Manufacturers are increasingly utilizing machine tools that are self-aware – they perceive their own states and the state of the surrounding environment – and are able to make decisions related to machine activity processes. This is called intelligent machining, and through this course students will receive a primer on its background, tools and related terminology. Learn how the integration of smart sensors and controls are helping to improve productivity. You’ll be exposed to various sensors and sensing techniques, process control strategies, and open architecture systems that can be leveraged to enable intelligent machining. This course will prepare you to contribute to the implementation of intelligent machining projects. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the fifth 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
The Evolution of Intelligent Machining
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来自 University at Buffalo 的课程
Intelligent Machining
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从本节课中
Introduction to Intelligent Machining
The purpose of this module is to introduce the concepts related to intelligent machining paradigm. The key focus will be discussing two key components of intelligent machining, i.e., sensing and control.
与讲师见面
Rahul RaiAssociate Professor
Mechanical and Aerospace Engineering
[MUSIC]
Welcome to lesson two.
The evolution of intelligent machining can be group into three major phases.
Phase 1, from the year of 1700- 1900.
During this phase, hand tools were transformed into mechanical tools.
This transformation was enabled by using mechanical principles, and
the machining was performed in much more efficient way.
Phase 2, from the 1900- 2000.
Machine tools based on mechanical principles are transformed
to digitally controlled machine tools.
Constrained conditions and
performance functions were given to machine to get desired output.
This allows for multifunctions and feedback of cutting processes.
This was the era of CNC machine.
Phase 3, from the year 2000- present.
The transformation of newly controlled CNC machines
with adaptive controls to intelligent controls.
In this phase,
the sensors are used to receive automatic feedback from the machine tools.
And use machine learning algorithms allows for optimizing future operations.
This leads to increased productivity and automatic decision making.
Let's move to the main focus of this module that is to understand what is
intelligent machining?
And why we need it?
To understand the concept of intelligent machining,
let us consider a simple example of car.
After running a car for certain period of time, or precisely after certain number of
miles, the car tire wear out and needs to be replaced.
If not replaced at an appropriate time, the performance of car can show
significant degradation, especially in harsh climates such as snow, or if
the wear is too high, there are chances of a flat tire and possible accidents, too.
One of the main factors in tire wear is driving a car when the tire pressure
is low.
Modern day cars are equipped with tire pressure sensors that alert a driver when
the car tire pressure is low, so that the driver can inflate the car's tire.
Some simple sensors can provide
intelligent information that can increase the life of car tire.
In fact, there are many sensors that are present in modern day
cars that continuously monitor the health of different car systems such as tires,
engines, and brakes that form the backbone of
Integrated Vehicle Help Management systems, or IVHM.
IVHM provides unified capability to assist the current state of the vehicle's health,
and help in improving the performance of a vehicle.
Coming back to machining processes similar to car tire, the machine tools also work.
The health of machining centers like [INAUDIBLE] with time and a user.
Degradation of machining system health can result in decrease of process efficiency,
poor quality product, and tool breakage.
So it is really important to monitor the health of machine, tools, and
workplace in an integrated fashion.
While integrated health management of machining processes can be
performed manually, a manual process will have high chance of error as
the information gleaned from a manual process may not be accurate.
The disadvantage of a manual process can be overcome by using sensors to monitor
the help of machining processes.
Help monitoring through the use of sensors has the following advantage, high
productivity, because there is no waste of time for inspecting tools and machine.
Better quality of product.
Feedback from the data collected by sensors can be also
used to make process improvements.
Data gathered by the sensors can also be analyzed to make smarter decisions.
And finally, data and information can be shared among different stakeholders
across the supply chain to improve the overall organizations process efficiency.
Now that we have explained some factors driving the need for
intelligent machining or IM, let us define IM.
Intelligent Machining is an approach to machining
that uses sensors in machining processes to achieve higher quality,
productivity, and enabling smarter decisions.
Manufactures are increasingly turning to intelligent machines in paradigm
to meet the growing demands of increased product quality,
greater product variability, and shorter product life cycle, and reduced cost.
Intelligence of a system can be perceived as the capability of the system to achieve
a goal or to sustain desired behavior in business of uncertain conditions.
In context of manufacturing, intelligent machining is a paradigm
in which machine tools can perceive their own states and
the state of the environment in which they are operating, and can execute start,
control, and terminate machine activities.
In other words, intelligent machines are self-aware and
can make decisions related to manufacturing processes.
The intelligent machining paradigm is enabled to the integration of
smart sensors and controls.
