How can IIoT work and be implemented for medium-sized

How can IIoT work and be implemented for medium-sized companies in the world of production?

SEBASTIAN BARDY | PTW TU DARMSTADT

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Agenda

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1. PTW / CiP Introduction

2. Challenge

3. Use Case 1: Smart Maintenance

4. Use Case 2: Digital Shopfloormanagement

5. Summary

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PTW TU Darmstadt

Institute of Production Management, Technology and Machine Tools

1300 Hearers per year

13 Courses/Lectures

102 Employees

50 Running research projects and

industrial projects

Pro

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PTW Research Groups

Main Fields of Application:

• Mechanical Engineering

• Automotive

• Aerospace

• Dental Technology

Machine Tools and

Components

Machining technology

Additive Manufacturing

and Dental Technology

Energy Technologies and

Applications in Production

Management of Industrial

Production

Center for Industrial

Productivity

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Process Learning Factory CiP(Center for industrial Productivity)

• Lean production and information technology

• Competence development for lean production

• Flexible parts production

• Flexible production and intralogistic systems

• Lean quality

Speaker:

Sebastian Bardy

Research Assistant

Realistically Equipped Production Environment

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Challenge / Scope

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assisted operators

Intelligent products

intelligent machines

The human being is put

in the center of the

considerations and the

systems provide him

with the right information

for the best possible

actions

OPTION 2

Increasingly stronger

automation of machines

and processes up to self-

optimization.

OPTION 1

Use Case 1: Smart Maintenance

Goal of the Project:

• Predictive Maintenance on a saw blade

Implementation of a condition monitoring through sensors

Data mining with machine learning algorithms to get a prediction for failure times

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New saw blade Worn-out saw blade


Operating time

Operating time

Operating time

Reactive maintenance – Replacement after failure

Preventive maintenance – Replacement by schedule

Condition-based maintenance – Replacement by condition

Wear

Wear

Wear

Reactive maintenance:

• Maintenance after machine failure

• Best possible utilization of the "life" of the machine

• Not plannable and disruptive for the production process

Preventive maintenance:

• Maintenance at fixed intervals, can be planned and before machine failure

• Time based on mean time to failure (e.g. MTTF)

• True machine condition remains unnoticed: Exchange often not necessary

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Operating time

Operating time

Operating time

Reactive maintenance – Replacement after failure

Preventive maintenance – Replacement by schedule

Condition-based maintenance – Replacement by condition

Wear

Wear

Wear

Reactive maintenance:

• Maintenance after machine failure

• Best possible utilization of the "life" of the machine

• Not plannable and disruptive for the production process

Preventive maintenance:

• Maintenance at fixed intervals, can be planned and before machine failure

• Time based on mean time to failure (e.g. MTTF)

• True machine condition remains unnoticed: Exchange often not necessary

Condition-based „predictive“ maintenance :

• Monitoring of the machine status indicates errors and impending failures

• Time of maintenance flexible and condition-dependent

• Best possible utilization of the "machine life" considering timely maintenance

before machine failure

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Messwerte mehrerer Messreihen (1 Messpunkt alle 0,5s)

Finding:

Characteristic values for the

experiments

Problem:

„Data contamination“

Problem:

„Outliers“

Finding:

Mean values change with

increased use (wear)

6 Measurement series, 6 conditions of wear

Measured values of several measurement series (1 measuring point every 0,5s)

Po

we

r

Number of measuring points


blade i. O.

blade

n. i. O.

Evaluation trough:

• Technician

• Cut quality

• Cut duration

Quantity of cuts

Training data: Power

Po

we

r [W

]

Characteristic measured values with corresponding relation of condition

get gathered and get fed into the articial neural network as training data

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The response of the artificial neural network gets examined with the

help of a measurement series that is unknown to the network

5% 20% 30% 70% 85% 100%

Finding:

Wear gets captured on a

microscopic level – barely

change of signals while little

wear

Finding:

Distinct difference between the

signals with growing wear

Test data

Test data: Power

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Use Case 2: Digital Shopfloormanagement

Goal of the project:

• Empowering employees in a digital environment

• Centralizing the data in a factory and preparate/process them for further investigation

P U B L I C15 15Von analogem zu digitalem Shopfloor Management

SFM supports the role of people in a smart factory

KPIs are automatically created out of the process

data

Process data Performance data

1. Recognize deviations with the help of real-time performance and

understand them through the enrichment with process data

2. Support of the systematic solving of problems with the help of digital

assistance with contextual examples, notes, data, etc.

DMG Mori

16


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ERP

MES

Sensors

Data

Production planning

& -control

Internal

connection of data

Information

Data base

Evaluation &

Processing

Knowledge

SFM software


Targeting KPIs:

• AXXOS OEE (Optiware)

• DELMIA Apriso (Dassault Systemes)

• Legato Sapient (Gefasoft)

• Nexeed Production Performance Manager

(Bosch)

• Visual Shop Floor (Solunio)

Shopfloor Management Solutions

• Team Board (SFM Systems)

• ActiveCockpit (Bosch Rexroth)

• Digital Shopfloor Management (New

Solutions)

• Forcam Force (Forcam)

• HeyDo! (HeyDo Apps)

• Digital Lean Board (Mevisio)

• ValueStreamer (Staufen)

Summary

• Actually USING the data created on the shopfloor:

˗ Using machine learning to create value out of the data collected

˗ Empowering employees to create value out of the data collected

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• Use Case 1: Smart Maintenance

• Use Case 2: Digital Shopfloormanagement

• IIoT Solutions for small and medium sizedcompanies

• Using generated data on the shopfloor

Challenge Solution

Benefit

Do you have a question for the

Presenter? Visit the Guest

Speakers

Virtual booth within the next

hour

for an interactive Q&A session.

Documents

How can IIoT work and be implemented for medium-sized