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ECOR 1010 (104)
Lecture 12

ECOR 1010 Lecture 12: (ALL LECTURES) ECOR 1010 Lecture Notes TSE & REG

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Engineering Common Core Courses
ECOR 1010
Glenn Mc Rae

ECOR 1010 Lecture Notes: Lecture 3: Engineering Reporting & Measurements, Dimensions and Units General Rules: • Date and Sign Everything Written • Be clear and Concise • Avoid Jargon and Fancy Words • Plan Context- Outline- Several Drafts • Spell Checkers Are Only and Aid • Do Not leaving writing to the last minute • PROOF READ!!! Good Engineering Report: • Clear Statement of purpose • Concise presentation of detail • Logical development of data and ideas  Analyze, explain, conclude, recommend • Objective separation of fact and opinion • Organized layout  Well-spaced heading and visuals • Has adequate references  NO plagiarism or forgotten references • Has no spelling mistakes or grammatical errors  NO slang or jargon READ CHAPTER 10 Lecture 4: Dimensions, Units, & Significant Digits Precision and Accuracy: • Precision of a Measurement  Precise measurements have small random error  Discrepancies between repeated measurements taken under the same conditions are small • Accuracy  Accurate measurements are close to true value  Some authors use term to describe total error  Others to describe systematic error  Accurate measurements have little bias Rounding Numbers: • When the leftmost discarded digit is 5 and all following digits are 0, then rightmost retained digit is unchanged if it is even; otherwise it is increased by one. (Odd the number increases even the number stays the same) READ CHAPTER 11 Lecture 5: Engineering Graphics-1 • Universal language between engineers • Be clear and unambiguous (must be precise and exact) Oblique Projection: • Front face of object is parallel to the viewer, therefore that face is true size • Projection lines do not converge to a vanishing point • Presents the exact shape of one face • Lack of perspective foreshortening makes it easier to compare sizes Isometric Projection • Parallel lines reoain parallel instead of converging to a vanishing point • Axis are each 120 apart • Special case of Axonometric projection (Viewed so as to reveal more than one side) • Shows more than one face of the object Orthographic Projection: • Most important graphical drawing method • 2D representations of a 3D object • Extensively used in Engineering • Allow parts to be made • Often an isometric view is included Third-Angle Orthographic Projection: • Object placed in the third quadrant  Then a front view, right view, and top view is drawn of the object • The most descriptive view is usually selected to be the front view  The remaining views should contain the fewest number of hidden lines READ CHAPTERS 12 & 14 Lecture 6: Engineering Graphics-2 Extension and Centre Lines: • Leave a gap between the end of an extension line and the object line • The short dashed of crossing perpendicular center lines form a small cross Precedence of Lines: • Visible object lines  Takes precedence over all other lines • Hidden lines & Cutting plane lines  Take precedence over center lines • Centre lines  Does not have precedence • Extension & leader lines Dimensioning Rules: • Dimension from visible lines, not hidden lines • Repeated regular identical features may be dimensioned once along with a small note indicating quantity, etc. • Designer should locate dimensions in a way to indicate how tolerances accumulate • Radii are dimensioned using leader lines with an arrow at the end of the line “R” indicates radius  Dimension rounds and fillets with their radii • Diameter has a leader line with an arrow  The symbol ø indicates diameter  Never dimension holes or cylinders with radii, always use diameter • Angles- Fractional degree measurements can be represented with decimals or minutes and seconds (drawn as arcs) Rule 1: • The first dimensions should be three times the letter height from the object • Successive dimensions should be 2x the letter height apart Rule 2: • Place dimensions between the views sharing the same dimensions Rule 3: • Dimension the most descriptive view Rule 4: • Dimension from visible not hidden lines Rule 5: • Give and overall dimension, omitting the last in a chain of dimensions Rule 6: • Organize the dimensions to reduce clutter in the drawing Rule 7: • To avoid errors and confusion, do not dimension twice (same dimension twice) Rule 8: • Dimension lines never cross any other lines unless absolutely necessary Rule 9: • Extension lines may cross other extension lines, or object lines, if necessary • Leave a small gap from the object to the line that extend from them • Do not leave gaps where extension lines cross object lines or other extension lines Rule 10: • Where possible place dimensions outside object Rule 11: • Dimension the diameter of the cylinders in rectangular view (Radii cannot be measured) Rule 12: • Dimension circular holes in the view