Evaluation HPDC Lubricant Spraying for improved cooling and die protection

AUTHORS

B. Zabalaa, A. Igartuaa, C. Raoneb, G. Timellic, F. Bonolloc, V. Scapic, M. Peggiatoc, F. Girotd

a) Fundación TEKNIKER, Parque Technologikoa, c/ Iñaki Goenaga, 5, 20600 Eibar, Spain

b)  Motul Baraldi, S.r.l. - Via Lombardia 2/I-2/L - 40024 Osteria Grande - Castel San Pietro - Bologna (BO) - Italy

c)  Univ. Padova, Department of Management and Engineering, Stradella S. Nicola 3, I-36100 Vicenza, Italy

d) University of the Basque Country, UPV-EHU, Faculty of Engineering, Department of Mechanical Engineering, IKERBASQUE, Basque Foundation for Science, Alameda Urquijo s/n 48013 Bilbao, Spain

Submitted to LUBMAT 2016 - Bilbao, June 2016

 ABSTRACT

This study tries to find out a better cooling and temperature homogenization as well as better die protection on High Pressure Die Casting (HPDC) spray lubrication. Test procedures have been set up to study the Leidenfrost Point (LFP), Contact Angle (CA), film thickness and protection from die soldering of lubricants typically applied into the die surfaces during the High Pressure Die Casting process.

Five different lubricants have been studied as well as the influence in different controllable process parameters (type of die material, oxidation of the surface, temperature, roughness, droplet diameter, water hardness and lubricant concentration).

The increase of the LFP, avoiding film boiling regime, and a reduced CA, improves the cooling and film ability of HPDC moulds during spraying. The best chemistry exhibited high LFP, showed an increased thickness of the formed film and it has been shown effective increasing the time until sticking the aluminum part to the die surface. The study performed, results in a better insight of the involved phenomena and allows selecting the most favorable operating window for high pressure die casting lubrication.

 

KEYWORDS: High Pressure Die Casting, Spraying, Leidenfrost Point, Contact Angle

AKNOWLEDGMENTS

The authors would like to thank the collaboration of MUSIC project partners. The research leading to these results has received funding from the European Union’s Seventh Framework Programme [FP7-2012-NMP-ICT-FoF] under Grant Agreement Number 314145.

For further information about this paper and the MUSIC Project, plase contact: borja.zabala@tekniker.es

Prediction of Moulds Wear in High Pressure Die Casting and Plastic Injection Moulding

B. Zabala, Doctoral Thesis due in 2016

CONCEPT

High Pressure Die Casting and Plastic Injection Moulding are very cost effective and high production rate manufacturing processes. Both consist introducing at high pressure and high speed a fluid material that solidifies inside a mould. These moulds have to endure thousands of cycles on this aggressive media which leads to a wear that limits the mould life, as well as unexpected production stops and high cost in maintenance and reparation. The damage of  the moulds is ruled by different wear mechanisms. Some are common for both manufacturing processes like erosion and corrosion, while some are specific like abrasion in Plastic Injection Moulding and die soldering and thermal fatigue in High Pressure Die Casting. This doctoral thesis deals with all these wear mechanisms, setting protocols to simulate them in laboratory scale and studying the influence of different process parameters, in order to predict the wear of the moulds.

Process parameters affecting quality of high-pressure die cast alluminum alloys

E. Fiorese, PH.D Thesis due in 2016

MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components

 N.Gramegna, M. Zanotti, EnginSoft S.p.A. - International Die Casting Congress at EUROGUSS 2016

ABSTRACT

MUSIC project: An intelligent management of manufacturing information to drive metal and plastic production line for injected components

