ENGI75243
Machine Vision Systems
Sheridan
 
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
 

Land Acknowledgement

Sheridan College resides on land that has been, and still is, the traditional territory of several Indigenous nations, including the Anishinaabe, the Haudenosaunee Confederacy, the Wendat, and the Mississaugas of the Credit First Nation. We recognize this territory is covered by the Dish with One Spoon treaty and the Two Row Wampum treaty, which emphasize the importance of joint stewardship, peace, and respectful relationships.

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Section I: Administrative Information
  Total hours: 30.0
Credit Value: 3.0
Credit Value Notes: N/A
Effective: Winter 2023
Prerequisites: N/A
Corequisites: N/A
Equivalents: N/A
Pre/Co/Equiv Notes: N/A

Program(s): Robotics and Industrial Applic
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20230109_01
Status: Approved (APPR)

Section I Notes: Access to course materials and assignments will be available on Sheridan's Learning and Teaching Environment (SLATE). Students will need reliable access to a computer and the internet.

 
 
Section II: Course Details

Detailed Description
This course includes the fundamentals of setting up, operating, and programing a machine vision system for industrial applications. Learners will gain the skills necessary to operate and program Cognex vision systems, to practice configuration of a vision application using the EasyBuilder configuration user interface, and to navigate a spreadsheet programming environment to develop a graphical user interface. Learners will complete topics such as: acquiring images; calibration; locating parts; image filtering; inputs and outputs; lighting and lensing; and troubleshooting associated issues. Learners will apply the skills and knowledge gained in this course to operate, configure, and program vision systems for industrial applications that involve guidance, identification, gauging and inspection (GIGI).

Program Context

 
Robotics and Industrial Applic Program Coordinator(s): N/A
Engineering, Sciences, and Environment Program: Robotics and Industrial Applications Micro-Credential Coordinator: CAPS (with Andy Alubaidy)


Course Critical Performance and Learning Outcomes

  Critical Performance:
By the end of this course, learners will be able to operate, program and troubleshoot with hardware or software essential to machine vision systems used in industrial applications.
 
Learning Outcomes:

To achieve the critical performance, students will have demonstrated the ability to:

  1. Demonstrate how to connect a machine vision camera to a network.
  2. Acquire an image of a part with a vision system camera.
  3. Calibrate an image using real-world measurements to convert the image dimensions from pixels to millimeters or inches.
  4. Utilize locator tools to find parts.
  5. Inspect parts using various tools in presence/absence, measurement, counting, and ID industrial applications.
  6. Utilize the image filter tools in the job to troubleshoot and improve the recognition process.
  7. Implement the core vision tools, including pattern recognition, histogram, blob, and edge tools.
  8. Configure a discrete input and a discrete output line.
  9. Troubleshoot to utilize the proper network communications for the job.
  10. Apply the fundamentals of lighting and optics to gain a good image of the part.
  11. Utilize a programming environment, such as EasyBuilder or In-Sight spreadsheet to troubleshoot and develop a graphical user interface.

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: IN-CLASS
 Project 1 (in-class)20.0%
 Project 2 (in-class)20.0%
 Project 3 (in-class)20.0%
 Project 4 (in-class)20.0%
 Project 5 (in-class)20.0%
Total100.0%

