Alter igit-cmbit midih-presentation oc2
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This document describes a proposed solution from IGIT & CMBIT to improve the pipe bending manufacturing process using Industry 4.0 technologies. The current process uses an industrial robot, band saw, and bending machine, but has limitations in traceability, repeatability, and producing lot-size-one orders. The proposed solution adds a robotic arm equipped with a 3D camera and microcomputer to recognize pipe materials using neural networks, grasp pipes in the optimal position using computer vision, and optimize waste reduction using reinforcement learning. The solution integrates components from the Apache software stack to acquire data, transmit messages, store data, and develop the system. Key performance indicators include accuracy improvements and reductions in errors, waste and processing time. Explo
Alter igit-cmbit midih-presentation oc2
- 1. ALTER Speaker: Norbert Bruch, Łukasz Głaz
- 3. IGIT & CMBIT A SYSTEM INTEGRATOR WORKING IN FOLLOWING AREAS: • ELECTRICAL WORK • IT INFRASTRUCTURE • SOFTWARE DEVELOPMENT Currently seeking its way to move from providing services to delivering innovative products of its own A PRODUCTION COMPANY INTERESTED IN IMPLEMENTING A SOLID INDUSTRY 4.0 SOLUTIONS FOR PIPE BENDING INDUSTRY
- 5. THE PROCESS THE ROBOTIZED PROCESS CELL DESIGNED TO WORK AS A FLEXIBLE PIPE BENDING CENTRE. • Cost-efficient, flexible robotic station for lot-size-one manufacturing of bent metal elements. • The currently used station consists of • an industrial robot • automated band saw • bending machine. • Costs five times less than a dedicated machine • But it has its limitations...
- 6. THE CHALLENGES Traceability Repeatability Lot-size-one Lack of warehouse optimization Lack of skilled workforce Missing pipe identification Careless and inaccurate pipe placement on feed rack
- 8. THE SOLUTION • Extension to process cell, allowing for: • Reduction of error-rate caused by operator • Optimalization of production with given criteria • Production time • Material waste
- 9. THE HARDWARE The robotic arm has been equipped with improved, robust gripper containing also: • 3D Camera • Raspberry Pi4 microcomputer
- 11. MATERIAL RECOGNITION APPROACH: TRANSFER LEARNING USING RESNET34 • What is transfer learning? • Why Resnet34? • Convolutional Neural Networks RESULTS: Confusion matrix Predicted class Black steel Brushed stainless steel Polished stainless steel Raw aluminium Actualclass Black steel 10 Brushed stainless steel 8 2 Polished stainless steel 6 4 Raw aluminium 10 Metrics Black steel Brushed stainless steel Polished stainless steel Raw aluminium TP 10 8 4 10 TN 30 24 28 30 FP 0 6 2 0 FN 0 2 6 0 Precision 1.0 0,57 0,66 1.0 Accuracy 0,8
- 12. MATERIAL GRASPING APPROACH: CAMERA BASED POSITION AND ORIENTATION RECOGNITION • Image preprocessing • Estimation of the shape • Identification of the grasping point 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% Boundaries identified Pipe catched Pipe catched in desired part SHAPE RECOGNITION AND CATCH - SUMMARY Straight pipe Pre-bent pipe RESULTS:
- 13. MATERIAL WASTE REDUCTION APPROACH: REINFORCEMENT LEARNING • Basic concepts: Agent, Environment & State, Action, Reward • Building an example data generator to validate important scenarios • Assumptions: • State must always be the same size • State can't be too big • Rewards must support optimization goals RESULTS: EFFECTS ARE VISIBLE BUT STILL HAS SOME ROOM FOR IMPROVEMENTS: • Always cutting all available pipes • Sometimes chooses an invalid pipe (too short for processing)
- 14. ARCHITECTURE
- 15. MIDIH COMPONENTS INTEGRATED FOLLOWING COMPONENTS FROM APACHE STACK HAVE BEEN IMPLEMENTED: • NiFi+MiNiFi Data acquisition from camera and orchestration • Kafka Middleware layer providing message-based communication • Cassandra The data persistence component • Zeppelin A multipurpose notebook serving as an integrated development environment
- 19. TECHNICAL KPIs ACCURACY OF THE LENGHT MEASUREMENT: 5CM ACCURACY OF MATERIAL IDENTIFICATION: 80% ACCURACY OF GRASPING: 80%
- 20. BUSINESS KPIs • NEW POTENTIAL CUSTOMERS OF IGIT: 3 • NEW POTENTIAL CUSTOMERS OF CMBIT: 3 • NEW PRODUCTS IN CMBIT OFFER PROCESSED AUTOMATICALLY : 20 • REDUCTION OF THE MANUFACTURING ERRORS: 5% • REDUCTION OF THE UNUSABLE WASTE: 35% • REDUCTION OF THE PROCESSING TIME: 25%
- 21. EXPLOITATION PLAN CONTINUE OF THE DEVELOPMENT OF THE AI BASED TECHNOLOGY • Reach TRL9 • Build a product around the solution • Implement the results to different domains (Welding) EARN THE CUSTOMERS ATTENTION THORUGH MARKETING STRATEGY • Association with MIDIH and H2020 • Demonstrate the technology through electronic media ( YouTube, remote demonstrations)
- 22. THANK YOU!