where they look like holes READ CHAPTER 13 Lecture 7: Engineering Graphics- 3 Lecture is about types of engineering graphics, how CAD programs help use and tips for hand sketching Lecture 8: Introduction to Design Method of Design: 1. Recognition of need a. Very obvious or hidden b. Very vague, causes open ended design, often written in a SoR (Statement of Requirements 2. Definition of the design problem a. Critical for good design b. Researching the topic c. Must define the problem correctly in order to find solution 3. Definition of the design criteria a. Design criteria (standards to be met) b. Design constraints (limitations on designer) 4. The design loop a. Synthesis i. Suggesting ideas or methods to solve the problem b. Analysis i. Calculating the expected results of each idea or method c. Decision-making i. Deciding which alternative is best 5. Optimization a. Compromising between cost and benefits (best design reasonable cost) b. If the design is not optimum, restart at step 3 6. Evaluation a. Reviewed when finished, senior engineer must approve b. If flaws are found start over at step 2 7. Communication a. Drawings, reports All designs must meet code and standards Creativity and Innovation: • There’s no such thing as a bad idea, someone’s out of world idea could lead to other topic that could result in your design • Always use brainstorming READ CHAPERS 16 & 17 Lecture 9: Part 1- 3D Computer-based Rendering with Creo Parametric Solid Modeling: • Representation of the part or product as it will appear when manufactured • Collection of elements that act and an object Protrusion- A profile used to add material Cut- a profile used to remove material from and existing body Rounds & Fillets- added to strengthen the part Holes- created using a number of different options READ CHAPTER 16 Lecture 9: Part 2- Rapid Prototyping with 3D Printing Basic Process of Rapid Prototyping with 3D Printing: 1. Create a CAD model of the design 2. Convert the CAD model to STL format 3. Slice the STL file into thin cross-sectional layers 4. Build the model one layer atop another 5. Clean and finish the model Rapid Prototyping (RP): • 2 main methods of RP  Additive Rapid Prototyping (ARP) ➢ Machine reads a CAD drawing and builds the object layer by layer  Subtractive Rapid Prototyping (SRP) ➢ Machine cuts a solid block with a cutting tool and carves away material layer by layer to build final object • Benefits of SRP  Great for testing  Uses a wide variety of inexpensive material  Products built in less time  No hand finishing Different ARP Techniques: 1. Stereolithography (SL)  Uses light sensitive liquid polymer  3D models solidify when exposed to UV light  Good for prototypes with fine details  Favored in aerospace  Inexpensive 2. Laminated Object Manufacturing  Layer of adhesive-coated sheet material bonded together  Inexpensive 3. Selective Laser Sintering  Laser beam fuses powdered materials (nylon, elastomer and metal) 4. Fused Deposition Modeling (FDM)  Variety of machines (fast concept to slow concept, also high-precision)  Materials include ABS, elastomer, polycarbonate, polyphenolsulfone and casting wax  Builds small, durable components  Not good with surface finish and accuracy 5. Solid Ground Curing (SGC)  Uses UV light to selectively harden polymers 6. 3-D Ink Jet Printing  Parts are built on a platform in a bin of powder  Powder is selectively hardened with binder deposited with the print head Dimension Process: • Products are built from the bottom up, one layer at a time using ABS plastic or other material • STL files are imported into Catalyst software that automatically slices and orients the parts • A thin filament of ABS plastic is passed through a liquefier • An extrusion head deposits the material in layers in a semi-molten state and the object is produced • Support structures are removed are the product is completed Lecture 11, 12, 13: Engineering Statistics: -Statistical analysis is the science of data collection and data interpretation -uses formal probabilistic methods for drawing inferences and making decisions from these data Definition of Statistic: A statistic is a specified, determinable function of a set of observations (Data Set) -Given n measures of some ‘random’ quantity: x yoi can calculate various statistics such as these examples: -Statistics are number that we can use to describe a large number of measurements Sometimes an arithmetic average (Mean) is relevant because it gives an indication of where the ‘center’ of the measurement distribution is Mean (arithmetic average): the sum of all the data divided by the number of data points Notation depends on whether we are computing the mean of a population (N) or of a sample (n) -Median is another statistic used to indicate the ‘center’ Median: the value of the data point in the center of the data set when arranged in ascending (or descending) order, if there is an even # of points, the median is the mean of the two center data points -Mode is a static