EnginSoft’s strategic approach to Industry 4.0

Key elements of Industry 4.0

Connection between the real and the virtual world, and human interaction

The EU’s Digital Europe Programme is based on key elements of Industry 4.0.

The PEOPLE, DIGITAL and Real PHYSICS worlds are key ingredients for the competitiveness and sustainability of a customer-oriented (the market place) Smart Factory.

EnginSoft invests in the human skills and competences to properly use Industry 4.0 tools. The company’s program of training and courses is integral to the transfer of knowledge to customers – some examples are its collaboration with European academic institutions and the TCN Consortium.

EnginSoft has digital in its DNA – right from the pioneering days of numerical modelling in the Eighties with CAE virtual prototyping.

A Decision Support System (DSS) along the value chain is mandatory to enable the effective adoption of Industry 4.0’s key enabling technologies (KETs) – advanced materials modelling, connected machines, data mining, cyber-physical systems (CPS), big data, Internet of things (IoT), augmented reality (AR), cloud computing, cybersecurity, etc. Obviously, the Digital Twin is the strategic connection between the digital and real worlds, and is geared towards minimizing scrap production (zero defect manufacturing, ZDM) and maximizing overall equipment efficiency (OEE). In this area, EnginSoft’s AI-driven technology supports resource and time usage, maintenance, configuration and automation, as well as quality forecasting. The Circular Economy links raw materials, eco-design, energy-aware processes, and the recycling and reuse of waste at the end of the product’s life.

 

  Digital solutions   Digital Twin categories   Digital approaches   Strategic trends   Advanced Research

Advantages and limitations of Industry 4.0

Key elements of Industria 4.0

While the well-recognized Industry 4.0 definition was developed in 2011 to describe the process of digitalization and interconnection in the factory of the future, only a limited percentage of companies have actually implemented the process of Digital Transformation. Similarly well-known is the series of advantages to be had and barriers to be encountered in tackling a 4.0 project.

EnginSoft has been helping its clients to overcome the barriers and to derive the earliest benefits due to its many years of multi-disciplinary experience:

  • ADVANTAGES: technology evolution; product innovation; production efficiency; professional growth
      EnginSoft's Proposal: the introduction and integration of innovative technologies in the smart factory; updating of the existing production line; development of innovative products that integrate the manufacturing process supported by the digital twin approach; monitoring production efficiency; evaluation of cost and time-to market; professional growth of existing resources in the context of Industry 4.0
  • LIMITS: lack of skills and vision; integration of skills; development times; investments
      EnginSoft's Mitigation Measures: on-the-job training and education; project planning in terms of short-to-medium-term objectives vs. investment; sustainable investment; digital assessment along the multi-departmental and supply chain.

Smart Factory strategy

The Smart Factory strategy develops and implements advances in measurement science that enable performance, quality, interoperability, wireless and cybersecurity standards for real-time prognostics and monitoring, control, and optimization of the integrity of smart manufacturing systems.

Smart Factories are complex manufacturing ecosystems in which the convergence of technologies and operational and ICT skills drive digital transformation.

Investment in Industry 4.0

Investment growth is estimated at 1,7% in the digital economy, and +40% of in the smart factory over the next 5 years, if the technological potential is unleashed through appropriate governance, transversal skills and a data-driven culture.

In 2024, the value of the smart factory could reach 1.4 trillion euros as a result of increasing productivity, extending market share, and improving quality and customer experience. However, , this will only be achievable if a strong governance program and a culture of data-driven operations are implemented.

The market for intelligent production platforms alone reached € 4 billion in 2019 and is expected to grow steadily over the next five years. According to the latest research, China, Germany and Japan are the top three countries in terms of smart factory adoption, closely followed by South Korea, the US nd France.

Two challenges are emerging: the convergence of IT and operating technology systems, and the expansion of the range of skills and capabilities needed to drive transformation, including cross-functional skills, transversal or soft skills, and digital talent.

