10th March 2018, Coimbatore
Sand Reclamation & Automation Implications & Applications for Foundry 4.0
Presented by Manish Kothari - Rhino Machines, Anand, Gujarat
Foundry 4.0 = SMART Foundry
The first thing to understand about Industry 4.0 is it is not one technology but a combination of modern technologies combined to create a ‘SMART factory’. by Mark Lewis at the World Foundry Congress in Nagoya, Japan in May 2016. Before we make the Foundry SMART…we must understand what goes into making a Casting – the business of the foundry itself, and then see how we can move to a SMART Foundry
12-Mar-18
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FOUNDRY SCHEMATIC PROCESS Energy
Environment
Productivity Sand Resin
Maintenance
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CORE
METAL
70%
Compressed Air 10%
5%
Handling
FETTLING
Lining
Inspection & Painting
Mould
Alloys
OEE Rejections
Lighting 5%
Gas Temperature Measurement
Process
Dispatc h
7%
Metallic
Shots Grinding Wheels
Manpower
Bentonite
Discard
Reclaim
Sand
Costs
Coal Dust Water
SAND
Waste
SMART Foundry – How?
The key elements required to make the Foundry SMART Process automation – most challenging Machine automation – most common, needs process integration Machine learning – very critical for foundry 4.0 Systems automation – Change to Demand based systems Upskilling Human Resources – key to success of Foundry 4.0 12-Mar-18
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Foundry 4.0 – Rhino & Ecolibrium interventions
Machine & Process Automation in Green Sand Process
Sand Handling System Moulding System
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Machine & Process Automation in Sand Reclamation Process
Green Sand Reclamation
Asset & Process Management with Industry IoT & Analytics
Melt Shop Optimisation
Core Sand Reclamation
Motor & Transformer Analytics Copyright - Rhino Machines Pvt Ltd
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Green Sand Process – Variables at Macro Level Sand Preparation
Moulding Machine
• Control and Consistency in Instantaneous properties • Logging of all inputs to the sand preparation e.g. return sand, new sand, bentonite, coal dust, water, temperature..etc
• Control of Mould Quality & Consistency • Recording of process parameters for each mould and casting • Monitoring and control of mould and casting rejection in realtime • Production capacity and availability
Return Sand System • Management of System sand properties • Control of Sand Temperature • Control of Silica Content, AFS, Clay, VM, LOI in sand, Grain Shape • Relation of Defect with Sand properties
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Waste Sand • Controlling Waste at Knock out, Shot Blast, Dust Collector, Screen, etc • Excess Sand disposal validation • Sand reclamation & recovery integration Copyright - Rhino Machines Pvt Ltd
Casting Data • Relation of Rejection with Mould • Validation and control of cooling time • Establishing relationship between sand, mould and casting rejection in realtime 6
Green Sand Plant – Machine to Cloud PLC
Sand Preparation
Mixer Motors
Water Addition Additives
Cooling Sand Controller 12-Mar-18
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Scope of Foundry 4.0 in Sand Preparation System Data Collected • Sand Temperature • Compactibility – set, measure and deviation • Water Addition • Cycle time • No of Batches • Additives addition • Return Sand & New Sand
Present usage of data • Graphical representation of data • Analysis on same time scale of different parameters • Average consumption, variation
Scope for Foundry 4.0
Data Acquisition, Repository, Analytics & Call to Action Process 12-Mar-18
Production
Asset Health
Consumptions
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• Machine learning to understand sand behavior based on material consumptions • Machine utilization, production efficiency • Relationship with Rejection (when connected on macro network) • Consumption Ratios w.r.t. casting production • Prediction & thereby control of process8 varation
Green Sand Moulding– Machine to Cloud PLC
High Pressure Moulding
Moulding Machine
Mould Handling
Process Pressure
Production 12-Mar-18 Data
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Data Acquisition & Data Analytics being done – User Interface under preparation • Moulding production data – hour, shift, day, week and any period • Energy consumption per mould, per kg casting • Casting moulding and production analysis • Mould rejection analysis • Asset maintenance – alarms, preventive maintenance, predictive maintenance analytics 9
IIOT in Green Sand Process – deliverables in Foundry 4.0
Green Sand Plant – Sand Preparation • With intelligence of software, and remote access of data in realtime, the supervisor does not have to go to the wired connection to check the information • Data is PUSHED, and has not to be pulled…saves time for finding the faults and deviations • Data is interfaced with Resource planning softwares – allows consumption monitoring in realtime instead of manual process • Production Shops comparative is created – sand produced v/s mould produced v/s casting poured brings out the efficiencies and losses at different shop interfaces – no more a blame game….purely data based systems 12-Mar-18
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Sand Reclamation – Data Analytics PLC
Thermal Reclamation Furnace
Gas, Water Consumption
Sand Input/Output
Temperature, pressure, Energy, Frequency 12-Mar-18
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Data Acquisition & Data Analytics being done – User Interface under preparation • Gas & Power Consumption per hour, per day, per MT of Sand output • Production Rate – per hour at all points • Temperature Data • Laboratory Results in real time • Asset Management – alarms, predictive & preventive maintenance, maintenance history 11
Rhino Sand Reclamation Project – Foundry 4.0 Relevance
Core Making
S1
• Control on Core Quality, Resin Consumption • Known processing cost, reduced variables in predicting costs
S2
S3
Moulding Sand Preparation
• No availability issues, reduced variances in process • Predictable process control in sand preparation process
Logistics & Work in Process
• Reduction in 60 to 80% of procurement with integrated sand reclamation plant • Reduced Storage, inventory management = better logistics and space management
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Core Sand Reclamation
Plant summary
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Area Occupied – 540 m2 : Storage – in Silos Floor losses & handling – nil Waste disposal – 60%-70% reduced • Processing 50 MT/day 12 • Process Cost – Rs 2.25/kg
IIOT in Sand Reclamation – deliverables in Foundry 4.0
Sand Reclamation • With PUSH Data systems – critical control of system w.r.t. temperature, consumptions of gas or energy, alarms does not need 24 x 7 vigilance or person. • Realtime production data possible, no manual checking of weights…combination of machine data acquisition, cloud and analytics make time available for productive work • Post production analysis helps evaluate the process from anywhere, provides process expert support at any time from anywhere…an advantage which can be leveraged with IIoT in creating stable & sustainable systems. 12-Mar-18
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Rhino’s Commitment Enabling Foundry 4.0
Reduce CO2 Emission 165 Mill Kgs/yr Save 31,000 MWh/yr
Reclaim Sand
Save 7000 MWh/yr
Multiflex Ecoflex
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From Darkness to Light
From Noise to Sound
From Waste to Money
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VET – Swiss Partner
Skilling India
From School to Skill
Data Analytics
From Approximate to Actual
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