The Data Analysis Process

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From the course by University at Buffalo

Advanced Manufacturing Process Analysis

100 ratings

University at Buffalo

Advanced Manufacturing Process Analysis

100 ratings

Course 4 of 9 in the Specialization Digital Manufacturing & Design Technology

Variability is a fact of life in manufacturing environments, impacting product quality and yield. Through this course, students will learn why performing advanced analysis of manufacturing processes is integral for diagnosing and correcting operational flaws in order to improve yields and reduce costs. Gain insights into the best ways to collect, prepare and analyze data, as well as computational platforms that can be leveraged to collect and process data over sustained periods of time. Become better prepared to participate as a member of an advanced analysis team and share valuable inputs on effective implementation. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the fourth course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA

From the lesson

Introduction to Advanced Manufacturing Process Analysis

The purpose of this module is to introduce the concept of advanced analysis in improvement of manufacturing processes. Also, this module will help you to understand the difference between discrete manufacturing and continuous manufacturing.

The Data Analysis Process4:47

Meet the Instructors

Rahul Rai
Associate Professor
Mechanical and Aerospace Engineering

Everything we see around us,

if not designed by nature,

has been designed and manufactured by us.

Manufactured items are conceptualized using techniques of sketching and brainstorming.

Once fully developed, the concept is converted into CAD models or virtual design model.

This CAD models are very good way to validate complex ideas.

They capture form, fit,

and function of the product and give confidence that the idea is going to work.

The information that is stored in the CAD model

is eventually converted into engineering drawings,

then handed off to manufacturing department.

There, the CAD information is used to make a physical product.

Manufactured physical products are then used by the customers.

A disconnect currently exist between the digital model and the physical production.

Most of the information created at the digital CAD model

is lost during the physical fabrication phase.

Once the physical product is in use,

rarely the user's information comes back to make

changes to improve the digital design phase.

Things are changing. We're in phase when

the integration of digital and physical worlds have began.

This is a very exciting time.

Their connections are being enabled by placing sensors on products and

machine and connecting them to Internet and analyzing the resulting flow of data.

This convergence of sport machine,

machines with sensors, Internet,

and data is referred to as Internet of Things or IoTs.

Sensors on IoT platform generate data.

Expressed in different way,

the IoT sensors leave Digital Threads.

Analytics is also referred to as advanced analysis,

and is the computational process that stitches together the Digital Threads

created by Internet of Things to generate actionable intelligence.

The anti-braking system in cars give us a good illustration of how data can

inform a system to respond in a positive way to improve system performance.

Both the front wheels and the back wheels of the cars are equipped with speed sensors.

These sensors continuously provide speed data for the wheels.

Analytics in this setting would be looking at the continuous stream of data to infer when

the back wheel speed is different from

the front wheel speed to recognize a slip or skid condition.

The recognition of slip condition would prompt

the activation of the ABS system to prevent skid.

In manufacturing, the data generated from machines,

once it is analyzed,

can improve the efficiency of making things.

Consider this simple analogy.

When manufacturing a product,

three steps occur: raw materials are collected,

processed, and transformed into a physical product.

The transformation renders something physical we can use.

When advanced analysis is performed,

raw data is collected,

processed, and transformed into information.

The information renders an informed decision or action we can take.

Analytics connects the digital and physical worlds and test links

the product design phase to manufacturing product use and end of life phases.

The exciting benefits of analytics include better designs,

better machines, and enhanced manufactured.

In closing, a great example of the use of advanced analysis is predictive maintenance.

Predictions based on analytics can tell us when a machine needs maintenance.

This is known as predictive maintenance,

and plays a major role in improved asset utilization.

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