High Pressure Die Casting (HPDC) of light alloys and Plastic Injection Moulding (PIM) are two of the most representative large-scale  production-line in manufacturing field, which are strategic for the EU-industry largely dominated by SMEs. In particular, the European non-ferrous casting production represent the 12% of world-wide production with 3.5 million of tons per year (source CAEF – 2013) where 78% is dedicated to Vehicle sector.  The light alloy products are growing of +10% in comparison with 2010 (Germany and Italy are producing about 2 million of tons, 60%) balanced by +9% of employment. Current trends show an improvement in demand for light products considering the material substitution for complex structural parts, the design and technology innovation (e.g. additive manufacturing) as well as the evolution in smart production. Due to the high number of process variables involved and to the non-synchronisation of all process parameters in a unique and integrated process control unit, HPDC and PIM are most “defect-generating” and “energy-consumption” processes in EU industry showing less flexibility to any changes in products and in process evolution. In both, 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. In agreement with Industry 4.0 and Smart Factory vision, the scenario of  the MUSIC project is strongly aimed at leading EU-HPDC/PIM 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. The MUSIC project, in the context of FP7 EU RDT Programme, introduces new ICT technologies at manufacturing plant with significant potential impacts: (i) strengthened global position of European manufacturing industry; (ii) larger European market for advanced technologies such as electronic devices, control systems, new assistive automation and robots; (iii) intelligent management of manufacturing information for customization and environmental friendliness.

The paper will consider the first achievements of the research activities, focusing its attention to the guidelines and standard procedures that characterize the huge variety of aspects related to products coming out from HPDC process. The product requirements are fundamental to determine measurable criteria necessary to design a monitoring network (ISN) able to provide useable, meaningful and quantitative data on product quality, as well as to define strategies to move toward a higher product quality.

The analysis and impact of these aspects will be supported by real applications used to present and verify the obtained results.

THEME:  

Smart Factory, Intelligent Manufacturing, Process Control, Quality Analysis

KEYWORDS

High Pressure Die Casting, Plastic injection moulding, intelligent manufacturing, real-time monitoring, data mining,  cognitive system, sensors, process optimisation, quality management.

Experimantal and numerical study for thermal fatigue appearance prediction in HPDC

 Authors:

B. Zabala a, d*, A. Igartua a, Rudolf Seefeldt b, Erik Hepp b, B. Kujat c, J. Mueller d, F. Girot e, f

a) PARKE TEKNOLOGIKOA, c/ Iñaki Goenaga, 5, 20600 Eibar, Spain

b) MAGMA Gießereitechnologie GmbH, Kackertstrasse 11, 52072 Aachen, Germany

c) I/PG-A3G AUDI AG, D-85055 Ingolstadt, Germany

d)University of Kassel, Casting technologies department, Kurt-Wolters-Str. 3, 34125 Kassel, Germany  

e) University of the Basque Country, UPV-EHU, Faculty of Engineering, Department of Mechanical Engineering, Alameda Urquijo s/n 48013 Bilbao, Spain

f) IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain

*corresponding author: borja.zabala@tekniker.es

International Die Casting Congress- EUROGUSS 2016

ABSTACT

HPDC is a very cost efficient method, however, the maintenance and initial costs of the mould limits the competitiveness of the process seriously. Thermal fatigue cracking is generated due to alternate heating and cooling of the die during the die-casting cycle, and reduce the quality of the casted parts making the mould useless after around one hundred thousand cycles.

In this study, results of fatigue cracks appearing in the mould surface on a induction heating based test bench are shown, and compared with simulation results performed through thermal stress calculation in the dielifetime module of MAGMA5. Wöhler curves at high temperatures of the typical HPDC (1.2344 and 1.2343) mould materials to improve this prediction have been implemented on the simulation tool. The simulation gathered some initial conclusions and was useful for setting the experimental design for the Thermal Fatigue Machine.

Through this procedure it is expected to improve the prediction of HPDC moulds cracking prediction and improve the lifetime.

