Industry 4.0 enabling Lean at Bharat Forge. ARC Forum MAKING IN INDIA FOR THE WORLD. 7 July, 2017

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1 Industry 4.0 enabling Lean at Bharat Forge MAKING IN INDIA FOR THE WORLD ARC Forum 7 July, 2017 K Chethan, Director Quality Praful Kushwaha, Senior Manager ITS

2 Kalyani Group Overview Group turnover over 2.5 Billion USD 15+ Operating companies ENGINEERING STEEL 10,000+ global workforce CORE MANUFACTURING Ranked among Super 50 Indian Companies by Forbes, 2016 Mundhwa Plant, World's largest single location forging facility DEFENCE SYSTEMS Preferred Technology & Engineering Driven Development Partner Has largest repository of metallurgical knowledge AUTO SYSTEMS INFRASTRUCTURE

3 Kalyani Group Overview ENGINEERING STEEL CORE MANUFACTURING DEFENCE SYSTEMS AUTO SYSTEMS INFRASTRUCTURE INDIA Kalyani Steels Ltd. Kalyani Carpenter Special Steels Bharat Forge Ltd. Kalyani Technoforge Ltd. Kalyani Strategic Systems Ltd. BF Elbit Advance Systems Ltd. Analogic Controls Ltd. BF Premier Energy Systems Kalyani Rafael Advance Systems Automotive Axles Ltd. Kalyani Maxion Wheels Pvt. Ltd. BF Utilities Khed City (SEZ) USA/EUROPE CDP Bharat Forge GmbH, Germany Bharat Forge Daun GmbH, Germany BF Aluminiumtechnik GmbH, Germany Bharat Forge Kilsta AB, Sweden Mecanique Generale Langroise, France BF PMT Technologies LLC, USA SPECIALTY CHEMICALS Hikal Chemicals

4 Bharat Forge - Global Manufacturing Footprint CDP BHARAT FORGE GmbH BHARAT FORGE DAUN BF ALUMINUMTECHNIK BHARAT FORGE KILSTA Capacity : Forging 90,000TPA Sectors : CV, PV, Railroad, Construction machinery Capacity: Forging & Specialty Machining Sectors: Automotive Capacity : Forging (Aluminum) - 10,000 TPA Sectors : Premium PV Capacity : Forging - 80,000 TPA Sectors : Commercial Vehicles BHARAT FORGE PMT Inc. MECANIQUE GENERALE LANGROISE GLOBAL HEADQUARTERS Steel Forging Light Weighting Aluminum Forgings, Machining Capacity : Precision machining capacity Sectors : Industrial (Oil & Gas) Capacity: 400,000 TPA Forging, Machining Innovation Center Fab & Assembly

5 Kalyani Group India Presence Homegrown Technology Leaders Strategically Aligned Joint Ventures BHARAT FORGE Pune Chakan Baramati Satara KALYANI CARPENTER SPECIAL STEELS LTD Pune (Since 1999) KALYANI TECHNOFORGE Ranjangaon Manesar KALYANI MAXION WHEELS Khed Pune (Since 1995) KALYANI STEELS Hospet, Karnataka INNOVATION CENTERS Pune Hyderabad Bangalore AUTOMOTIVE AXLES LTD Mysore Jamshedpur (Since 1980)

6 Customers AUTOMOTIVE NON - AUTOMOTIVE

7 Customers AUTOMOTIVE NON - AUTOMOTIVE

8 BFL Excellence System - Pillars & Guiding Tools

9 BFL Excellence System Total projects-yoy 1000 Total no. of Projects completed Target

10 Integration of Excellence approach/lean approach with Industry 4.0 Generally asked question Lean and Industry 4.0 Twins/Partners or Contenders? Industry 4.0 will not materialize as a revolution, but in pieces which have to be integrated into Lean framework. Industry 4.0 will only succeed if in synergy with fundamental laws of lean. Industry 4.0 is the topping on cake of lean It makes Lean more flexible, faster, smoother and stable Industry 4.0 in Lean s next level. Industry should learn to walk the lean path first and then attempt to fly, through Digitally enabled lean

11 Integration of Excellence approach/lean approach with Industry 4.0 Examples: Reducing machine breakdown will remain fundamental advancement in sensor technology/analytics will give a new edge. Performance management on shop floor: in today s practice performance is checked at end of the shift, as we move to digital world, performance deviations are monitored in real time.

12 Integration of Excellence approach/lean approach with Industry 4.0 Case Study Predictive Intelligence for Crankshaft Grinding

13 Case Study - Predictive Intelligence for Crankshaft Grinding Operation Objective: Condition based machine monitoring through smart sensors to monitor machine behaviour, detect anomalies, prevent breakdowns and thus reduce poor quality Causes of crankshaft quality defects that can be detected by studying machine behaviour: Work head: Jerky motion, bearing defect, motor burn-out, coupling damage, high vibration, unusual noise Wheel head: Instability, jerky motion, spindle wear out, misalignment, unusual noise, high vibration Motor Pulley assembly: Motor wear-out, belt looseness, belt wear-out, motor burn-out, unstable mounting The above causes amount to 60% of quality defects

14 Smart Sensors - IDE Predictive Analysis of Machine Behavior Collaborating with a Pune based start-up: Infinite Uptime Smart Sensors IDE (Industrial Data Enabler) includes 3 axis accelerometer Temperature sensor Acoustic sensor Scan rates of 25KHz to 50KHz Processor, OS and algorithms for Edge Computing to analyze real time data stream Wireless connectivity to stream data to Cloud Combined with Cloud based platform having Machine Learning algorithms to learn base line machine behaviour and anomaly detection automatically. Also provides production and maintenance dashboards with detail reports

15 Predictive Intelligence Crankshaft Grinder (MCD-I) 3 Critical assemblies of the crankshaft grinder: Wheel Head Work Head Motor Pulley One IDE on each critical assembly Wheel Head Motor - Pulley Data collected 24x7: Vibrational energy (g 2 s) Velocity in X (mm/s) Velocity in Y (mm/s) Velocity in Z (mm/s) Temperature (C) Acoustic level (db) Work Head

16 Learning Phase Collected data for 6 parameters for 3 critical grinder assemblies for 2 full wheel cycles Identified baselines for all critical assemblies for normal operation Alarm / alert generation and notification activated based on set thresholds Dashboard and detail reports

17 Learning Phase Simulation Trails Higher vibrations at higher RPM 12 MPS = 58 Velocity in Y in mm/s (Loading direction on the motor) MPS = 50.3 MPS = 54 Machine is off Datapoints (@ 2 Hz) Aim Set machine stability benchmark and achieve it at all MPS levels

18 Bearing Failure Predictive Intelligence Bearing Failure Prediction & Detection using vibrations of work head assembly on 13 March 2017 Predict Failures In Advance and Prevent Poor Quality Production

19 Enabling Forging Press Line for Predictive Analysis Top 5 areas critical assemblies in Forging Lines Main Press Reduce Roll Transfer Conveyor Padding Press Shot Blasting 4KT Forging Press Line

20 Benefit Targets Achieve Predicting Accuracy of 90% for major breakdowns Prevent causes of highest breakdown time by 60% Prevent poor quality due to process failures and thus reducing fall outs by 60%

21 Next Steps Learn from initial projects Leverage AI for improving prediction accuracy Deploy Predictive Analytics under Industry 4.0 across enterprise

22 THANK YOU!