Syllabus
MANE 3351.01 & A: Manufacturing Engineering Analysis
Syllabus
Fall 2025
Subject to any new Texas legislative mandate changes.
Course Information
Lecture Meeting Days, Time, Location: MW 8:00 - 9:15 EACSB 2.102 Laboratory Meeting Days, Time, Location: MW 9:30 - 10:45 EACSB 2.102
Course Modality: Traditional Face-to-Face Courses (TR)
Instructor Information
Instructor Name: Dr. Doug Timmer
UTRGV E-mail: douglas.timmer@utrgv.edu Office Phone: 956-665-2608 Office Location: ENGR 3.258 Office Hours: M - R 2:00 PM - 3:15 PM
Welcome and Teaching Philosophy
Welcome to MANE 3351 - Manufacturing Engineering Analysis. This course will focus on improving your program skills to solve common numerical analysis problems in engineering. The Python programming language will be used almost exclusively. Developing applications using Raspberry Pi's and Arduinos will also be included.
Course Description, Prerequisites & Course Modality
COURSE DESCRIPTION
Topics include linear algebra, numerical methods and programming with engineering analysis software;
COURSE PREREQUISITE
MATH 2414 (or MATH 2488) and CSCI 1380 (or CSCI 1387)
Mode of Learning
This course is scheduled to be taught as a traditional face-to-face mode. UTRGV policies and CDC recommendations will be followed.
Course Assignments & Learning Objectives
Course Assignments
Course assignments will fall into one of three categories: laboratories, homework and examinations. Further details will be provided in the course schedule and assessment section.
Learning Objectives
There are four student learning outcomes (SLOs) for MANE 3351. The table below lists the SLOs. Maps the SLOs to the Program Student Learning Outcomes and provides the assessment technique for each SLO.
| Student Learning Outcome | Program Student Learning Outcomes | Major Course Requirement/Major Assignment/Examination |
|---|---|---|
| SLO-1. Construct computational solutions in Python and Octave to solve engineering problems | ABET SO 1, an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | Laboratories |
| SLO-2. Compare algorithms for solving engineeering problems using numerical methods | ABET SO 1, an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | Homework Assignments, Test 1 and Final Examination |
| SLO-3. Creating interactive visual solutions | ABET SO 1, an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | laboratories |
| SLO-4. Using Raspberry Pi and Arduino to explore Linux and Internet of Things | ABET SO 1, an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | Laboratories |
Assessment of Learning/Grading Policy
Your performance in this course will be evaluated in the following manner:
| Component | % of Overall Grade |
|---|---|
| Examinations (Test 1, Test 2, Final) | 66% (equally weighted) |
| Laboratories | 22% |
| Homework | 12% |
Examinations
There will be three examinations which are equally weighted. The final examination is not comprehensive. Students can prepare a single, hand-written 4 inch by 6 inch notecard for each exam. In addition, tables from your textbooks will be provided as needed. Otherwise the exams will be closed book.
Laboratories
There is only one method to learning programming and that is to program. For the majority of the class, there will be weekly laboratory assignments. Laboratory assignments will be completed on the Raspberry Pi and submitted using GitHub. More details will be provided about the laboratories once the class starts.
Homework
For the majority of the class, there will be weekly homework assignments. The homework assignments will focus on the analytical portion of the class and the ability to use pseudo-code.
Letter Grade Assignment
An overall course average will be calculated using the weighting scheme specified above. Your course average will be a value between 0 and 100. Your final letter grade will be assigned using the following definition. UTRGV's grading policy is to use straight letter grades (A, B, C, D, or F) (no + or -).
| Course Average | Letter Grade |
|---|---|
| 90 - 100 | A |
| 80 - 89 | B |
| 70 - 79 | C |
| 60 - 69 | D |
| <60 | F |
Late Work
Descriptions of each assignment, including due dates, will be provided throughout the course. All assignments should be submitted on their due date using the provided Brightspace drop box. The course policy for late work is a 10% penalty per day for work submitted after the deadline. After one week, no credit will be given for late work. No late work will be accepted after study days. There will be certain assignments where late work will not be accepted. This fact will clearly be stated in the appropriate assignments.
Students who miss graded assignments will receive a grade of zero. If you are ill or have a serious problem that prevents you from submitting an assignment on the day it is due, please contact me prior to the due date and we will arrange an alternative date.
Extra Credit Policy
It is my personal policy to not provide extra credit in this course.
Attendance Policy
All students are expected to attend all scheduled course meetings. Students often find it challenging to attend 8AM lectures. If timely attendance is not observed, the instructor reserves the right to change the grading policy and grade student attendance at 8:00 AM.
Required Readings, Technology Needs, and Resource Materials
The following resources are required for completion of Manufacturing Engineering Analysis.
Required Textbook
The following textbook is required for this course. It is an open source (free) textbook that can be downloaded or purchased for a small fee. Note that using an electronic version (pdf) is acceptable.
Brin, Leon. Tea Time Numerical Analysis: Experiences in Mathematics, 3rd edition. Southern Connecticut State University.
The following textbook is recommended for this course. It, too, is an open-source textbook.