Correlation between parameters and quality characteristics in aluminum high pressure die casting

M. Winkler, L. Kallien, Hochschule Aalen - International Die Casting Congress at EUROGUSS 2016

ABSTRACT

Das Aluminium-Druckgießverfahren ist ein hochproduktives Verfahren zur Herstellung endkonturnaher Gussteile. Die Komplexität und die Anforderungen an die Gussteile steigen. Die Herausforderung im Druckgießverfahren ist die Einhaltung hoher Qualitätsstandards trotz der unzähligen Parameter, welche die Qualität der Gussteile beeinflussen. Die Wechselwirkung aller qualitätsrelevanten Einflussfaktoren führt zu hohen Ausschussraten von 10 – 25%. Die Prozessparameter werden nicht in einer zentralen Einheit sondern von verschiedenen Einheiten wie der Druckgießmaschine, dem Ofen oder dem Temperiergerät gesteuert. Die typischen Parameter, die aufgezeichnet werden, sind unter anderem die Kolbengeschwindigkeit in der ersten und zweiten Phase und der Maschinendruck, allerdings gibt es weitaus mehr Parameter wie beispielsweise die Feuchtigkeit der evakuierten Luft, welche die Qualität ebenso beeinflussen.

Das Europäische Forschungsvorhaben MUSIC (MUlti-layers control and cognitive System to drive metal and plastic production line for Injected Components) hat die Reduzierung der Ausschussraten durch die Entwicklung eines intelligenten kognitiven Systems, welches alle qualitätsrelevanten Prozessparameter berücksichtigt, zum Ziel. Im Gießereilabor der Hochschule Aalen wurde hierzu ein Gussteil entwickelt, mit dem die unterschiedlichsten Gussfehler wie Schwindungslunker, Gasporositäten und Kaltlauf hergestellt und untersucht werden können. Die Form ist mit vielen neuen, innovativen Sensoren ausgestattet, welche zusätzliche Prozessparameter wie beispielsweise die Beschleunigung des Kolbens aufzeichnen und bisher noch nicht verwendet wurden. Die Sensormessungen und die Prozessparameter der Druckgießmaschine und der Peripheriegeräte werden zusammen mit den Qualitätsdaten der Gussteile in einer gemeinsamen Datenbank gespeichert. Diese Daten bilden die Grundlage für das kognitive System um die Qualität nachfolgender Gussteile zu prognostizieren.Industry 4.0 die casting plant - possibilities of a standardized data acquisition

 Kai Kerber,Oskar Frecb GmbH + Co. KG - International Die Casting Congress at EUROGUSS 2016

Correlation between defect content, mechanical properties and fractographic investigation of AlSi9Cu3(Fe) alloy Reference Castings

E. Battaglia, F. Bonollo, I. Tonello, E. Fiorese, Proceedings of THERMEC 2016

ABSTRACT

High Pressure Die Casting (HPDC) is a foundry process particularly suitable for high production rates and applied in several industrial fields, but the amount of scrap, caused by defects or incomplete filling, is sometimes very high. Thus it is important to know which are the main causes of defect formation and their effects on microstructure and mechanical properties. This paper presents, within the European MUSIC project, the qualitative and quantitative results of a study conducted on AlSi9Cu3(Fe) alloy castings, referred to as Horse-shoe Reference Castings, specifically designed to generate different kinds of defects with different severity levels. The work focuses on the correlations obtained between the casting mechanical properties, their defect content in terms of porosity and oxide films and the process parameters adopted, mainly second phase plunger velocity and intensification pressure. The three point bending test was carried out on the four specimens obtained from the two appendixes of the casting. The fracture surfaces were studied by scanning electron microscopy (SEM) and optical microscopy (OM) highlighting that the defect content is clearly correlated to the mechanical properties and the process parameter settings.

KEYWORDS: High Pressure Die Casting, defects, three point bending test, process paramenters, horse-shoe shaped casting

HPDC foundry competitiveness based on smart Control and Cognitive system in Al-alloy products

N. Gramegna – Enginsoft SpA; F. Bonollo – DTG, Università di Padova at HTDC2016


Abstract
High Pressure Die Casting (HPDC) technology is facing new challenges in terms of quality requirements from the endusers, production rate achievable, process monitoring and control, in a complex worldwide scenario. A relevant contribution to HPDC competitiveness has been offered by the EU-FP7-funded MUSIC Project. It is probably the biggest research project ever carried out in the field of HPDC, with 16 partners and an effort of about 1000 personmonths. MUSIC developed a totally new Control and Cognitive system, giving an integrated and multi-disciplinary answer to the most relevant issues for HPDC industry: “zero-defect” production, real time process control, understanding the role of process variables, process optimization and real time cost evaluation. 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” processes. Sustainability imposes that machines/systems are able to efficiently and ecologically support production with higher quality, faster delivery times, and shorter times between successive generations of products. The new “smart Prod
ACTIVE” tool is a flexible and totally integrated system able to predict the real-time quality and the cost. Its extension of application to further multi-stages and multi-disciplinary production lines (e.g. sheet metal forming, forging, rolling, thermoforming, machining, welding, trimming, or the innovative additive manufacturing) is planned to exploit the same methodology in different industrial contexts.

