Data Collection in Different Manufacturing Settings

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Из курса от партнера University at Buffalo

Advanced Manufacturing Process Analysis

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University at Buffalo

Advanced Manufacturing Process Analysis

163 оценки

Курс 4 из 9 — 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

Из урока

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.

Data Collection in Different Manufacturing Settings4:13

DMD Dialogue 1: Discrete Part Manufacturing7:32

DMD Dialogue 2: Continuous Manufacturing7:59

Познакомьтесь с преподавателями

Rahul Rai
Associate Professor
Mechanical and Aerospace Engineering

There are a couple of points that I want to mention,

one, that data is all around you and two,

that it can be transformed into information to make improvements.

Two different types of manufacturing requires

different types of data collection and analysis.

Discrete manufacturing refers to

a manufacturing process in which the output is an individual unit.

An example of such a manufacturing process will be manufacturing office crew or gear.

In contrast to discrete manufacturing,

we have continuous manufacturing.

In continuous manufacturing, the output typically flows

continuously and cannot be counted as a single unit.

The output is typically measured in weight, volumes and percentages.

Some examples are production of petrochemicals, beers and fertilizers.

Data analysis regardless of whether or not,

it takes place in discrete setting or continuous setting occurs in four stages.

Stage one is data collection,

where you collect the data from different sensors.

Stage two is data storage.

Stage three is data preprocessing,

where you define the data so that it can be processed further.

Stage four is data analysis,

in this stage preprocessed data is converted into actionable intelligence.

This example shows data about a manufacturing company with three product lines.

It lists down the performance of this product lines in quarter one, two and three.

Look at this historic data,

speculate about what the performance of

the product lines will be in upcoming fourth quarter.

Which product lines should be focused on as Delta Inc prepares for upcoming quarter.

This example does not involve a lot of data,

so manual calculation or Excel sheet can work to analyze the data.

Advanced manufacturing enterprise relies on large amounts of data in different formats.

All of this data needs to be converted into

actionable information very quickly, efficiently and accurately.

In digital manufacturing and design,

multiple data analysis often in parallel is needed to be performed.

In order to accomplish this,

you will need advanced computational platforms that are integrated into hardware

and software systems designed specifically to handle significantly large amount of data.

In case you plan to develop your own analytics engine,

consider these following questions: is historic data available to you?

How can you capture the data?

What sensors will be placed and where?

What will be the data collection infrastructure?

The key thing to remember is that data has

a limited use if it is not converted into actionable information.

You can be data rich and information poor.

As a final note,

the analytics process is not implemented in isolation.

A cross-functional team and organizational structure is needed.

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