industry 4.0 case studies in operations - foundry conclave · 2018-12-04 · cmb cmh 2017 17...

© ZF Friedrichshafen AGInternal

Industry 4.0Case Studies in OperationsZFWPC

P.K.Sarkar – Operations Head


Agenda

1.

2.

3.

4. Conclusion

Digitalization Strategy ZFWPC

Introduction to ZFWPC

Case Studies

© ZF Friedrichshafen AGInternalZFWPC – CII Presentation 16th Nov 2018

Overview ZF Wind Power Coimbatore

ZFWPC - Established in 2006

Sales for 2017C € 139 million

Employees (end of the year) 839

Investments in property, plant and equipment

€ 200 million

Two Business UnitsWind Power Technology (IW)

Off-Highway Systems (IA)

ISO 14001 : 2004

January 2016

ISO 9001 : 2008

April 2016

OHSAS 18001 : 2007

February 2016

Certifications:


ZF Wind Power Coimbatore - State of the Art Facility

ZFWPC – CII Presentation 16th Nov 2018

IN-HOUSE MANUFACTURING FACILITY :

- GEAR HOBBING AND GRINDING

- HORIZONTAL AND VERTICAL MACHINING

CENTERS

- HEAT TREATMENT – CARBURIZING,

NITRIDING FACILITIES

QUALITY ASSURANCE :

- 3 COORDINATE MEASURING MACHINES

- 3 KLINGENBERG GEAR TESTERS

- NON DESTRUCTIVE TESTING – UT & MPI,

NITAL

- SPECTROSCOPE AND MICRO HARDNESS

TESTING EQUIPMENT

- CHEMICAL AND MATERIALS LABORATORY

ASSEMBLY FACILITY :

- 6 ASSEMBLY STATIONS

- 3 TEST RIGS

- PAINTING


Agenda

1.

2.

3.

4. Conclusion



Case Studies


Measurement

Monitoring

Control

Improve

Communicate

Training

Digital Online Monitoring

Digitalization Strategy



Direct Factors Impacting Operations



Agenda

1.

2.

3.

4 Conclusion



Case Studies


Digital Autonomous Maintenance System

1.Open the AppEmp ID-EXXXX

2.Select the machine 3.Select OK/Not Ok

4.Camera Open for abnormality capture

5.Report Review

6.Mail send

If everything OK Steps 1,2,3,5,6

If something Not OK Steps 1,2,3,4,5,6

Digital Image will be attached send as report in Excel format

Objective To improve the machine Availability / Reliability

Project

Results

Paper less Environment, Easy data tracking and system Adherence. Online closure of abnormalities and corrective

actions



Digital Training

Trainee On Job WorkingSimulator training to understand the process

Using UNITY Software

Objecti

ve and

Results

To Improve the effectiveness of the training with interactive method,Results – Training time reduced from 14days to 3days

Evaluation time reduced from 120 min to 20 min

Improved 3D visualization

Improved

written stateme

nts

Adding audio effects

User interacti

ve controls

Manual Work Instruction

Video Work Instruction

Interactive Digital Training

Module

Lesser time Efficient

More time Less Efficient

Lesser time More

Efficient

Time

Eff

ect

iveness

& E

mplo

yee

Satisf

act

ion



Machine Energy Consumption Monitoring

Energy data Capturing from each machine Machine status data Capturing from each machine

During ProductionDuring non Production – Machine power only ON

Objective To reduce machine power consumption

Project

ResultsImprovement in value add and machine power consumption reduction

KW

VA



Building Management SystemMonitoring the Shop floor Temperature and Maintaining 27*C

Monitoring the Performance of the Chiller units

Objective To Monitor and maintaining of temperature in the shop floor without variation in temperature

Project

Results

Set temperature is ensured across the shop floor,

Easy to identify abnormalities and monitor the temperature across shop floor



Machine Data AcQuisition Flow

Siemens / FannucCNC system

MPIEthernet

Data stored inCommon File Server in TSV

MPI

% Load

Temp

Ethernet

16 bit Word

32 bit Double word

Ethernet

Field devices Input and output status



Machine condition monitoring

Objective To improve the machine reliability through online monitoring of machine load and temperature of each axis

Project

Results

Real time monitoring of machine load and temperature data’s etc, Data’s recorded and stores to analyse…

Trending of data and limits are set to identify the abnormalities before failure to eliminate machine down time

Machine Elements Monitoring



Online monitoring of Warehouse E-cars

Objective To improve the warehouse reliability through online monitoring of ecar drive parameters