An Integrated and Intelligent Sensor Network as tool for monitoring HPDC process parameters

U. Gauermann, A. Mazzamuto – Electronics GmbH; N. Gramegna, P. Donaggio - EnginSoft SpA at HTDC2016


Abstract
In agreement with Industry 4.0 and Smart Factory vision, the Intelligent Sensor Network (ISN) is the hardware base to control the multi devices HPDC production line from the melting furnace, throughout the injection machine and die, to the final post-casting operations. Traditionally, each single device, delivered by different vendor, is using a specific PLC and user panel to setup and control some parameters of one phase of HPDC process. The machine operator and Production Manager have to manage all devices in a separate way and time. Typically, the press is controlling the injection, the die thermal behavior can be visualized by TTV thermo-camera during the injection and solidification heat pulsing and thermal-regulation unit impact. The setup and control of process stability, in all phases of the process, can be centralized
today in a common database with unique smart user interface. Data homogenization, centralization and synchronization are the key aspects of the monitoring system to collect information in a structured, modular and flexible database. This Intelligent Manufacturing Approach (IMA) works at machine-mould project level to optimize the production line starting from the management of manufacturing information based on OPC_UA communication protocol. The real-time data acquisition by LAN network and the remote visualization in a Computer monitor, Tablet or Smartphone, with available internet access are the innovative smart tools for the new era of machine operator and production manager. Information from ISM can be managed by specific self-adaptive device. In this way, ISN is a tool suitable for fully exploiting the
potential of HPDC process, which can be transformed from a production-rate-dominated manufacturing field into a cost/efficiency-driven and integration-oriented process.

Intelligent management of the lubrication phase in High pressure die casting

C. Raone – Motul Tech Baraldi, N.Gramegna – EnginSoft S.p.A. at HTDC2016

Abstract
In the process of high pressure die casting of light alloys, In the process of high pressure die casting of light alloys, the lubrication of the mold by spraying dilute aqueous emulsions is the less controllable phase of the cycle. Many parameters in the application (type and number of nozzles, pressures and distances, time of spray, lubrication cycle) as well as those related to the chemical aspects, play a role on the mechanisms of wetting of the surface, heat exchange and deposition of the film. Within the FP7 MUSIC Project, it has been designed and assembled an equipment for bench testing, with the aim
of studying the influence of various parameters on cooling effect, in the application of a spray lubricant on a die at different surface temperatures. An H11 steel plate is cyclically heated at preset temperatures, then automatically translated and sprayed. Infrared thermal images of its surface are recorded before and after spraying, and elaborated in terms of on different areas. From the early tests it turned out that an increase in the concentration of the die lubricant led to an increased cooling of the plate, whereas the influence of the distance between nozzles and steel plate was far less important, in the given conditions.

Correlation between process parameters and quality characteristics
in aluminum high pressure die casting

M. Winkler, L. Kallien, T. Feyertag, Aalen University of Applied Sciences, Aalen, Germany at HTDC2016


Abstract
Aluminum high pressure die casting is one of the most productive manufacturing processes. The complexity of the parts rises and the quality requirements are increasing. The challenge in high pressure die casting is to reach the high quality standards in spite of the huge number of quality influencing process parameters. The interaction of all quality influencing parameters leads to extremely high scrap ratesup to 10 -25%. The European research project MUSIC (MUlti-layers control and cognitive System to drive metal and plastic production line for Injected Components) has the aim to decrease the scrap rates in high pressure die casting by developing an intelligent cognitive system taking all quality controlling parameters
into account. In the frame of the project a special casting geometry has been developed, that allows the production of parts with several defects such as shrinkage porosity, cold shuts and distortion. The die is instrumented with many new and innovative sensors to monitor new process parameters, such as the sound of the shot, which have not been applied to date. The sensor data, the process parameters of the machine and the peripheral devices are stored together with the quality index of the castings in one common database. To find correlations the process parameters were varied with different DOEs. The database with the data of the cast trials is the basic information for the prediction of the cast part quality.

