ISE 5570
Transcript Abbreviation:
ManfacDataProcAnal
Course Description:
Project-based introduction to manufacturing data streams and methods to process and analyze them towards solving manufacturing problems in process planning and quality control.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings:
no
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
(CSE 1222 or CSE 1223 or CSE 1224 or CSE 2021) and (STAT 3470 or STAT 3450 or STAT 3460) and Major in Engineering or instructor approval
Electronically Enforced:
Yes
Exclusions:
having taken this course as a 5194
Course Goals / Objectives:
Expose students to typical data streams generated in manufacturing workflows, including: process planning, process monitoring sensors, processing logs, post process inspection, etc.
Introduce common data processing methods, including: data organization and filtering, image processing, data registration and fusion, etc. in the context of data provenance and application
Introduce common data analytic methods, including: regression, outlier detection, causal analysis, optimization, etc. in the context of data quality and application
Provide practical experience with algorithm development and decision-making with presented with applied manufacturing problems
Check if concurrence sought:
Yes
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Introduction to manufacturing data streams and practical considerations of data origin, sources of error/noise, and other limitations to use | 3 | 0 | 0 | 0 |
| Overview of selected manufacturing process and the critical data streams associated with their workflows | 6 | 0 | 0 | 0 |
| Data processing methods and implementations for data streams including, but not limited to image data, time series data, process logs, mechanical and profile measurements | 14 | 0 | 0 | 0 |
| Data analytics methods and implementations for relevant manufacturing problems including, but not limited to process planning and quality control | 14 | 0 | 0 | 0 |
| Total | 37 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Project 1 (All assignments will be workflow development, algorithm development and implementation, and decision-making projects. Collected materials will be 1) a written documents detailing workflow and proposed algorithm, as well as a discussion of resul | 33.3% |
| Project 2 | 33.3% |
| Individual Assessment Process | 33.29% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| no textbook - lecture slides provided |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Substantial contribution (3-6 hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Some contribution (1-2 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 3 | Substantial contribution (3-6 hours) | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| 4 | Some contribution (1-2 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 5 | Some contribution (1-2 hours) | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| 6 | Significant contribution (7+ hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| 7 | Substantial contribution (3-6 hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info:
Advanced Writing
2.3 Develop scholarly, creative or professional products that are meaningful to them and their audience
Data Analysis - Quantitative
1.1A Explain basic concepts of statistics and probability
Technology
1.2 Recognize how technologies emerge and change
Attachments
(N/A)
Additional Notes or Comments:
This course has been offered 3 times as a 5194 and now we would like to make it a permanent course.