Dr. Binil Starly

Dr. Binil Starly

Industrial and Systems Engineering

Phone: 919-515-1815
Instructor Website

ISE 725 601 Smart Manufacturing

3 Credit Hours

Smart manufacturing is a confluence of information technology, human-machine interaction, data sciences and manufacturing process technology. It really is about increasing efficiency within factory floor operations and gaining visibility into the production floor. It has several layers of technology components that drive the digital transformation of the manufacturing industry. It encompasses intelligent machines, human-computer interfaces, data analytics, process sensing and control, process automation, information systems and computing to form the factory of the future. This course is intended to introduce what makes manufacturing machines ‘smart’ within the realm of ‘smart manufacturing’. It introduces some of the key fundamental knowledge necessary to implement ‘smart manufacturing’ within factory floors. This includes machine communication protocols from PLCs to servers, from the edge device to the cloud. It includes storing this data in databases that can handle data streams. Once this sensor data (from temperature to accelerometer or even still images) are stored, analysis of the time-series data and image data enable factories to be ‘monitored’ and for quality inspection. All of the concepts and practical implementation will be brought together with Raspberry PI – which is affectionately called as ‘Raspi’. It will have temperature, accelerometer and a camera to mimic a basic manufacturing machine. While ‘smart manufacturing’ can contain a number of enabling technology platforms – this course particularly focuses on the information technology aspects within a ‘smart’ factory.


A working knowledge of the Python Programming Language is required. SQL or rather writing structured query language queries is desired but not required. Any graduate student in Engineering can take this class.

Course Objectives

By the end of this course, the student will be able to:

  • Describe the underlying software stack for ‘Smart Manufacturing’ within Factories.
  • Utilize client-server programming with associated front-backend software infrastructure to build a cloud-hosted application.
  • Understand the importance of various sensors within Smart Factories and be able to conduct time-series analysis of sensor data (Temperature, Vibration)

Describe various machine communication protocols used in Digital Factories across the ISA-95 layers.

Topic Outline


Part 1 Smart Manufacturing Lecture Content
Part 2 Streaming Data from Device to Cloud with “Raspi”
Part 3 Analyzing Sensor Data through Time-Series Analysis for “Raspi”
Part 4 Machine Vision for Quality Inspection by “Raspi”
Part 5 Application Discussion of “Smart Manufacturing”

Course Requirements

Homework 25%
Mini-Projects 35%
Research Paper 25%
Class Participation 15%


Rasberry PI-4 Hardware Requirements (IMPORTANT):

  • Raspberry Pi-4 kits will be made available for each student who is on campus. Kits can be shipped to students within the US who are located anywhere standard shipping exists. For those outside of the US, kits must be separately bought from local distributors available in your country. For those who would like to keep a personal kit, the kit should be less than $100. For those who do not have access to the kit, you can still partake in class activities through remote login over a VPN network. Please direct questions to Dr Starly (


None required.

Computer and Software Requirements

Please review minimum computer specifications recommended by NC State University and Engineering Online.

Requirements for THIS course:


  • Registration on the Free Level Account with Heroku
  • Registration on the Github NCSU Server Site or a public domain Github account
  • Installation of Python, PostGreSQL and MongoDB (all of which are free to install)


Computing Hardware:

A laptop/desktop with at least 16GB RAM memory is required. Remote Access PCs are available through NC State ISE Remote Labs on a reservation basis.


Updated 10/21/2020