Recommended Textbook
The following (free) book is an excellent resource on learning Python.
VanderPlas, Jake. A Whirlwind Tour of Python. O'Reilly Media Inc.
Technology
Students will be issued Raspberry Pi 4 single board computers and Arduino Unos along with bread boards and other electronic components to complete the first three laboratory assignments. The remaining laboratory assignments can be completed using personal computers or Raspberry Pis. Students are encouraged to install Anaconda Python on personal computers and bring their computers to both lectures and laboratory sessions.
Tentative Calendar of Activities
A tentative weekly schedule of the lecture activities (MANE 3351.01) is provided in the table below.
| Week, Date, Theme | Learning Objective | Readings Due | Assignments Due |
|---|---|---|---|
| 1, 9/1 - 9/7, Course Introduction | |||
| 2, 9/8 - 9/14, Python and GitHub | SLO-1, SLO-4 | Homework | |
| 3, 9/15 - 9/21, Numerical Analysis and Arduinos | SLO-1, and SLO-2 | Portions of Chapter 1 | Homework |
| 4, 9/22 - 9/28, Markdown and Taylor Series Expansions | SLO-1, SLO-2, and SLO-3 | Portions of Chapter 1 | Homework |
| 5, 9/29 - 10/5, Bisection Method | SLO-1, and SLO-2 | Portions of Chapter 2 | Homework |
| 6, 10/6 - 10/12, False Position Algorith | SLO-1, and SLO-2 | Portions of Chapter 2 | Homework, Test 1 |
| 7, 10/13 - 10/19, Newton's and Secant Method | SLO-1, and SLO-2 | Portions of Chapter 2 | Homework |
| 8, 10/20 - 10/26, Numerical Integration | SLO-1, and SLO-2 | Portions of Chapter 4 | Homework |
| 9, 10/27 - 11/2, Romberg Integration and Gaussian Quadrature | SLO-1, and SLO-2 | Portions of Chapter 4 | Homework |
| 10, 11/3 - 11/9, Numerical Differention and Linear Algebra | SLO-1, and SLO-2 | Portions of Chapter 4 | Homework |
| 11, 11/10 - 11/16, Linear Algebra and Determinants | SLO-1, and SLO-2 | Homework | |
| 12, 11/17 - 23, Matrix Inversion | SLO-1, and SLO-2 | Test 2 and Homework | |
| 13, 11/24 - 11/30, Gaussian Elimination | SLO-1, and SLO-2 | Homework | |
| 14, 12/1 - 12/7, Octave/Matlab and Open session | SLO-1, and SLO-2 | Homework | |
| 15, 12/8 - 12/14, Open Session and Review | SLO-1, and SLO-2 | ||
| 16, 12/15 - 12/19, Final Exam Week |
A tentative weekly schedule of the laboratory session (MANE 3351.A) is provided in the table below.
| Week, Date, Theme | Learning Objectives | Laboratory Assignments Due |
|---|---|---|
| 1, 9/1 - 9/7, Course Introduction | ||
| 2, 9/8 - 9/14, Raspberry Pi | SLO-1, and SLO-4 | Lab 1 |
| 3, 9/15 - 9/21, Arduinos | SLO-1, and SLO-4 | Lab 2 |
| 4, 9/22 - 9/28, Arduinos | SLO-1, and SLO-4 | Lab 3 |
| 5, 9/29 - 10/5, Arduinos | SLO-1, and SLO-4 | Lab 4 |
| 6, 10/6 - 10/12, GitHub Desktop | SLO-1, and SLO-2 | Lab 5 |
| 7, 10/13 - 10/19, Python | SLO-1, SLO-2, and SLO-3 | Lab 6 |
| 8, 10/20 - 10/26, Python | SLO-1, SLO-2 and SLO-3 | Lab 7 |
| 9, 10/27 - 11/2, Python | SLO-1, SLO-2, and SLO-3 | Lab 8 |
| 10, 11/3 - 11/9, Python | SLO-1, SLO-2, and SLO-3 | Lab 9 |
| 11, 11/10 - 11/16, Python and ChatGPT | SLO-1, SLO-2, and SLO-3 | Lab 10 |
| 12, 11/17 - 23, Python | SLO-1, SLO-2, and SLO-3 | Lab 11 |
| 13, 11/24 - 11/30, Python | SLO-1, SLO-2, and SLO-3 | Lab 12 |
| 14, 12/1 - 12/7, Octave Demo | ||
| 15, 12/8 - 12/14, No Meeting | ||
| 16, 12/15 - 12/19, Final Exam Week |
Please note that all reading assignments and assessment activities dates are subject to change. These dates are my best estimate of how the course will proceed but, usually, changes are inevitable.
Recorded Material Policy
Should you elect to record your instruction, sample syllabus language is included here:
The use of classroom recordings is governed by the Federal Educational Rights and Privacy Act (FERPA), UTRGV's acceptable-use policy, and UTRGV HOP Policy STU 02-100 Student Conduct and Discipline. A recording of class sessions will be kept and stored by UTRGV, in accordance with FERPA and UTRGV policies. Your instructor will not share the recordings of your class activities outside of course participants, which include your fellow students, teaching assistants, or graduate assistants, and any guest faculty or community-based learning partners with whom we may engage during a class session. You may not share recordings outside of this course. As referenced in UTRGV HOP Policy STU 02-100 Student Conduct and Discipline, doing so may result in disciplinary action.
