San Josť State University

Department of Mechanical Engineering

ME 283 Automatic Control of Manufacturing Processes

Section 01, Fall 2013


Class Hours and Location:  Mon/Wed 16:30-17:45 in Engineering 340

Class Code:  46914

Credit Units:  3


Instructor:  Prof. John Lee

Office, Phone, Email:  Engineering 310,  408-924-7167,

Office Hours:  Tue/Wed 11:00-12:00; changes will be posted on the instructor's website.


Course Description

Develops general concepts for control of manufacturing processes. The concepts of and tools for process modeling, process optimization and process control. Emphasizes the integrated approach combining statistical process control (SPC) and automatic process control.

Prerequisites:  BSME or Instructor Consent.


Required TextbookIntroduction to Statistical Quality Control, 7th Edition, by D. C. Montgomery. Published by John Wiley & Sons, 2013, ISBN 9781118146811.  An eTexbook option  is avaiable from


Course Website:  This course heavily uses the Canvas learning management system (LMS), accessible to enrolled students at  Successful completion of course requirements will require accessing the course website regularly, typically multiple times per week.  Technical support is available at


Delivery Mode:  This is a "mixed-mode" course, such that some core activities will be in person and some core activities will be online.  Some activities (e.g. pre-reccorded lectures, online discussions) will be conducted online via Canvas and/or WebEx, and all class members must have access to a compatible web browser.  Not all class meetings mandate live attendance.  By default the whole class will meet in person every scheduled Wednesday, but some Mondays may instead be used for asynchronous lessons and/or small-team activities.  Plans will be communicated at least one week in advance, with updates provided via Canvas.


Email:  Messages regarding this class may be sent to student email addresses listed in MySJSU or in Canvas.  So each student is required to maintain an up-to-date email address in both systems.


Workload:  Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica.  More details about student workload can be found in University Policy S12-3 at



Course Goals

  1. Develop an approach to improving quality in manufacturing processes with the fundamental goal of reducing variability.

  2. Learn core principles of statistical process control, design-of-experiments, and discrete process control.

  3. Interpret manufacturing processes in terms of controllable parameters, measurable variables, disturbance sources, and decision strategies.

  4. To promote and develop lifelong learning skills, both independently and through effective teamwork.

Student Learning Objectives  Upon successful completion of this course, the student should be able to:

  1. Explain the significance and benefits for controlling variability as a fundamental goal in manufacturing process control.

  2. Identify specific controllable parameters, measurable variables, and disturbance sources for specific manufacturing processes.

  3. Apply tools from statistical process control, in particular control charts for process monitoring.

  4. Apply tools from design-of-experiments, in particular factorial designs, fractional factorials, analysis-of-variance, and response surfaces.

  5. Apply knowledge of manufacturing processes to tools of automatic process control, including transfer functions, feedback loops, and discrete process control.

  6. Select and justify a strategy as well as a set of tools for reducing variability in a given manufacturing process.


Graded Work and Weight Distribution

10% for Participation Tasks

15% for Reading Quizzes

15% for Homework

15% for SPC Project

15% for DoE Project

30% for Final Exam


Grading Scale

The overall course grade is calculated from a weighted sum of all graded components, computed as grade point average (GPA) on a 4.0 scale as follows:

  3.85-4.00 A+ | 3.70-3.84 A | 3.50-3.69 A- | 3.15-3.49 B+ | 2.85-3.14 B | 2.50-2.84 B-

  2.15-2.50 C+ | 1.85-2.14 C | 1.50-1.84 C- | 1.15-1.50 D+ | 0.85-1.14 D | 0.50-0.84 D- | 0-0.49 F

Work that is evaluated directly by letter grade receives the following values on 4.0 scale:

  A+ = 4.0 | A = 4.0 | A- = 3.7 | B+ = 3.3 | B = 3.0 | B- = 2.7

  C+ = 2.3 | C = 2.0 | C- = 1.7 | D+ = 1.3 | D = 1.0 | D- = 0.7 | F = 0.0

Work that is evaluated by percent score is converted to letter grade as follows:

  97.0-100 A+ | 93.0-96.9 A | 90.0-92.9 A- | 87.0-89.9 B+ | 83.0-86.9 B | 80.0-82.9 B-

  77.0-79.9 C+ | 73.0-76.9 C | 70.0-72.9 C- | 67.0-69.9 D+ | 63.0-66.9 D | 60.0-62.9 D- | 0-59.9 F


Participation Tasks

Throughout the semester there will be several tasks to promote active engagement, particularly online.  There will be a running tally of credit for  keeping up with these assigned tasks according to strict deadlines.  The percentage of credit earned will be multiplied by 4.0 to determine grade for this component of semester grade (e.g. 75% of possible credit earned means 3.0, equivalent to a letter grade of "B").  In a typical week there would be at least one and up to five distinct participation tasks, assigned and tallied via Canvas.


Reading Quizzes

In-class quizzes will be held on most Wednedays to promote accountability with assigned reading.  These quizzes are intended to be short (typically 10-15 minutes).   Scope for upcoming quizzes will be narrowed via online discussion in Canvas.  There are no make-up quizzes and there is no extra time allowed for tardiness.   Missing a quiz will result in a score of zero, but to accommodate unavoidable absence for any reason (e.g., illness, mandatory travel, work obligations, family duties), the two lowest quiz scores (zero or otherwise) for each student will be excused from average grade computation.



