. Accessed December 12, 2015: https://newtonexcelbach.wordpress.com/2009/07/02/cubic-splines/ [2] MIllor, Nora, et.al ., “Drift-Free Position Estimation for Periodic Movements using Inertial Units.” IEEE Journal of Biomedical and Health Informatics , 18(4) 2014. doi: 10.1109/JBHI.2013.2286697

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https://newtonexcelbach.wordpress.com/2009/07/02/cubic-splines/

https://www.researchgate.net/journal/2168-2194_IEEE_Journal_of_Biomedical_and_Health_Informatics