Use of Artificial Intelligence (AI) Technologies
Generative AI technologies are growing and evolving rapidly. We will have an opportunity to explore the benefits, challenges, and ethical decisions engineers encounter in the use of AI in this course. Generative AI will be incorporated into this course in a limited and specified manner.
Students are discouraged from using Generative AI technologies such as Chat GPT or Microsoft Copilot for homework. The goal of homework is to gain confidence and proficiency in your engineering analysis and Generative AI technologies will not be available for in-class tests. Submission of printed Generative AI output for student homework will result in a grade of zero and be reported as academic dishonesty.
For most laboratory assignments, Generative AI may not be submitted. Each laboratory assignment will contain a clear statement whether Generative AI can or cannot be used. Submission of code generated using Generative AI when not allowed will result in a grade of zero and will be reported as academic dishonesty.
ACADEMIC INTEGRITY
Academic integrity is fundamental in our actions, as any act of dishonesty conflicts as much with academic achievement as with the values of honesty and integrity. Violations of academic integrity include, but are not limited to: cheating, plagiarism (including self-plagiarism), and collusion; submission for credit of any work or materials that are attributable in whole or in part to another person; taking an examination for another person; any act designed to give unfair advantage to a student; or the attempt to commit such acts (Board of Regents Rules and Regulations, STU 02-100, and UTRGV Academic Integrity Guidelines). All violations of Academic Integrity will be reported to Student Rights and Responsibilities through Vaqueros Report It.
Student Support Resources
Technical Support
If you need assistance with course technology (Bright Space) at any time, please contact the Center for Online Learning and Teaching Technology (COLTT).
University Policy Statements
SEXUAL MISCONDUCT AND MANDATORY REPORTING (Required)
In accordance with UT System regulations, your instructor is a "Responsible Employee" for reporting purposes under Title IX regulations and so must report any instance of sexual misconduct, which includes sexual assault, stalking, dating violence, domestic violence, and sexual harassment to the Office of Title IX and Equal Opportunity (otixeo@utrgv.edu). More information can be found on the OTIXEO website. If students, faculty, or staff would like confidential assistance, or have questions, they can contact OAVP (Office for Advocacy & Violence Prevention).
STUDENT ACCESSIBILITY SERVICES
Student Accessibility Services has offices on Brownsville and Edinburg campuses. Visit the SAS web page to learn more and explore accessibility services.
STUDENTS WITH DISABILITIES
Students with a documented disability (physical, psychological, learning, or other disability which affects academic performance) who would like to receive reasonable academic accommodations should contact Student Accessibility Services (SAS)) for additional information. The student must apply for accommodations using the mySAS portal and is responsible for providing sufficient documentation of the disability to SAS. Upon submission of the request, students should expect to participate in an interactive discussion, or an intake appointment, with SAS staff. Accommodations may be requested at any time but are not retroactive, meaning they are valid moving forward after approval by SAS. Students should contact SAS early in the semester/module for guidance.
Students who experience a broken bone, severe injury, or undergo surgery may also be eligible for temporary accommodations. Please contact Student Accessibility Services (SAS)) for more information.
PREGNANCY, PREGNANCY-RELATED, AND PARENTING ACCOMODATIONS
Title IX of the Education Amendments of 1972 prohibits discrimination based on sex, which includes discrimination based on pregnancy, marital status, or parental status.
Students seeking accommodations related to pregnancy, pregnancy-related condition, or parenting should submit the request using the form found at Pregnancy and Parenting | UTRGV.
MANDATORY COURSE EVALUATION PERIOD
Students have the opportunity to complete an ONLINE evaluation of this course through Watermark Course Evaluations and Surveys, which may be accessed through my.UTRGV or the Bright Space course module. Course evaluations are used by the instructor to inform revisions of the course to ensure student success. Course evaluations are also used by the instructor for annual performance review, promotion applications, teaching award applications, among others.
Online evaluations will be available on or about:
Fall 2025 Regular Term November 19 -- December 18, 2025
Important Course Dates
A subset of the events in the academic calendar is provided below.
| Date | Event |
|---|---|
| September 1, 2025 | Labor Day Holiday |
| September 2, 2025 | Fall classes begin |
| September 17, 2025 | Census Day |
| November 13, 2025 | Last day to drop or withdraw |
| November 27 - 29, 2025 | Thanksgiving Holiday. No Classes |
| December 11, 2025 | Study Day. No classes |
| December 12 - 18, 2025 | Final Exams |
| December 16, 2025 8:00 - 9:45 AM | MANE 3351 Final Exam (proctored) |
| December 19 - 20, 2025 | Commencement Exercises |
| December 22, 2025 | Grades are due at 3 pm |
I volunteer as an ABET program evaluator and will be on an ABET visit from December 13 - 17. During this time, I will not be available to answer any questions and will have very limited time for non-ABET communications.