The more team members you enroll in your organization, the more benefits you can acquire. Depending on the number of members enrolled in our courses, you could obtain these benefits:
Become part of the MIT community, the leading institution in the world for innovation.
Bring together technologies like Machine Learning, IoT, and data analytics in manufacturing.
Utilization of a smart machine to demonstrate concepts and incorporate problem-solving skills.
Generate ideas for making the manufacturing process in your own workplace smarter.
Unite new technologies, such as machine learning, the Internet of Things, and data analysis, resulting in comprehending the transformation process currently happening in the manufacturing sector. Discover the latest trends and problem resolution methods with smart manufacturing and learn how to apply these skills to your organization.
Data production visualization
Role of sensors
Actual and predicted dynamics
Machines & artificial vision
Advanced data analysis datasets
All the participants who successfully complete their program will receive an MIT Professional Education Certificate of Completion, as well as Continuing Education Units (CEUs)*.
To obtain CEUs, complete the accreditation confirmation, which is available at the end of the course. CEUs are calculated for each course based on the number of learning hours.
*The Continuing Education Unit (CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time they have devoted to a non-credit/non-degree professional development program.
To understand whether or not these CEUs may be applied toward professional certification, licensing requirements, or other required training or continuing education hours, please consult your training department or licensing authority directly.
Participants in our courses
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Module 1: Introduction to Smart Manufacturing and FrED
We will begin the course by identifying global trends disrupting our society and our products to understand how these trends impact the manufacturing process. Furthermore, we will introduce you to FrED as a prototype for potential innovations in the field of manufacturing.
Module 2: Analyzing Data: A Visualization Approach
In this module, we will explore the convergence of manufacturing and data science expertise in the field of smart manufacutring. We will also use use time-series analysis to understand FrED and investigate periodicity, and become familiar with different visualization tools.
Module 3: Modeling to Make Sense of Data
Here, we will build models to examine and improve FrED, explore how the length of a production run can affect results, and, lastly, examine and understand the data acquired from FrED.
Module 4: Sensors
In this module, we will review the integral role that sensors play in smart manufacturing, evaluating them and assessing the types of data that they produce. Furthermore, we will learn to discretize time and amplitude by sampling signals at equal time intervals and smoothing them.
Module 5: Control of Manufacturing Processes
We will explore manufacturing process control, the role of feedback, process modeling, and monitoring. Additionally, we will discuss actual versus predicted dynamics and address variation as it relates to a fundamental principle of manufacturing.
Module 6: Machine Vision
In this module, we will take test measurements using a camera and explore how machines use cameras and images to inform decisions and improve the manufacturing process.
Module 7: Applications of Machine
In this module, we will explore the applications of machine vision for video search, sports, and medicine. Also, we will discuss the applications of machine vision in additional contexts.
Module 8: Model Fitting and Sensitivity Analysis
Here, we will make the connection between machine vision as a tool and statistical process control to explore the process of discovering the best fit for a model.
Module 9: Statistical Process Control
In this module, we will apply statistical process control to a manufacturing setting and integrate deterministic and random variation.
Module 10: Advanced Data Analysis
In the last module, we will work with datasets derived from the manufacturing process, differentiate machine learning data methods, and create a digital twin for FrED to predict and improve its performance.
I encourage you all to obtain this certificate because it has supplied me with all the necessary tools to be a team leader in a company that aspired to take part in Digital Transformation. This certificate has drastically increased my understanding of technical language and the ability to serve as a bit of a translator for the rest of my organization... I greatly appreciated the program's facilitators. Thank you MIT Professional Education.
Marcos Agudo - Regional Manager, H&M