  • It is highly advisable to invest significantly in digital platforms, the availability and integrity of data, cybersecurity expertise and governance, and to have a balanced approach between "efficiency in planning" and "effectiveness in operations", by exploiting the potential of data and collaboration. The next frontier is the efficiency of production systems as opposed to the productivity of labor. Secure data, real-time interactions, and connections between the physical and virtual world will make the difference.
  • To unlock the full potential of the smart factory, organizations must design and implement a strong governance agenda and develop a culture of data-driven processes to make better decisions based on available, reliable and meaningful data.

EnginSoft’s advanced research for Industry 4.0

EnginSoft is responding to the key priorities for 2020-2027

Introduction to the macro challenges ahead

Any EU manufacturing company needs to strive for excellence and competitiveness by improving productivity, quality management and time-to-market in accordance with global priorities corresponding to macro-challenges and impacts.

The innovative, high-tech product is the market’s answer to compliance with societal requirements and innovation along the product life-cycle. Manufacturing teams must be prepared to manage the new machine- and data-driven production controls reshaping human-machine interaction and decision making, while taking into account energy and resource efficiency. The final and most important impact will be the footprint for the planet.

Reducing manufacturing industry emissions is a must and can be achieved through waste reduction, recycling, renewable energy and resource efficiency.

Key elements of Industria 4.0

Future–Oriented Manufacturing

The 2018 World Manufacturing Forum Global Report identifies six areas of manufacturing that will shape its future development (cognitive, hyper-personalized, risk-resilient, circular, inclusive and rapidly-responsive).

Key elements of Industria 4.0

Six disruptive trends for the future of manufacturing (source: World Manufacturing Forum, www.worldmanufacturingforum.org)

EnginSoft is investing in some of the six disruptive trends for the future of manufacturing by introducing a digital approach for each of them.

The digital approach supported by EnginSoft is a fundamental element of inclusive and resilient manufacturing.

Key priorities 2020-2027 and key enabling technologies (KETs)

The 2020-2027 key priorities require innovative solutions that integrate the well-established key enabling technologies (KETs) of Industry 4.0.

 Excellent, responsive and intelligent factory

The robust product design, the simulation of manufacturing process and the digital quality approach proposed by EnginSoft is oriented to scalable lot-size first-time right manufacturing.

Agile manufacturing means adapting to customer needs in search of maximum flexibility in automation, and plug-and-play production lines supported by virtual commissioning or system engineering modelling. Robust, optimal manufacturing includes new materials or advanced technologies (e.g. additive manufacturing) that are virtually integrated and optimized taking into account the multi-stage production line and supply chain in a unique simulation workflow (the simulation-driven digital twin). In addition, the manufacturing digital twin regulates and predicts the quality and failures in production in sand factories for connected machines in real time.

  Parallel product and production engineering

The digital twin solution integrates end-to-end life-cycle engineering from product to production lines at different levels.

Innovative design is supported by CAE product performance verification, assembly and in-service capabilities with a model-based definition approach (e.g. chain of tolerance impact) and AI-driven solutions. Product-associated services (e.g. predictive maintenance of machines or lifespan prediction in a massive production) are designed with simultaneous, holistic and collaborative product-service engineering. The more complex and intelligent the product, the more important is the integrated process-to-product design chain (e.g. design for additive manufacturing).

  Ecological footprint, customer-oriented value networks

The innovative product responds to demand and consumer-oriented manufacturing networks with the estimation of the product footprint.

More and more design scenarios are proposed by the simulation-driven digital twin. The Pareto set of feasible solutions includes advanced materials, different configurations and manufacturing processes in a multi-objective workflow to overcome the contrasting objectives of mechanical performance and durability with the metrics of eco-sustainability, cost and resource-efficiency.

  Human-driven innovation

The traditional computer-aided technologies (CAx) and virtual tools or the complex digital twin are tools for people. Human beings are always the leaders of innovation, using digital tools supported by data analytics and decision support systems (DSS) (e.g. the data-driven digital twin).

The graphical user interface (GUI) is fundamental because human and digital technologies are complementary (e.g. the way human skills complement A.I. solutions) like a Cobot in robotics. The digital solution must involve all stakeholders in the factory and along the value chain to support decision making to manage constant change.

