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SMART FACTORY …...….Industry 4.0 series……….. CXO's Roundtable on Manish Kothari
‘Manufacturing Industry 4.0’
Managing Director – Rhino Machines Pvt Ltd CII Gujarat State Council & CII-MSME Panel Member
1730 - 2000 Hrs | Friday, 24 August 2018 | CII House, Ahmedabad
Making our Manufacturing Industry “Future Ready”
Mapping the Journey to Smart Factory
Smart Factory 4.0 IOT Technology
Culture • Shop Floor Practices • Lean Manufacturing • Documenting Processes
• Digitization of Process, Production & Documentation • Connectable Devices & Machines
• Connecting Machines • Connecting People
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• Analytics • Actionable Insights • Automation of Insights to physical actions
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SMART Industry– How?
The key elements required to make the Factory SMART Process automation – most challenging Machine automation – most common, needs process integration Machine learning – very critical for manufacturing 4.0 Systems integration – Bringing all Digital Devices to one platform Upskilling Human Resources – key to success Copyright - Rhino Machines Pvt Ltd Approach to Smart Factory
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Manufacturing 4.0 – Typical Case Study of Foundry Industry Energy
Environment
Productivity Sand Resin
Maintenance
CORE
METAL
70%
Compressed Air 10%
5%
Handling
FETTLING
Lining
Inspection & Painting
Mould
Alloys
OEE Rejections
Lighting 5%
Gas Temperature Measurement
Process
Dispatch
7%
Metallic
Shots Grinding Wheels
Manpower
Bentonite
Discard
Reclaim
Sand
Costs Approach to Smart Foundry
Coal Dust Water
SAND
Waste
Melt shop Overview Metal Tapped Weight Melting Energy, P.F., …all energy parameters
KPI’s/Objectives
Pouring temperature Furnace kW/kWh kWh/Ton Spectro / CEV Meter
Water Consumption Water Pump Energy
Hrs/Heat Consumptions/Heat
Scrap/Metallics
Deviations= Issues/Alerts
Spectro / CEV Meter Melting Heat Time/Tapping Time / Holding Time
Implemented at: • Sky Technocast – Ahmedabad • Shree Jayaram Foundry - Coimbatore
Green Sand Plant – Machine to Cloud PLC
Sand Preparation
Mixer Motors
Water Addition Additives
Cooling Sand Controller
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Steps to get on IOT Platform – Points to Ponder
Data Analytics Data Interpretation Data Gateway to Cloud Data Mining The source • Determining the parameters influencing the process • Determining the data source - PLC, Sensors, Spreadsheet, Tables,…
• Determining the deployment of IOT for data acquisition • Determining the grain size, data formats for uploading data
• Defining the relationship of the data with the process • Defining the interrelationship of different data
• Algorithms to connect the data collected • Algorithms to find trends
Moving Forward to Implement Factory 4.0 Proactive – Predictive Analysis Building the User Interface
Insights into Data • Determining types of insights to be developed • Determining how the data will be displayed in real time • Creating visualisation graphics for insights • Predictive insights - algorithms and development
• Determine Alerts & Updates for user • Create summary report formats • Create Decision making reports & alerts • Create dashboards - flexible and user dependent
• Create Machine Learning & AI Insights for improvements • Resource efficiency & utilization analytics • Integrate with manufacturing analytics • Enable forecasting analytics for manufacturing & process
The Partners in Smart Factory – Leveraging Expertise of Each to Grow Together Rhino Machines – Foundry Projects & Machines
www.acefound.org
ACE Foundation – Creating the Culture Change
Industry Experts onboarding as we progress www.rhinomachines.net
Foundry Process & Operation Experts
IOT Foundry 4.0
www.rhinomachines.net
Ecolibrium Energy – IOT Enabler
Composite Solutions – Business Analytics
www.ecolibriumenergy.com
www.cspl.net.in
Conclusion The Smart Factory: Shift from reactive to predictive management Enabled by IoT
Thank you For any queries please contact Manish Kothari manish@rhinomachine.com +91 9227124977 25-08-2018
Fix after failure
Eliminate Defects At Early Stage
Scheduled Checking
Use Analytics To predict & prevent failure 10