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EnginSoft Extended Abstract

The Role of Manufacturing Process Simulation in the Data-DrivenDigital Twins

Nicola Gramegna
(EnginSoft S.pA., Italy);


Industry 4.0 is the industrial revolution based on Cyber-Physical-Systems (CPS) in the context of Factory of Future. The digital innovation is not an exclusivity of new and advanced production technologies or machines. 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, to accelerate the fine-tuning and real-time adjustment of the process parameters oriented to the zero defect quality.

An Intelligent Sensor Network (ISN) monitors the real-time production acquiring the multi-layers data from different devices and an extended meta- model (the Cognitive model) correlates the input and sensors data with the quality indexes, energy consumption cost function [7, 8]. The sensitivity and location of sensorized equipment is designed with the support of manufacturing process simulation. Process simulation, data management and training of the meta-model are key factors to generate an innovative Cognitive system to improve the manufacturing efficiency [9-11].

In the context of multi-stages production processes, metal and polymer 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 (e.g. smart foundry). 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, High Pressure Die Casting (HPDC), as well as plastic injection molding (PIM), 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 [7-13]. In both, sustainability issue imposes that the production cells are able to efficiently and ecologically support the production with higher quality, faster delivery times, and shorter times between successive generations of products.

The Overall Equipment Effectiveness and the Zero Defects Manufacturing approach are connected to Data-Driven Digital Twin and its Decision Support System (DSS) based on data mining and predictive models.

The digitalization in foundry and in plastic injection factory plays a key role in competitiveness introducing new integrated platform to Control the process and predicting in real-time quality and cost of castings [1-6].

The Digital Twin 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) can exploit the same methodology in different industrial contexts.

1. Manufacturing process simulation in design chain

Nowadays, CAE tools offer to designers a large number of efficient computational codes to create an innovative virtual design chain. Each phase of product development can be analyzed starting from the manufacturing process to thermal-mechanic fatigue life. Manufacturing process simulation is a key tool for designers in product development because the process determines the mechanical response of the component. This full integrated method, considering all life cycle aspects of a component since the design stage, is a winning arm for leveraging a company’s knowledge base and to improve competitiveness of a new product reducing time, cost and resources in product development.

Considering its industrial infrastructure, the area of manufacturing process simulation could be regarded as a separate domain of computation. The attention here is not purely focused on the product, as also the required equipment for the manufacturing processes have to be taken into account. Those CAE simulation methods, dedicated to all possible manufacturing processes, comprise among others:

  • Simulation of casting processes: it includes filling and solidification processes predicting the impacts on the material microstructure and the corresponding local mechanical properties as well as the residual stresses,
  • Simulation of forging processes: it includes metal forming simulations performed continuously or in several steps, taking into account material strain rate modelling and mechanical performance of the forging dies,
  • Plastic Injection-Molding Simulation: it includes filling and cooling phases as well as polymer-die interaction formation,
  • Simulation of machining processes: it includes chip-forming analysis, thermo-mechanical analysis of the material removal of the workpiece and the precision tools durability

Example, the casting process simulation for the defect prediction resulting from the cavity filling process or to solidification shrinkage, is therefore applied during the design phase including residual stresses and strains and themechanical properties prediction at the end of the production chain. Advanced micro-modelling can assist the prediction of microstructure and the corresponding local mechanical properties to be input of final virtual fatigue validation [5].

Figure 1: realistic performance of casting taking into account the effect of
microstructure and defects on mechanical properties and the final residual stresses.
(a) stress distribution with traditional approach, (b) stress distribution with design chain approach

In the additive manufacturing field, Powder Bed Fusion (PBF) techniques, like SLM, are considered enabling technologies for the production of complex-shape and lightweight components. Some of the typical challenges in this field involve build rates and the associated costs, the management of residual stresses and distortions, heat treatments and the proper design of support structures and their removal [13].

The virtual simulation tools in the design for additive manufacturing (DfAM) integrate the topological shape optimization according to the expected mechanical performance, with the orientation and the definition of the supports for the 3D printing process in order to calibrate the AM parameters and predict the final density and microstructure of the material (fig.2).

Figure 2 – Design for additive manufacturing: from topological optimization to the prediction of the microstructure

In addition to the metallurgical density and the possible anisotropy, the residual stresses and distortions of the printed part are fundamental outputs to zero defect manufacturing.

The design of industrial components that are produced by manufacturing processes (e.g. casting, forging, forming, machining, or additive), can take advantage using traditional structural and/or fluid-dynamic numerical codes in order to optimize the shape and the performance of the component [1-3].

The ever growing global competition will call for more and more applications of optimization techniques in various industrial sectors, necessary to reduce time- to-market, minimize the costs and improve quality [5-6].

2. The data-driven digital twin in production line

Digital Twin (DT) is a virtual model of a process, product or service. This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations. The digital twin connects digital and real worlds in real-time data exchange infrastructure. Typically, the DT application refers to the product life prediction under real actual or historic conditions, to the predictive maintenance of the equipment as well as to the quality prediction during the multi-stage production process.

In particular, the Data-Driven Digital Twin of Production Chain (fig. 3) constitutes of following tasks to

  • support different tracking systems to identify each component and location as well as the communication protocols with process machine and quality investigation systems
  • integrate multi-resolution and multi-variate process data monitored and gathered by a network of sensors or actuators collecting distributed control system, and advanced models linking process variables to ZDM
  • activate real time data analytics and knowledge-based reaction, the decision support system, at any process variation and quality risks,
  • implement automatic updating of process meta-model based on intelligent learning method with virtual and real-time manufacturing information,
  • calibrate the direct measurements and their correlation with process and product objective functions, typically fitting with the product requirements, to control the level of uncertainty for significant manufacturing process parameters,
  • visualise real-time data elaboration, alarm message and statistic production diagrams for multiple-users interfaces.

