This course will expose you to the transformation taking place, throughout the world, in the way that products are being designed and manufactured. The transformation is happening through digital manufacturing and design (DM&D) – a shift from paper-based processes to digital processes in the manufacturing industry. By the end of this course, you’ll understand what DMD is and how it is impacting careers, practices and processes in companies both large and small. You will gain an understanding of and appreciation for the role that technology is playing in this transition. The technology we use every day – whether it is communicating with friends and family, purchasing products or streaming entertainment – can benefit design and manufacturing, making companies and workers more competitive, agile and productive. Discover how this new approach to making products makes companies more responsive, and employees more involved and engaged, as new career paths in advanced manufacturing evolve. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the first 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
Gaining Insight from Data
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來自 University at Buffalo 的課程
Digital Manufacturing & Design
334 個評分
從本節課中
Components of the Paradigm
The purpose of this module is to introduce learners to the multiple components that integrate to create a future manufacturing enterprise (i.e., a digital link between design and production, leveraging data analytics to identify opportunities for increased quality and efficiency, interconnected and transparent machines, production facilities, and supply chains). Details of individual lessons in this module are provided below.
與講師見面
Ken EnglishDeputy Director
Sustainable Manufacturing and Advanced Robotic Technologies Community of Excellence
[MUSIC]
In this second lesson of the module,
we will be exploring a high level understanding of advanced analysis,
a key component of the digital manufacturing and design paradigm.
Upon completion of this lesson you'll be able to
state a generic definition of basis data analysis.
Explain with at least one example, the need for
advanced analysis techniques in the advanced manufacturing enterprise.
Jeff Immelt, chairman and CEO, of General Electric has made this comment.
If you went to bed last night as an industrial company,
you're going to wake up today as a software and analytics company.
The change is happening in front of us.
This observation is a clear call to action to transform manufacturing to a new state,
that uses the data generated in the digital manufacturing process,
to more efficiently produce higher quality products.
Before discussing an analysis we need to establish definitions.
First is data.
Data is unprocessed raw input.
Some example of data are, in a class, each student's individual test score.
A measured dimension that results from an inspection process like
the diameter of a whole on a manufactured part.
Information is data that has been processed to provide additional insight.
Instead of a single student's grade, the average score of a class or
of the entire school, is information that can be derived from the given data.
In manufacturing, tracking how a dimension changes across multiple products,
like if a diameter of a whole is consistently getting smaller,
represents usable information.
Data analysis is the act of transforming data into usable information.
This information can be grouped together to establish long term
findings that represent knowledge.
This knowledge becomes the basis for taking action, like replacing or
sharpening a cutting head if dimensions are tracking
in a consistent direction that indicates wear.
Data, by itself, is just a number and is not very useful.
Data becomes powerful once it is processed and put into context.
Like the saying goes, data rich, information poor.
We can recall some simple data analysis methods from statistics.
The mean is what typically comes to mind when you're asked to find the average of
a set of data, and is found by adding up all of the data points and
dividing by the number of data items.
Median and mode, are two other approaches to developing a average for
a data set, representing the middle value and the most commonly found value.
While the range of a data set is the difference between the highest and
lowest value.
These statistics, along with slightly more advanced information,
can measure how much variance or deviation is present in a data set.
When coupled with knowledge about a manufacturing process, these simple
pieces of information can guide a process improvement strategy like Six Sigma.
Another example of how data and
information drive action is using frequency, or how many times a particular
value occurs in a data set, to focus on a high frequency occurrence first.
With the success of Six Sigma and other process control strategies,
a natural question may be, why would we need advanced analysis?
The short answer is that the complexity of analysis required
typically follows the complexity of the data or the product.
So simple data analysis methods can be used for simple data analysis.
But as we look to increasingly complex problems, like the supply chain for
a complex product,
we need more sophisticated approaches to generate actionable information.
Technologies that have resulted in significant improvements in advanced
analysis are, high performance computing and big data.
High performance computing, also known as HPC, can enable advanced analysis.
Sometimes, the analysis techniques required to process data,
require computational power far beyond what our desktop computers are capable of.
In such cases, we use high performance computing to analyze data, and
convert data into information quickly, efficiently and with high reliability.
Big data refers to the overwhelming amount of digital data,
over 2.5 quintillion bytes of data every day that comes from everywhere.
Digital content like pictures and videos, posts to social media,
product design requirements, supply chain and purchase transaction records, as well
as sensors in manufacturing equipment and products operating in the field.
Big data spans three main dimensions, volume, velocity and variety.
Volume refers to the constantly increasing amount of data.
With companies accumulating terabytes of raw data and
information as part of the design and manufacturing process.
Velocity captures the concept of time sensitive data.
Using production sensor data to predict that a piece of manufacturing equipment
needs serviced can prevent scrap, rework or
even down time that can result from an unexpected failure.
Variety recognizes that there are many sources of data.
Some, well structured and annotated,
others that may require processing to become meaningful sources of information.
An example of variety would be sensor data from production machines being coupled
with video feeds from production lines.
Advanced analysis offers opportunities to find insights and opportunities for
improvement in manufacturing like, models could be developed for
product designs and definitions.
Rather that diagnosing a failure, prognostics or predictive analysis,
could forecast when a piece of production equipment will go out of tolerance.
Information about a jet engine could be used to predict it's performance,
and when coupled with operational data, be used to generate a customized preventative
maintenance schedule while in service.
Advance analysis also enables design for x.
Where the x stands for manufacturing, safety, reliability,
performance, quality, etc.
This lesson is intended as a basic introduction to the topic.
To learn more, additional resources have been made available on the course website.
Dr. Rowell Rye, an expert in this topic, walks through a more detailed discussion
in course four of the Digital Manufacturing and
Design Specialization Advanced Analysis.
