Smart Control and Cognitive System applied to the HPDC Foundry 4.0

 
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A robust and competitive methodology developed under EU-FP7 MUSIC Project

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Smart Control and Cognitive System applied to the HPDC Foundry 4.0 A robust and competitive methodology developed under EU-FP7 MUSIC Project Edited by Nicola Gramegna and Franco Bonollo SQL Database No-SQL Database Data Analytic Quality Prediction Control & Cognitive System Remote Monitoring & Control Process Optimization Energy & Cost Optimization Assomet Servizi Srl EnginSoft SpA

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Smart Control and Cognitive System applied to the HPDC Foundry 4.0 A robust and competitive methodology developed under EU-FP7 MUSIC Project

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Copyright© 2016 - MUSIC Consortium MUSIC: MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components Collaborative IP Project - FoF-ICT-2011.7.1:Smart Factories: energy-aware, agile manufacturing and customization Contract no. 314145 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise without the permission in writing of the publisher. ISBN: 978-88-87786-14-9

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INDEX INTRODUCTION.............................................................................................................................. 11 HPDC Foundry Competitiveness based on Smart Control and Cognitive System in Al-alloy products 1. Introduction: Factory of Future and Industry 4.0.......................................................................................13 1.1. The Foundry Market and HPDC Multi-Disciplinary Process............................................13 1.2. Challenges and Proposed Solutions............................................................................................14 1.3. Process Parameters And Cognitive Predictive Quality Model.......................................15 1.4. The Expected Impacts..........................................................................................................................16 2. Conclusions And Future Developments........................................................................................................16 CHAPTER 1............................................................................................................................................. 19 Methodology for quantitative classification of quality requirements for HPDC products considering their in-service function and performance requirements 1.  Introduction....................................................................................................................................................................21 2.  Quality requirements for classes of HPDC components.......................................................................21 2.1. Foundries and alloys..............................................................................................................................22 Geographical distribution of EU HPDC foundries.......................................................22 Typical structure and size of HPDC foundries................................................................23 2.2. HPDC products.........................................................................................................................................25 Main classes of HPDC products.............................................................................................25 Main in-service function and performance requirements (by classes)...........26 2.3. Quality control and classification...................................................................................................27 Classes of defects in HPDC products..................................................................................27 Methods for quality control.....................................................................................................32 Quantitative evaluation of defects......................................................................................36 Frequency of defects...................................................................................................................36 2.4. Template table for the quantitative evaluation of defects..............................................37 2.5. Information modelling of the product/quality requirements.......................................39 2.6. Placement of MUSIC Partners Typical Components (AUDI, RDS)................................40 Introduction......................................................................................................................................40 AUDI Typical Component: Shock Tower..........................................................................40 RDS Typical Component: Motor Gear housing.............................................................44 3. Conclusions.....................................................................................................................................................................48 5

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Index CHAPTER 2............................................................................................................................................. 51 HPDC process requirements and parameters 1. Introduction....................................................................................................................................................................53 2. P rocess requirements for HPDC components............................................................................................53 2.1. HPDC Process parameters..................................................................................................................53 2.1.1. Relevant HPDC process parameters ......................................................................53 2.1.2. Most quality-influencing HPDC parameters......................................................58 2.1.3 HPDC Process tracking system (RFID).....................................................................70 2.2. HPDC Process design............................................................................................................................71 2.2.1 HPDC Process design at AUDI.....................................................................................71 2.2.2 HPDC Process design at RDS.......................................................................................74 3. Conclusions.....................................................................................................................................................................76 CHAPTER 3............................................................................................................................................. 77 An Integrated and intelligent sensor network (ISN) as tool for monitoring HPDC process parameters 1. Introduction....................................................................................................................................................................79 2. ISN Implementation...................................................................................................................................................80 3. Verification of the Machine Parameters..........................................................................................................80 3.1. Shot Profile Management..................................................................................................................80 4. Electronics Monitoring System............................................................................................................................81 4.1. Verification of the Inside Die Parameters..................................................................................81 4.2. Metal front contact sensor.................................................................................................................82 4.3. Metal Front Temperature Sensor...................................................................................................82 4.4. In-Cavity Pressure Sensor....................................................................................................................83 4.5. In-Cavity Sensors in General.............................................................................................................84 4.6. Mechanical Tolerances of the Die under High Die Opening Force............................84 5. Infrared / Pyrometer Surface Temperature...................................................................................................84 6. Verification of the Vacuum System...................................................................................................................85 6.1. Multi-Air-pipe-Sensor-System (MASS sensor).........................................................................85 7. Injection Observation and Shot Profile Management............................................................................85 8 . Conclusion.......................................................................................................................................................................86 CHAPTER 4............................................................................................................................................. 87 Intelligent management of the lubrication phase in high pressure die casting 1. Introduction....................................................................................................................................................................89 2. Theoretical principles of lubrication.................................................................................................................89 3. Optimization of the die surface temperature..............................................................................................91 4. M onitoring the impact of lubrication on the die surface temperature........................................92 5. Thermal shock Correlation model ....................................................................................................................94 6. Conclusions.....................................................................................................................................................................96 6