Figure 3 – Data-Driven Digital Twin of the Process Control & Quality management

The virtualization of manufacturing process undoubtedly provides considerable advantages. The role of process simulation considers the traditional and innovative application to

  • reduce the time of process verification and quality inspection minimizing the trial and error approach finding the optimal process setup,
  • design the sensorization of equipment defining the best sensors in the right sensible position to capture the process instability (fig.4),
  • study the extended boundaries conditions, with proper DoE, to predict equipment failures and component quality,
  • train the cognitive model based on fusion of virtual and real datasets with deeper knowledge of cause-effect correlations.

Figure 4 – the sensors introduced in the die to monitor the casting areas

Standardization of the Quality classification and investigation methods, as well as the part traceability, are fundamental to train the Quality predictive model guiding the minimization of relevant indexes affecting the scrap rate. All process parameters possibly affecting the quality of specific casting have been taken into account in the planned DoE, both virtual and real, correlating input process variables and data from sensors with quality indexes in the areas of interest. The extended DoE result is the correlation matrix showing correlation coefficients between sets of variables (fig. 5). The highest correlation shows the most significant pairs of values need to be monitored in the production line to control the process stability and their effect on quality.

Figure 5 - Correlation matrix based on virtual DoE of castings

Of course, simulated design of experiment (DOE) adopted to train the virtual meta-model is at "low cost" in term of time and resources; it typically constitutes the first model to be applied in production. The model needs to be trained with reference to a specific product and process stage, because the quantification of correlations are unique and not generalized.

3. Conclusions

In the industry 4.0 context, the “zero defect” target is always the first priority of the approach, to minimize the defects with real-time retrofit suggested by the Digital Twin platform. The scrap rate reduction focuses on those defect factors mainly contributing the overall quality requirements of the product. Being the energy consumption connected to the production rate, the cycle time optimization (more pieces per hour) and the improved management of energy- demanding devices lead to cost reduction.

The role of manufacturing process simulation, assisted by CAE tools, extends the value from product-process optimization to equipment sensorization and meta-modelling training. The Data-Driven Digital Twin integrate the virtual and real datasets in the training of cognitive meta-models. The decision is supported by cause-effect correlations, and proper reactions suggested by a continuously updated meta-model.

The application of Data-Driven Digital Twin has been demonstrated to some multi-stages traditional production lines. It can be exploited to further traditional or innovative manufacturing domains.

4. References

  1. Gramegna, N., Bucchieri, L.,Furlan, L. : “Integrated CAE development of innovative grey iron heat exchanger”, NAFEMS World Congress 2005 – Malta, 17th- 20th May 2005
  2. Gramegna, N., Merlo, R., Pirola, L.(2005): “The CAE design chain concept applied to automotive engine blocks”, 9th International Conference FLORENCE ATA 2005, May 11, 2005 - May 13, 2005
  3. Gramegna, N: “Using CAE tools to apply advanced materials and processes in automotive research”, Benchmark, April 2007
  4. Gramegna, N., Della Corte, E., Cocco, M., Bonollo, F., Grosselle, F., (2010) Innovative and integrated technologies for the development of aeronautic components. TMS2010, Seattle, 14-17 February 2010
  5. Gramegna, N., Della Corte, E., Poles, S. (2011): “Manufacturing process simulation for product design chain optimization”, Taylor&Francis, Material and Manufacturing Processes journal, GA issue 2011,
  6. Gramegna, N., Loizaga, I., Berrocal, S., Bonollo, F., Timelli, G., Ferraro; S.,(2012) “The multidisciplinary virtual product development integrates the influence of die casting defects in the mechanical response”, APMS 2012 International Conference, 24-26 September 2012
  7. Gramegna,N., Bonollo, F. (2013) : MUSIC project: intelligent management of manufacturing information to drive metal and plastic production line for injected components, Nafems Word Congress, 9-12 june 2013, Saltzburg, Austria
  8. Gramegna, N., Bonollo, F., (2014): The MUSIC guide to the key-parameters in High Pressure Die Casting Assomet servizi srl, Enginsoft SpA, ISBN 978- 8887786-10-1, 2014
  9. Gramegna, N., Bonollo, F., (2016) Smart Control and Cognitive System applied to the HPDC Foundry 4.0 Assomet servizi srl, Enginsoft SpA, ISBN 978-8887786-11-8, 2016
  10. Gramegna, N., & Bonollo, F., (2016). HPDC foundry competitiveness based on smart Control and Cognitive system in Al-alloy products. Metallurgia Italiana, (6), 21-24.
  11. Kujat, B., Gramegna, N., Benvenuti, M. (2016): Innovative control and real- time quality prediction for the casting production of aluminum alloy structural components at AUDI AG – HTDC 22-23 June 2016, Venice (Italy)
  12. Ansberg , L., Gramegna, N., & Bonollo, F., (2018): 20 years of research projects targeted to zero defect manufacturing in diecasting, 73WFC, 25 Sept2018
  13. Boscolo, D., Gramegna, N., Veronesi, P., Bolinauri, M.F., Baiocchi, F., Forghieri (2019): Generation of compensated geometry for Ti6Al4V Formula 1 aerodynamic parts produced by PBF, Int. CAE conference, 28-29th Oct. 2019, Vicenza, Italy