Evaluation Notes and Academic Missed Work Procedure:
To encourage behaviours that will help students to be successful in the workplace and to ensure that students receive credit for their individual work, the following rules apply to every course offered within the Faculty of Continuing and Professional Studies. 1. Students are responsible for staying abreast of test dates and times, as well as due dates and any special instructions for submitting assignments and projects as supplied to the class by the professor. 2. Students must write all tests at the specified date and time. Missed tests, in-class/online activities, assignments and presentations are awarded a mark of zero. If an extension or make-up opportunity is approved by the professor as outlined below, the mark of zero may be revised by subsequent performance. The penalty for late submission of written assignments is a loss of 10% per day for up to five business days (excluding weekends and statutory holidays), after which, a grade of zero is assigned. Business days include any day that the college is open for business, whether the student has scheduled classes that day or not. 3. Students who miss a test or in-class/online activity or assignment or fail to submit an assignment on time due to exceptional circumstances are required to notify their professor in advance of the class whenever possible. A make-up test may be supplied for students who provide an acceptable explanation of their absence and/or acceptable documentation explaining their absence (e.g., a medical certificate). All make-up tests are to be written at a time and place specified by the professor upon the student¿s return. Alternately, students may be given an opportunity to earn the associated marks by having a subsequent test count for the additional marks. Exceptional circumstances may result in a modification of due dates for assignments. 4. Unless otherwise specified, assignments and projects must be submitted at the date and time specified by the instructor. 5. Students must complete every assignment as an individual effort, unless the professor specifies otherwise. 6. Since there may be instances of grade appeal or questions regarding the timely completion of assignments and/or extent of individual effort, etc., students are strongly advised to keep, and make available to their professor, if requested, a copy of all assignments and working notes until the course grade has been finalized. 7. There will be no resubmission of work unless this has been previously agreed to or suggested by the professor. 8. Students must submit all assignments in courses with practical lab and field components in order to pass the course.

Provincial Context
The course meets the following Ministry of Colleges and Universities requirements:


 

Essential Employability Skills
Essential Employability Skills emphasized in the course:

  • Communication Skills - Respond to written, spoken, or visual messages in a manner that ensures effective communication.
  • Critical Thinking & Problem Solving - Apply a systematic approach to solve problems.
  • Personal Skills - Manage the use of time and other resources to complete projects.
  • Interpersonal Skills - Interact with others in groups or teams in ways that contribute to effective working relationships and the achievement of goals.
  • Information Management Skills - Analyze, evaluate, and apply relevant information from a variety of sources.
  • Numeracy - Execute mathematical operations accurately.

Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) - Office of the Registrar

  • Not Eligible for PLAR

 
 
Section III: Topical Outline
Some details of this outline may change as a result of circumstances such as weather cancellations, College and student activities, and class timetabling.
Instruction Mode: In-Class
Professor: N/A
Resource(s):
 TypeDescription
RequiredOtherCourse material will be provided.
OptionalTextbookMachine Vision Algorithms and Applications, Carsten Steger, Markus Ulrich, and Christian Wiedemann, Wiley-VCH, ISBN 9783527407347, 2008

Applicable student group(s): Continuing and Professional Studies: Robotics and Industrial Applications Program
Course Details:

Module 1: Introduction 

Hardware and connections  

Software overview and image acquisition 

Calibration 

(Project 1 P/F) 

 

Module 2: Vision Tools 

Locator tools  

Inspection tools 

Identification  

(Project 2 P/F) 

 

Module 3: Recognition 

Pattern matching and logic 

Histogram and edges 

Blobs and image filters 

(Project 3 P/F) 

 

Module 4: Communication  

Digital I/O 

Network communication 

Deployment  

(Project 4 P/F) 

 

Module 5: Spreadsheet Programming and Troubleshooting 

Spreadsheet environment 

Lighting and optics 

GUI Building 

(Project 5 P/F) 

 



Sheridan Policies

It is recommended that students read the following policies in relation to course outlines:

  • Academic Integrity
  • Copyright
  • Intellectual Property
  • Respectful Behaviour
  • Accessible Learning
All Sheridan policies can be viewed on the Sheridan policy website.

Appropriate use of generative Artificial Intelligence tools: In alignment with Sheridan's Academic Integrity Policy, students should consult with their professors and/or refer to evaluation instructions regarding the appropriate use, or prohibition, of generative Artificial Intelligence (AI) tools for coursework. Turnitin AI detection software may be used by faculty members to screen assignment submissions or exams for unauthorized use of artificial intelligence.

Course Outline Changes: The information contained in this Course Outline including but not limited to faculty and program information and course description is subject to change without notice. Nothing in this Course Outline should be viewed as a representation, offer and/or warranty. Students are responsible for reading the Important Notice and Disclaimer which applies to Programs and Courses.


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