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CHAPTER 5............................................................................................................................................. 99 Real-time HPDC quality prediction and optimization supported by trained cognitive model 1. Introduction ................................................................................................................................................................ 101 2. T he Intelligent Sensor Network and training of Cognitive model................................................ 102 3. HPDC optimization using Smart Prod ACTIVE in production ......................................................... 104 4. Conclusions.................................................................................................................................................................. 107 CHAPTER 6...........................................................................................................................................109 A reference die for set up of process control methodologies based on intelligent sensor network, in view of quality optimisation in HPDC aluminium alloy products 1. The concept of Reference Die........................................................................................................................... 111 2. Concepts and basics of MUSIC HPDC Reference Die. ......................................................................... 111 3.  Preliminary design and final concept of MUSIC Reference Die...................................................... 113 4. Process monitoring in MUSIC Reference Die ........................................................................................... 115 CHAPTER 7...........................................................................................................................................119 Mold behaviour and Post-injection data management 1. K nowledge generation of mold behaviour for predictive software tools................................ 121 2. Wear Mechanisms in HPDC ............................................................................................................................... 121 3. Wear Mechanisms Simulation in Laboratory Scale............................................................................... 122 3.1. Thermal Fatigue.................................................................................................................................... 123 3.2. Die Soldering.......................................................................................................................................... 124 3.3. Erosion........................................................................................................................................................ 125 3.4. Corrosion................................................................................................................................................... 125 3.5. Remarks..................................................................................................................................................... 126 4. Post-injection data management................................................................................................................... 127 4.1. High Pressure Die Casting process (step 1)........................................................................... 127 4.2. Casting System Trimming (step 2)............................................................................................. 129 4.3. Heat Treatment (step 3).................................................................................................................... 131 4.4. Machining (step 4)............................................................................................................................... 136 4.5. Remarks..................................................................................................................................................... 138 CHAPTER 8...........................................................................................................................................141 Innovative control and real-time quality prediction for the casting production of Aluminium alloy structural components at AUDI AG and Gearbox housings at RDS 1. Aluminium alloy Gearbox housings............................................................................................................... 143 2. Aluminium alloy Shock Tower.......................................................................................................................... 147 2.1. Intelligent Sensor Network ............................................................................................................. 148 2.2. Connection with all devices ........................................................................................................... 149 2.3. Configuration of the cognitive model ..................................................................................... 150 2.4. Smart control application in production ................................................................................ 151 7

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Index CHAPTER 9...........................................................................................................................................153 Application of cost model approach in HPDC contexts 1. Introduction ................................................................................................................................................................ 155 2. Literature review....................................................................................................................................................... 156 3. Methodology.............................................................................................................................................................. 157 4. Cost centers identification................................................................................................................................... 157 4.1. Cost voices............................................................................................................................................... 159 5. Application of Cost Model................................................................................................................................... 162 CHAPTER 10.......................................................................................................................................165 Impacts of MUSIC Project on a European scenario 1. Technology impact.................................................................................................................................................. 167 2. Economic impact...................................................................................................................................................... 167 3. Environmental impact........................................................................................................................................... 168 4. Education impact..................................................................................................................................................... 168 5. Standardisation impact......................................................................................................................................... 169 6. Exploitable results.................................................................................................................................................... 169 ANNEX 1...................................................................................................................................................177 Survey of Defects and Imperfections ANNEX 2...................................................................................................................................................201 Complete list of publications related to the MUSIC Project 8