used to indicate the most probable measurement Mode: one (or more) sets of numbers that occurs with the greatest frequency, it is possible for a data set to have no mode Histograms: Valuable statistical tool for showing the frequency distribution of data -information about the looks of the histogram can provide clues about the underlying process that generates the data How to make a Histogram : 1. Start with a data set of observations 2. Find the big Range (=max value-min value) 3. Make a ‘number’ of bins with smaller equal-size ranges that span the big range 4. Count how many of your data fit in the ranges of your bins There are many types of distributions for your graph but many engineering measurements have normal (near normal) distributions Outlier: data point that appears to deviate markedly from other members of the sample in which it occurs. You should only eliminate outliers after very, very careful consideration Measures of Variation (Width): -A measure of variation is a number that indicates the extent to which data are spread out around the mean (Standard deviation, Variance, Range) -Three ways of indicating with a number how far x is from the mean i Standard Deviation (Population): Standard Deviation (Sample test): Variance: -Variance is simply the square of the standard deviation Summary of Statistics: Mean- Sum of all the data divided by the # of data points (gives center point) Median- The # in the center of the data Mode- The most occurring value in the data Standard Deviation- The width of the data around the mean value Range- max value – min value Standard Error: -Standard Error is related to the width of the sample distribution. -Indicates how well you know the mean The standard error associated with the estimated population mean is Where zc= 1.96 for 95% confidence Common confidence Values: Continuous Form of the Gaussian Distribution: Standard Normal Distribution: -We created a special table that eliminates the need to perform a new integration every time - Use a standard form, otherwise you need a table for every new mean and standard deviation Standard normal distribution, Small Samples: -Gosset showed that small samples taken from an essentially normal population have a wider confidence interval than we could predict with z- statistics -For small samples we must use t-statistics - When using large number t-statistics become z-statistics, when you get close to 30 they start to become the same (use z-statistics for anything over n=30) Estimating Proportional Mean Values: Tossing a Coin -P(H) = P(T) = 0.5 -Try 100 tosses and find: 58/100 are heads in one trial 45/100 are heads in another trial -These ratios are called proportions Population Mean for Proportions: -Estimate the proportion ‘p’ by sampling Example: Suppose we take n = 150-coin flip, 80 of them are heads, what error do we put on p? Lecture 14 & 15: Correlation and Regression: Bivariate Relationships -Two variable statistics -Pairs: (xi yi we want to know if there’s a relationship between these variables, and if we can predict one from another Linear Correlation Between -1 and 1 r indicates how well a line of best fit correlates with the data • When r is close to 1, it means that -x is large when y is large, and x is small when y is small -the plot of (x,y) is tightly packed • When r is close to -1, it means that -x is large when y is small, and x is small when y is large -the plot of (x,y( is tightly packed -we say this is a “Good negative correlation” • If r is near 0 -little or no (linear) relationship exists between x and y Correlation is NOT the slope Correlation plots will always be at 45 degrees when 1.0 or -1.0 No linear correlation when you can’t put a line through it ONLY talk about linear correlation in this course Regression • The correlation coefficient expresses the strength of the relationship between x and y without worrying about units or direction relationships • However, if x and y are correlated, this implies that we can use information about x to predict values of y • If we want to predict y based on x we need to perform a regression 2 (yiy) -Fits a mathematical model to the data, allows us to test if the model provides a good description of the data and allows us to predict other values of data with confidence -Line of best it is, y(hat) = mx + b y(hat) = predicted dependent variable x= independent variable m= slope (difference in y(hat) associated with a change of x by 1 unit) b= y-intercept (value of y(hat) when x= 0) Linear Regression SSE (sum of squared errors) = sum (y – y(iat)i The parameters of m and b of the best fit line are determined by finding the line that minimizes SSE 2 m(slope)= sum (x-xibar)) (y-yibar)) / sum (x-xibar)) (Sum of squares of the deviations from the regression) SSR = sum (y(hat)– y(bar)) 2 2 (Total sum of squares from the mean) TSS = sum (y – y(bar)) Explained variation = SSR / TSS Unexplained Variation = SSE / TSS TSS = SSR +SSE TSS
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