GO0D MAN go0dman-project.eu. Dr. Cristina Cristalli

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1 Dr. Cristina Cristalli

2 Advanced sectors have many sensors typically present on the production line for automated quality and process controls. 100% quality controls are performed on the final products at the end of the production line. Manufacturing Sector Simpler production environments continue to exist, where most operations are human-based including quality controls; a very limited numbers of sensors are used. Today, data coming from quality controls are mostly used in real-time for each single production process control: they are usually processed together as vertical silos in a multistage process, mainly off-line at the level of SPC.

3 Multi Stage Production Flows (serial / parallel) of: Materials / Components Data / Information Final product quality depends on: Variations propagated from upstream stages Performance of each single process Interactions between processes

4 Quality and Process Control Evolution

5 Manufacturing Sector Needs Increasing Demand of Mass Customization, Personalization and Small Batch Pressure to Deliver Zero Waste and Improve Social and Environmental Performances New Service-oriented Businesses and Globally Efficient Manufacturing Ecosystems Integrated approach to Quality Control, Production Logistics and Maintenance Policies

6 GO0D MAN Objective The main idea of GO0D MAN project is to integrate and combine process and quality control for a multi stage manufacturing production into a distributed system architecture built on agentbased Cyber-Physical Systems (CPS) and smart inspection tools. GO0D MAN supports the real-time data collection and defect diagnosis at single process level, as well as the inter-stage sharing and processing of information at global level using data mining techniques.

7 Partnership

8 Representative of a wide range of EU manufacturing plants Demonstrators Industrial Sector Electrolux Professional Volkswagen Autoeuropa Zannini Professional food, beverage & laundry appliances Automotive (OEM) car manufacturing Automotive (Tier 2) machined metal components Production Process Customized production Highly flexible multi stage manual and semi-automatic assembly Serial production Multi stage manual and semiautomatic assembly Batch production High precision multi stage machining operations Expected Results Time reduction and optimization of the final testing area, to increase the productivity and reduce the Not Right First Time. Reduction of the final inspection and rework time and consequent increase of productivity without compromising quality. Reduction of production costs, as a consequence of the increase of the overall OEE: reduction of scraps, downtime and rework time. -8-

9 Implementation WP1: Specification of ZDM Strategies WP4: Develop Data and Knowledge Management Tools for ZDM WP2: Develop Multi-Agent based CPS for ZDM WP3: Develop Smart On-Line Inspection Tools for ZDM WP5: Integrate ZDM Tools in Multi-Stage Production Line WP6: Demonstrate ZDM at VWAE WP7: Demonstrate ZDM at Zannini WP8: Demonstrate ZDM at Electrolux WP9: Exploitation, Training and Impact Analysis WP10: Dissemination and Standardization WP11: Project Coordination and Management

10 Distributed System Architecture

11 Technological Pillars Intelligent quality control systems (smart inspection tools) - real-time defect detection - adaptive behavior - active management of measurement uncertainty - quality indicators extraction - self-diagnosis and calibration

12 Target Developments 1. Develop a portable device to measure Gap&Flush on tailgate, rear glass, chromed components and rear headlights of T-ROC model in the assembly line to reduce operator interventions (reworking) Target uncertainty: < 0.5mm 1. Develop a contactless inspection tool for measuring the geometrical features of bores to reduce compensation in the honing process Target uncertainty: < 5mm 2. Develop an automated inspection tool to identify presence/location of burrs ( 1 mm long, mm thick) in turned components to reduce reworking 3. Monitor tool wear to reduce reworking ON-LINE quality control systems 1. Develop a device to highlight presence/location of leaks on the oven front door-frame assembly to reduce heating inefficiency 2. Develop a device to measure Gap&Flush on the oven front doorframe assembly for aesthetical and functional purposes Target uncertainty: < 0.5mm 3. Develop a device to identify noisy/faulty fans in the oven assembly 4. Develop a device to identify wrongly assembled components in the oven Introduce SMART features SELF-ADAPTIVE BEHAVIOUR SELF DIAGNOSIS / SELF-CALIBRATION MODULARITY OF TEST, PLUG-IN/PLUG-OUT SMART ON-LINE INSPECTION TOOLS

13 Technological Pillars Multi Agent System (MAS) Cyber-Physical Systems (CPS) - distributed - decentralized - modular - robust

14 Approach to the MAS Architecture

15 Technological Pillars Data-driven approach at local and global levels - early detection of patterns and trends in process/product quality - from data to knowledge and from knowledge to management

16 1 6 ZDM Data Analytics Data Analytics Environment

17 Knowledge-Supported Production Men Machine Method Material Design P C D A Systematic (Quality) Knowledge Production 1 2? 4M + D + Context (Station) + Anomolies + Quality Parameters 3 Stage Stage Stage KPI Visualisation

18 KPI Identification

19 Evaluation Criteria for the Functional Requirements

20 Evaluation Criteria for the Non- Functional Requirements Category Functionality Reliability Usability Efficiency Scalability Portability Maintainability Description The system needs to be functional and available. The system should maintain the overall performance of the platform during a long usage time. The system is easily understandable and usable by the identified stakeholder with reduced effort. The system is capable to manage and operate the available resources according to the defined rules and goals. The system is capable to be instantiated in bigger lines. The system has to be able to handle more resources and bigger amounts of processing entities. The capability of the system to be instantiated in different environments and settings. Capability to update and/or configure the system without experts and without damage the overall functionality of the system.

21 Summary of the Use Case Objectives An assessment of the objectives was made in order to define the overall goals for the different demonstration scenarios

22 ZDM Methodology Phases

23 Expected Impact VWAE Zannini Electrolux Achievement of zero defects in a multistage production line First Run Capability (FRC) +10% Process capability index (Cpk) +15% Scraps reduction -12% Final test time -23% Scraps reduction -10% Reduction of production costs by 15% -15% thanks to the reduction of rework time -18% thanks to the reduction of scraps and the increased Cpk -15% thanks to the reduction of End-Of-Line test stations (- 36% - from 11 to 7) and the increase of the line flexibility Increased production flexibility. Higher production rates by 15% +10% thanks to the fact that the operator can do other tasks and the reduction of the inspection and repair time From +12% to +15% thanks to the increase of the process capability index (Cpk), reduced down-time of tool machines and reduction of defects +15% thanks to reduction of the time spent in the final quality control area (-40% - from 77 min to 46 min) and in the whole testing process (- 23% - about 17 min) Reduction of waste and scrap by 10% -10% thanks to the introduction of new measurement techniques (optical devices) and data correlation -12% thanks to the introduction of new measurement techniques (optical sensors for geometry and burrs, laser diffraction) and tool process monitoring -10% thanks to the introduction of new measurement techniques (thermography, noise and vibrations)

24 Advisory Board Prof. Jianjun (Jan) Shi, Professor at the Georgia Institute of Technology. His interests focus on system informatics and control for the design and operational improvements of manufacturing and service systems. Prof. Shozo Takata, Professor at the Department of Industrial and Management Systems Engineering at Waseda University in Tokyo. His research interests are in life cycle engineering focusing on design and management of reuse and recycling system for the circular manufacturing 4ZDM Cluster

25 4ZDM cluster The GO0DMAN project has its roots in the 4ZDM cluster, the European initiative around the FoF Zero Defect Manufacturing priority which aims to promote the adoption of ZeroDefect production and quality control systems by industry.

26 Thank You Dr. Cristina Cristalli Project Co-ordinator GO0D MAN project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No