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The MUSIC Project is aimed at developing a MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components under the Factory of Future (FoF) initiative, targeted at improving efficiency, adaptability and sustainability of manufacturing systems as well as their better integration within business processes in an increasingly globalised industrial context. This ambitious and challenging goal can represent a key-action for leading European High Pressure Die Casting and Plastic Injection Moulding Companies to cost-based competitive advantage, achieved by lower scrap generation, efficiency, robustness and minimum energy consumption. The essential tool to do this will be a completely new ICT platform, based on innovative Control and Cognitive system linked to real time monitoring and allowing an active control of quality. Written at the end of the Project, this book, referred to High Pressure Die Casting (HPDC) of Aluminium alloys, intends to analytically describe methods, tools, parameters and innovative approaches developed to monitor and control the process and the quality product. The book collects the guidelines to design and implement the Intelligent Sensor Network (ISN) in HPDC production line as first outcome of MUSIC project. The monitoring network is able to provide useable, meaningful and quantitative data on product quality, as well as to define strategies (varying production process parameters, changes to the tooling, etc.) to move toward higher quality product with economic efficiency. This real time control system capability is then presented and applied to industrial case-histories, showing how to train a cognitive-based ICT platform for the industrial optimisation of High Pressure Die Casting production transforming the acquired knowledge and control methods into know-how. The “Control and Cognitive system”, which constitutes the final MUSIC outcome, will have a positive impact, in next years, on intelligent management of manufacturing information for new smart factory oriented to energyaware, agile manufacturing and customization. As Coordinator and Scientific Manager of MUSIC project, our wish is that this book could be useful to the people involved in Light Alloys foundry industry (more than 2000 Companies in Europe, mostly SMEs, with an enormous potential due to the increasing demand of lightweight and reliable products in each application fields) and our thanks are for all the colleagues who very cooperatively worked in this Project. 9

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INTRODUCTION HPDC Foundry Competitiveness based on Smart Control and Cognitive System in Al-alloy products N. Gramegna EnginSoft SpA F. Bonollo University of Padova - DTG 11

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1. Introduction: Factory of Future and Industry 4.0 The so called Industry 4.0 is the industrial revolution based on Cyber-Physical-Systems (CPS). In the context of Factory of Future (FoF), the smart manufacturing, or Factory 4.0, is part of Internet of Thinks (IoT), of Services and People. The well-known digital transformation of the industrial value chain is relevant for smart grids in the field of energy supply, advanced materials, sustainable mobility strategies (smart mobility, smart logistics) and smart health in the realm of healthcare. The new and radically changed processes in manufacturing companies are applied for new technologies such as Sensors, 3D printing and next-generation robots or globalization of supply chain. The digital innovation is not an exclusivity of new and advanced technology and production processes. The traditional production processes and plants are evolving following this digitalization combining the long experience and the new fast methods to improve the production efficiency and to accelerate the fine-tuning and real-time adjustment of the process parameters oriented to the zero defect quality. Evolution instead of Revolution. The FP7- MUSIC project (funded in the frame of the Call FoF-ICT-2011.7.1 Smart Factories: Energy-aware, agile manufacturing and Customization) is giving a new age to the traditional multi-stages production processes such as High Pressure Die Casting (HPDC) and Plastic Injection Moulding (PIM). The use of Sensors, the totally integrated systems, as well as the data mining and cognitive model are the key ingredient of the MUSIC project to be a reference in the Industry 4.0 context. Having this general context clear in mind, it becomes evident that all project objectives are focused on trans-sectorial production technologies, larger European market, capability of manufacturing site to be flexible, fast and reactive, manufacturing customization and environmental friendliness, management of manufacturing information, ICT application to improve the manufacturing process, new metrology methods and international standardization. 1.1. The Foundry Market and HPDC Multi-Disciplinary Process European Aluminium foundries are a group of about 2600 companies, which produced 3 million of tons of castings in 2011. Key players are Germany and Italy, with 60% of total production from Europe (0,931 and 0,844 Mio tons for Germany and Italy corresponding to a turnover of 5.092,00 and 4.051,00 Mio of euro) and an average number of employees of 15 in Italy and 96 in Germany (source CAEF). The 50-60% of Al alloy castings is produced by HPDC process. Categories of diecast products as thin wall and safety castings (represented in MUSIC project by a shock tower manufactured by AUDI) and Housing and Covers (represented in the MUSIC project by a gearbox manufactured by RDS) are the 75% of the total HPDC production (Fig. 1). High Pressure Die Casting (HPDC) of light alloys is one of the most representative largescale production-line in manufacturing fields, which are strategic for the EU-industry largely dominated by SMEs. Due to the high number of process variables involved and to the non-synchronization of all process parameters in a unique and integrated process control unit, HPDC is one of the most “defect-generating” and “energy-consumption” process in EU industry showing less flexibility to any changes in products and in process 13