Project

Results

Real time monitoring of drive load current and temperature datas etc, Datas recorded and stores to analyse…

Trending of data and limits are set to identify the abnormalities before failure to eliminate e-car down time



DATA Capturing

Descriptive analysis

Predictive analysisRules algorithm

Predictive Notifications share from Web app

Big data Analysis using Python

Project ResultsThe abnormalities are classified as High severity requiring immediate actions and Low severity to be planned as a PM. Whereveractions are required the same is taken in a planned manner so that the unplanned stoppages are avoided. This tool is helping thePEM team to improve availability as well as reduce cost by optimization of maintenance.



Online Monitoring of machine Performance

Machine Spindle runtime data capturing through Online monitoring

Machine utilization Visualized & Help to improve VA by counter action against NVA

Project ResultsThis enabled a paradigm shift in capturing real machine ( spindle) utilization and improve it continually by identifying and eliminating wastes in

the system. These Identified Improvement projects result OEE improvement in machining cells across ZFWPC ranges from 5% to 10% .

Nov2017

CMMH002 - Heckert Master M/C

OEE – 66% OEE – 79%

Aug2018

1

4 3

2



DIGIBORE (ACDBS)

Need

We rely on skilled CNC operators using graduated boring tools to adjust boring bars,

risking accuracy. CNC machines “remains unproductive”

Solution

ACDBS makes machine controllers to give wireless signals to boring systems, which adjust

automatically “without manual intervention” and “without stopping manufacturing”.

This is an initiative under – Digital Manufacturing Process – Industry 4.0



Assembly ANDON System


Atlas1.2-50Hz CM0166 3.35

Total 8 8 2.5 Hrs 2.5 Hrs 2.5 Hrs

Day Plan Actual S1 in % S2 in % S3 in % Per Day in % Date Unique ModelNumber CMNumber Station 1A Station 1B Station 1C

27-Sep-18 8 8 59.38 104.46 101.33 88.34 27-Sep-18 EH811CM0152 EH811 CM0152

27-Sep-18 Atlas1.2-50HzCM0041 Atlas1.2-50Hz CM0041 2.22 2.15 2.22

28-Sep-18 Atlas1.2-60HzCM0042 Atlas1.2-60Hz CM0042 2.42 2.42


28-Sep-18 EH811CM0153 EH811 CM0153

27-Sep-18 EH811CM0151 EH811 CM0151


27-Sep-18 EH811CM0150 EH811 CM0150

27-Sep-18 Atlas1.2-60HzCM0029 Atlas1.2-60Hz CM0029 2.54

27-Sep-18 EH862-50HzCM0371 EH862-50Hz CM0371

Target TaktTimePPP

Overall

Multiskill Training(One

model to another model), 0.28

Single Man Power, 0.51

Three Man Power Working,

0.42

No Plan, 0.05Support for Other

Cell / department, 0.06

Downtime Reason Distribution

Multiskill Training(One model to anothermodel)

Single Man Power

Three Man Power Working

No Plan

Support for Other Cell / department

88.34

8.483.18

PPP Distribution

PE3 Per Day in %

Planned Loss in %

Loss Hours in %

Objective To online monitoring of assembly output as per the takt to achieve the productivity target/KPI

Project

Results

Losses are identified in real time and it is captured to trigger the corrective and preventive actions

It improves the productivity and reduction in losses in the assembly process

ANDON Dashboard Loss data capturing Performance Monitoring


DIGIQUAD – ONLINE DIGITAL QUALITY CONTROL


CENTRALIZED SYSTEM FOR DATA CAPTURING

PAPERLESS PRODUCTION PROCESS FLOW INCREASED QUALITY AND RELIABILITY OF DATA A BETTER OPERATOR QUALITY – AWARENESS AUTOMATIC MONITORING & EFFECTIVE CONTROL OF CTQ’s PROCESS IN CONTROL & IMPROVES PRODUCTION PROCESS EFFIENCY LOWERED ANNUAL QUALITY COST


Other Important Parameters being monitored digitally


BLDC Online Monitoring HT Substation Online Monitoring

HT Substation Online Monitoring

RO Plant Online Monitoring

HT Equipments Online Monitoring (EOT, Ammonia, Endo-gasFire Alarm Online Monitoring


