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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


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Productivity Sand Resin






Compressed Air 10%





Inspection & Painting



OEE Rejections

Lighting 5%

Gas Temperature Measurement


Dispatc h



Shots Grinding Wheels







Coal Dust Water



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


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


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


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


Asset Health


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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


• Control on Core Quality, Resin Consumption • Known processing cost, reduced variables in predicting costs



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


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


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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