Innovative Control and real-time Quality prediction for high tech Al-alloy structural components at AUDI

B. Kujat – AUDI; N. Gramegna, G. Scarpa – EnginSoft SpA; M. Benvenuti – DTG, Università di Padova at HTDC2016
 

Abstract
Thin-wall structural parts produced by high pressure die casting (HPDC) are designed and applied in the automotive production sector. The Audi strategy is the application of lightweight alloy components produced by HPDC in the structure of future car bodies. One of the key components of this strategy is the shock tower. The research of smart control strategies in order to improve the quality and production efficiency of these parts is a main objective of the technical center for HPDC of AUDI AG. An optimized cognitive method is therefore introduced and integrated in a single centralized control system. The shock tower use case is the selected demonstrator for testing and validating the cognitive control system. Based on an intelligent sensor network, communication with all devices, process data management and a quality
prediction in terms of filling and solidification defects, a vast improvement of the casting production process is expected.

Real-time HPDC quality prediction and optimization supported by trained Cognitive model

P. Donaggio, A. Bassi, G. Scarpa, N. Gramegna – EnginSoft SpA; E. Battaglia, E. Fiorese – DTG, Università di Padova at HTDC2016


Abstract
Manufacturing current trends show an improvement in demand for light products considering the material substitution for complex structural parts, the design and technology innovation as well as the evolution in smart production. Due to the high number of process variables involved and to the non-synchronisation of all process parameters in a unique and integrated process control unit, HPDC is one of the most “defect-generating” and “energy-consumption” processes in EU industry showing less flexibility to any changes in products and in process evolution. In both, 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. Starting from an intelligent monitor system of HPDC
process, an advanced trained meta-model is the key factor to improve the manufacturing efficiency predicting the real-time quality and cost of the product. The offered training methods and virtual or real Cognitive models correlate the input and sensors data with the quality indexes, energy consumption cost function. The Machine Operator or Production Manager can react shot by shot, supported by Control & Cognitive system integrated in the foundry site, to improve the quality and the production rate of each production line.

Application of cost model approach in HPDC contexts

L. Macchion, G. Kral, F. Bonollo – DTG, Università di Padova; N. Gramegna – EnginSoft SpA at HTDC2016


Abstract
The application of cost model approach in High Pressure Die Casting (HPDC) context has been investigated by defining parametric analysis to identify the main sources of costs and the related impacts on production processes. Results highlight the possibility for companies to better control industrial costs of their products by examining in detail the different costs composing the HPDC process and verifying at the same time the adherence to budget. Furthermore, particular attention was dedicated to attest that the industrial cost of production significantly decreases when companies invest in quality controls during the casting trial activities.

Overview of the correlations between process parameters, microstructure and mechanical properties of reference castings and industrial demonstrators

E. Battaglia, F. Bonollo, E. Fiorese, G. Kral - INALCO 2016

Relationship spray intensity, nozzle configuration and local heat transfer coefficients at the die surface

 F. Zuliani, thesis at DTG , January 2016

Simulation of wear mechanisms and modelling lifetime of AlHigh Pressure Die Casting Moulds

 B. Zabala, A. Igartua, IK4-Tekniker; F. Bonollo, G. Timelli, Univ. Padova;E. Hepp, R. Seefeldt, Magma; L. Baraldi, C. Cosimo, Motul-Baraldi; B. Kujat, AUDI, F. Girot, UPV

Presentation delivered during the KMM-VIN 6th Industrial Workshop on Materials for Transport, Hatfield, UK, 7-8 April 2016

Analisi qualitativa di getti in alluminio pressocolati e sviluppo di correlazioni con i parametri di processo caratteristici

 A. Brotto, thesis at DTG , April 2016