Digital projects covering different areas


Project Description

Origin Area Year No

CMB PEM-F 2016 1 EMS management

CMB PEM-MM 2017 1 DOMORE… Machine Condition

CMB PEM-MM 2017 1 FANUC machine monitoring

CMB PEM-MM 2017 1 counter & Hrs of running for all machines

CMB OPS 2017 2 DAMS app

CMB OPS 2017 2 EOT crane app

CMB CMH 2017 3 Machine Performance Monitoring

CMB OPS 2017 4 5S paperless work

CMB PEM-ATP 2017 5 Warehouse online monitoring

CMB PEM-ATP 2017 5 Warehouse online monitoring client

CMB PEM-HT 2017 6 Endogas Visualization

CMB PEM-F 2017 7 BMS server

CMB PEM-F 2017 7 BMS client

CMB ATP 2017 8 Washing and kitting Part picking app

CMB ATP 2017 9 Atlas Housing inspection app

CMB QES 2017 10 Visual inspection app

CMB ATP 2017 11 Testrig cycle SMS

CMB ATP 2017 12 Shrink test app

CMB ATP 2017 13 CARDON app

CMB CMH 2017 14 DIGI BORE + Adaptive Control

CMB CMH 2017 15DIGI QUAD

CMB ATP 2017 16 ANDON- system ATP (RFID)

CMB CMH 2017 17 Performance loss visualization

CMB CMH 2017 17 Action plan report generation

CMB CMH 2017 17 Shift details loss monitoring

CMB CMH 2017 17 Multiple loss entry

CMB CMH 2017 17 Component and model details capturing

CMB PEM-HT 2017 18 EOT crane moitoring

CMB PEM-HT 2017 19 Ammonia Gas monitoring

CMB PEM-F 2018 20 Substation app

CMB PEM-F 2018 21 BLDC

CMB PEM-MM 2018 22 Breakdown SMS details

CMB PEM-MM 2018 23 Conveyor Monitoring

CMB OPS 2018 24 Power Bi dashboard for loss

CMB OPS 2018 24 Power BI dashboard for DAMS

CMB MANG 2018 25 Kaizen

CMB PEM/EHS 2018 26 Loto register

CMB OPS 2018 27 Migration of DOMORE data to SQL

CMB ATP 2018 28 Interactive training in assembly process

CMB PEM-MM 2018 29 Geometry Measurement

CMB PEM-F 2018 30 Lifting tool and tackels

CMB PEM-F 2018 31 ETP/ STP monitoring

CMB ATP 2018 32 CAA (Computer Aide Assembly) {under

CMB CMH 2018 33 Implementation of RFID in tooling

CMB ATP 2018 34 Big washning machine monitoring

CMB HT 2018 35 Digi WH stock Project

CMB PEM-MM 2018 36 M/C analysis using Python

CMB PEM-F 2018 37 RO Plant online status monitoring

CMB PEM-F 2018 38 Pump house online status monitoring

CMB PEM-F 2018 39 Machine Energy monitoring

CMB PEM-F 2018 40 CMM,ATP,Office Temp monitoring/ ATP AHU

CMB PEM-F 2018 41 All BLDC Ventillators status monitoring

CMB OPS 2018 42 DAMS V0.2

CMB OPS 2018 43 5S paperless work v0.2

CMB ATP 2018 44 Pallete weight and height details

CMB PEM-F 2018 45 Fire alarm

PAS OPS 2018 46 MDM Project Phase 1

CMB PEM 2018 47 Heckert counter analysis

CMB PEM-QES 2018 48 Grinding burn analysis

CMB HRM 2018 49 Idea submission-Employees

CMB SCM 2018 50 GMR,CAPEX,SCRAP - Approval online

CMB HRM 2018 51 Annual day registration form

Project NO


Conclusions


• Digital Solutions are effective to improve operations efficiencies as well as reduce

workload and augment operators output.

• The success of a smart factory is to inculcate a digital culture through a pull system

rather than a conventional push system

• There are several ( too many ) digital solutions available in the market – it needs some

competence to adapt the system which suits the technical requirement , budget and

culture of the organization

• If implemented correctly it can exceed the expected results as there is a cascading

effect of the improvements .

• Caveat: Digitalization or industry 4.0 is not going to solve all the problems of an

industry , it will augment ,complement and supplement the competence of the available

resource. At the end it is the people who would need to use the tools correctly to meet

the end objectives ( It is like a hospital equipped with the best technology – finally it is

the doctor who cures the patient)


Thank you

industry 4.0 case studies in operations - foundry conclave · 2018-12-04 · cmb cmh 2017 17...

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