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Introduction Fig.1 – Main categories of high pressure die casting products evolution [1]. Sustainability issue imposes that machines/systems are able to efficiently and ecologically support the production with higher quality, faster delivery times, and shorter times between successive generations of products. This is the scenario of the MUSIC project, strongly aimed at leading EU-HPDC factories to cost-based competitive advantage through the necessary transition to a demand driven industry with lower waste generation, efficiency, robustness and minimum energy consumption. The development and integration of a completely new ICT platform, based on innovative Control and Cognitive system linked to real time monitoring, allows an active control of quality, avoiding the presence of defects or over-cost by directly acting on the process machine variables optimisation or equipment boundary conditions. The Intelligent Manufacturing Approach (IMA) works at machine-mould project level to optimise the production line starting from the management of manufacturing information. An Intelligent Sensor Network (ISN) monitors the real-time production acquiring the multi-layers data from different devices and an extended meta-model correlates the input and sensors data with the quality indexes, energy consumption cost function. Data homogenization, centralization and synchronization are the key aspects of control system to collect information in a structured, modular and flexible database. Process simulation, data management and meta-model are key factors to generate an innovative Cognitive system to improve the manufacturing efficiency. 1.2. Challenges and Proposed Solutions Introducing intelligent manufacturing systems in HPDC, made available by autonomous and self-adaptive devices, changes totally the actual organization and potential of this process. According to the experience of MUSIC Partners, which are well established players in the HPDC manufacturing scenario, six main challenges have to be faced for the progress in this field. These challenges are directly addressed by MUSIC project multi-level objectives and answered by several specific outcomes, associated to the main breakthroughs identified as follows: 14

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Challenge # 1) leading HPDC/PIM to “zero-defect nvironment 2) introducing real-time tools for process control 3) monitoring and correlating all the main process variables 4) making the process set up and cost optimisation a knowledge-based issue 5) involving to multi-disciplinary R&D activities 6) impacting on EU HPDC/PIM companies, by dissemination and standardization activities Represents the breakthrough Quality improved and defects minimised Process data acquired by real-time tools Extent of knowledge of Process data vs. Quality as well as vs. Maintenance Extension of average die life, Cost reduction in HPDC/PIM production cell, Process efficiency (energy & material) improved Control & Cognitive System International standardization The innovation aspects introduced by MUSIC project results are referred to process optimization, centralized control of multi-stages production efficiency as detailed in the following list: „„ A new Control and Cognitive system (the “smart Prod ACTIVE” tool), from design chain simulation and process optimization to real-time quality and cost models, is ready for the market, „„ The High-performance production is supported by introducing advanced Sensors Network and Centralized data management to control all stages and tools in the production line, „„ T he process data, recorded in a proper database, are managed and elaborated in real time to predict Quality and Cost of single product through advanced and trained process meta-models, „„ The Machine operator, the Production manager or Plant director are supported by smart web-based GUI to remotely visualize and navigate the real-time or historical process data. 1.3. Process Parameters And Cognitive Predictive Quality Model The “smart Prod ACTIVE” tool (Fig. 2) predicts the quality, energy and cost of the injection process in real-time, covering the 100% of products, and suggests the appropriate re-actions to adjust the process set-up and/or mechanism. It works in combination with the real time monitoring system (or Intelligent Sensor Network) to elaborate instantaneously the production data set with respect to quality/energy/ cost prognosis. The client-server mechanism works in combination with the real time monitoring system (or Intelligent Sensor Network) to elaborate instantaneously the production data set with respect to quality/energy/cost prognosis. The client-server Connections, based on OPC_UA protocol that is accepted as Interface for Industry 4.0, are collecting all process data coming from all existing devices [2-3] and active sensors in a centralized database. A fundamental innovative characteristic of “smart Prod ACTIVE” tool is the Cognitive predictive quality model integrating multi-resolution and multi-variate process data, monitored and gathered by an articulated network of sensors by means of the collection of distributed control system, advanced models linking process variables to specific 15

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