The DIGITAL TWIN as a KET in smart manufacturing

There are four new key enabling technologies (KETs) for future Smart Manufacturing: the digital twin (DT), intelligent process control (IPC), artificial intelligence (AI), and decision support systems (DSS)

Definition of the Digital Twin

A Digital Twin is a virtual and connected model of a real working system. The DT consists of three elements: intelligent process control (IPC), AI-driven modelling, and Decision Support Systems (DSS). It reproduces the behavior and history of the real system, allowing better resource management.

Intelligent Process Control (IPC)

If the DT is not classified as a single virtual model separated from real performance, it is ranked above other new KETs such as Intelligent Process Control. Machine-to-machine (M2M) connectivity and new sensors/alerts advance the collection of input data from the production system to be correlated with in-line inspection measurements in a comprehensive tracking framework referred to as Product ID or Cycle.

New KETs in Smart manufacturing

Artificial Intelligence (AI)

The application of deep data analytics (DDA) and machine learning (ML), already applied in other fields, interprets the complex and highly non-linear correlations between data to understand, predict and optimize the performance of a process, product or service. The first result is the automatic definition of alarm and warning thresholds; the final goal is the predictive model of product quality or the prediction of equipment failure to improve maintenance (e.g. maintenance as a service, MaaS). The training and re-training of predictive models often requires large amounts of data and the actual dataset could be very expensive in terms of time and resources. EnginSoft proposes the fusion of the virtual and real Design of Experiments (DoE) to accelerate the construction of suitable data set.

Decision Support System (DSS)

If data is the newest asset of smart manufacturing, data interpretation and visualization must be intelligent and fast to support decision making in case of performance deviations. The optimal new configuration of production parameters is based on a deeper cause-effect correlation to react quickly and correctly.

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Our Expertise in Industry 4.0 - Strategy and Research

CASE STUDY

Propeller design by means of multiobjective optimization: CP propeller test case

Validatipon of the results by means of an experimental campaign carried out at Genoa University Cavitation Tunnel

Modern propeller requirements involve many different characteristics, not limiting only to maximum efficiency, but considering also propeller cavitating behavior and, more and more, its side effects, in terms of radiated noise and pressure pulses.

marine modefrontier optimization

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CASE STUDY

Optimization of an automotive manufacturing system design taking into account regional requirements

Applying CAE to facilitate business CapEx decision making in the automotive manufacturing sector

In this case study, EnginSoft engineers explain how they used modeFRONTIER to assist Comau, a Fiat Chrysler subsidiary, to optimize their approach to the preliminary design of production systems for automotive manufacturing system RFQs.

automotive optimization rail-transport modefrontier SIMUL8 iphysics industry4

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CASE STUDY

How to optimize an external gear pump in highly constrained conditions

Meeting the engineering challenge of increasing efficiency while reducing costs

In this technical case study, EnginSoft assists Casappa to further refine an already-optimized standard series pump for an electrical motor and generator

mechanics optimization modefrontier

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Insights

Some software solutions for Industry 4.0 - Strategy and Research

software

ViveLab Ergo

Ergonomic verification in 3D virtual space

ViveLab Ergo is a high-performance cloud computing innovative simulation system that is perfectly capable of modeling machines, robots and people moving in a given physical environment.

vivelab automotive biomechanics

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software

smart ProdACTIVE

ICT platform to real-time QUALITY PREDICTION and OPTIMIZATION supported by TRAINED COGNITIVE MODE

The “smart Prod ACTIVE” tool 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.

industry4 metal-process-simulation optimization smartprod

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software

Ansys

Explore Pervasive Engineering Simulation

Ansys offers a comprehensive software suite that spans the entire range of physics, providing access to virtually any field of engineering simulation that a design process requires

ansys

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software

Maple

Advanced numeric and symbolic solution with a powerful maths engine

Maple is an advanced numeric and symbolic solution with a powerful maths engine

maple maplesim

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software

modeFRONTIER

The innovative integration platform for multi-objective and multi-disciplinary optimization

modeFRONTIER provides a seamless coupling with third party engineering tools, enables the automation of the design simulation process, and facilitates analytic decision making.

modefrontier optimization

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software

MapleSim

The advanced system-level modeling solution

MapleSim is the advanced system-level modeling solution based on the Maple mathematical engine and analysis environment to design and simulate multidomain systems, plants and controls in one single environment.

maple maplesim

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