Unless otherwise noted, regular homework will typically be assigned on Wednesdays and will be due at the beginning of class the following Wednesday.  Collaborating with others on approach and strategy is encouraged, and in some cases solutions may even be revealed by the instructor via online discussion.  However, all homework must still be freshly prepared and submitted individually.  Merely copying from classmates or prior solution sets constitutes cheating.



The projects apply skills in statistical process control and design of experiments to "real life" scenarios.  The projects are also intended to develop (1) ability to formulate and solve open-ended problems, (2) teamwork, and (3) engineering communication skills.  More details about requirements and expectations will be provided via separate documents for each project.


Team Assignments and Peer Grading

Team assignments may be used for some portions of the course (projects in particular). Furthermore, some assignments may involve peer grading.  Alternatives will be considered for compelling reasons, but arrangements must be pre-approved in writing with ample time before corresponding deadlines (i.e. several days in advance).


Exception Handling

Any grading exceptions or appeals must be petitioned promptly in writing (email is acceptable).  Late assignments will normally be recorded with zero credit in the grade roster, but may be submitted promptly for consideration in context with all other exceptions class-wide.  Evaluation of exceptions will normally happen at the very end of the semester.  Special consideration of truly unavoidable and extenuating circumstances will depend on expeditious timing and supporting documentation (e.g. doctor's note, jury summons, military orders).



Preliminary Course Schedule

Schedule is subject to change with fair notice via announcement in class, notification in Canvas, and/or MySJSU email.


Aug 21 Wed:  Introduction

Aug 26 Mon:  Statistical Process Control (SPC) Overview; Variation and Distributions

Aug 28 Wed*:  Variation and Distributions

Sep 02 Mon:  Labor Day - campus closed

Sep 04 Wed:  SPC Project initiation; HW1 due

Sep 09 Mon@:  Statistical Inference

Sep 11 Wed*:  Statistical Inference; HW2 due

Sep 16 Mon@:  Control Charts

Sep 18 Wed*:  Control Charts; HW3 due

Sep 24 Mon@:  Control Charts (CUSUM)

Sep 26 Wed*:  Control Charts (EWMA); HW4 due

Sep 30 Mon@:  Uncertainty Analysis

Oct 02 Wed*:  SPC Project pre-evaluation and refinement

Oct 07 Mon:  Design of Experiments (DoE) Overview; SPC Project due

Oct 09 Wed:  Factorial Design

Oct 14 Mon@:  Factorial Design

Oct 16 Wed*:  DoE Project initiation; HW5 due

Oct 21 Mon@:  Analysis of Variance

Oct 23 Wed*:  Fractional Factorials; HW6 due

Oct 28 Mon@:  Regression Modeling

Oct 30 Wed*:  Process Optimization; HW7 due

Nov 04 Mon@:  Robustness

Nov 06 Wed:  DoE Project pre-evaluation and refinement

Nov 11 Mon:  Veteran's Day - campus closed

Nov 13 Wed:  Discrete Process Control (DPC) Overview; DoE Project due

Nov 18 Mon@:  Feedback Control Basics

Nov 20 Wed*:  Feedback Control Basics

Nov 25 Mon@:  Digital Control Concepts

Nov 27 Wed*:  Run-by-Run Control; HW8 due

Dec 02 Mon:  Discrete Process Simulation

Dec 04 Wed:  Discrete Process Simulation

Dec 09 Mon:  (Review); HW9 due

Final Exam will be held on December 12 Thursday, 14:45-17:00


* Tentative dates for Reading Quizzes

@ Tentative dates for online alternative


University Calendar Reminders

Sep 03 Tue:  Last day to drop courses

Sep 10 Tue:  Last day to add courses

Nov 28 Thu:  Thanksgiving holiday - campus closed

Dec 09 Mon:  Last day of instruction



Registration Policies and Academic Calendar

Each student is personally responsible for understanding the university registration policies available at  Academic deadlines are listed at  The Late Drop policy is available at  Students should be aware of the current deadlines and penalties for dropping classes.  Information about the latest changes and news is available at the Advising Hub at


Consent for Recording of Class and Public Sharing of Instructor Material

Course material developed by the instructor (including video and audio recordings) is the intellectual property of the instructor and may not be shared publicly without written approval.  You may not publicly share or upload any instructor-generated material for this course such as exam questions, lecture notes, homework solutions, or video links without written consent.  Also, University Policy S12-7 requires students to obtain instructorís permission to record the course.


University Policies on Academic Integrity

Your commitment as a student to learning is evidenced by your enrollment at San Jose State University. The Academic Integrity policy requires you to be honest in all your academic course work.  Faculty members are required to report all infractions to the office of Student Conduct and Ethical Development  Instances of academic dishonesty will not be tolerated. Cheating on exams or plagiarism (presenting the work of another as your own, or the use of another personís ideas without giving proper credit) will result in a failing grade and sanctions by the University. For this class, all assignments are to be completed by the individual student unless otherwise specified. If you would like to include your assignment or any material you have submitted, or plan to submit for another class, please note that SJSUís Academic Policy S07-2 requires approval of instructors.


Campus Policy in Compliance with the American Disabilities Act

If you need course adaptations or accommodations because of a disability, or if you need to make special arrangements in case the building must be evacuated, please inform the instructor as soon as possible. Presidential Directive 97-03 requires that students with disabilities requesting accommodations must register with the Accessible Education Center (AEC) to establish a record of their disability.


Other Campus Resources for Student Success

Engineering Student Success Center 

Student Academic Success Services (SASS)

Academic Success Center

Writing Center

SJSU Library