Benefits of Digital Manufacturing

Advanced digital solutions and key enabling technologies must constantly be reoriented towards solving the problems of the manufacturing sector which can only return to competitiveness by increasing efficiency, reducing time-to-market, minimizing costs and increasing product and service quality.

EnginSoft’s experience confirms the relevant impacts that can be achieved with Digital Manufacturing solutions as predicted by the European Factories of the Future Association:

  • reduction of downtime and increased production efficiency,
  • implementation of zero-defect policies, improved product quality and management of non-conformities,
  • better inventory management and reduced time-to-market,
  • better management of resources and reduction of energy consumption.

Digital manufacturing outcomes

Expected impacts in terms of TIME, COST and QUALITY.

 

Digital manufacturing outcomes

Digital manufacturing driving business outcomes in detail: Reduced downtime – 48% | Reduction in defects – 49% | New product introduction – 23% | Overall Equipment Effectiveness improvement – 16% | Improved inventory – 35% | Reduction in energy use – 18%

(Source: European Factories of the Future Association, www.effra.eu.)

EnginSoft’s Digital Twin (DT) solutions

A Digital Twin is a virtual and connected model of a real working system. It reproduces the behavior and history of that system to enable better design, production and operational management of industrial processes, assets, and systems.

EnginSoft proposes various categories of DTs that represent different and distinctive application domains

 

Production Processes data-driven Digital Twin

Application of the Digital Twin approach
to advanced and intelligent control
of existing production processes

Production Systems Digital Twin

Application of the Digital Twin approach
to production systems

Asset-level simulation-driven Digital Twin

Application of the Digital Twin approach
to Product and maintenance of Production assets

 

Solving innovation challenges with the right combination of expertise

 

smart ProdACTIVE

The data-driven digital twin responds to the need for integrated cell control by moving the process control logic in real time to the individual phase by applying data analytics, alerting and A.I. on site

DT consists of three elements: intelligent process control (IPC), AI_driven modelling and Decision Support Systems (DSS)

The combined use of simulation (finite element analysis, FEA) and data analysis reduces costs and increases reliability for quality predictive models.

Using simulation you can understand the production process, identify significant process parameters and the corresponding intervals. Thereafter, you can implement targeted experimental design of experiments (DOEs) and collect the data needed to build the models at a lower cost.

By applying multi-variable analysis techniques you can create accurate predictive models and validate them. The models can then be integrated onto the machine by creating virtual sensors or suggesting the correct process parameters to the operator.

Data-driven Digital Twins:

  • support different tracking systems to identify each component and position, as well as communication protocols with process machines and quality control systems
  • integrate multi-resolution and multi-variable process data monitored and gathered by a network of sensors across the distributed control system, and advanced models that link process variables to zero defect manufacturing(ZDM)
  • activate real-time, knowledge-based reactions to any process variations and quality risks,
  • implement automatic updating of process meta-models based on intelligent learning methods with virtual and real-time production information,
  • calibrate direct measurements and their correlation with the process and target functions of the product, generally adapting the product requirements to control the level of uncertainty for significant parameters of the manufacturing process,
  • display real-time data processing, safety messages and statistics production diagrams for multi-user interfaces such as machine operators, production managers, quality engineers and plant directors.

 

IndustrialPhysics

Integrated design of automatic machines and robotic cells through virtual commissioning

Designing automatic machines and robotic cells with Virtual Commissioning

Virtual commissioning is applied to the integrated design of mechanics, robotics, and automation of machines, automatic lines, and systems for factory logistics.

The 3D CAD model of the automated machine or production system is used to create a virtual commissioning model, integrating the programming logic of plc and robot controllers, to provide a realistic simulation.

These models allow you to design the system and validate the automation (interaction between sensors, actuators and control software) before its realization, with the aim of simulating its real behavior and preventing problems at the time of commissioning (a "marriage" between the physical machine and the control system). In addition, augmented reality features allow the use of virtual commissioning models to support the sale and maintenance of automatic machines, and for simulation-based training.


 

Simul8

Design and optimization of systems and production lines with discrete event simulation (DES)

Design and optimize systems and production lines with discrete event and agent-based simulation

SIMUL8 is a simulation software for modeling, analyzing and optimizing the system-level performance of production systems during their design, reconfiguration, and production planning phases.
The simulation building blocks allow you to create accurate system models of complex system architectures, such as production lines, job shops, robotics cells, assembly systems and complex product flows. Creating a preliminary model requires limited information (for example starting from a process list and the 2D layout of the plant), while it is possible to progressively add detail to the simulation.
SIMUL8 models provide decision makers with critical information, such as:

  • resource utilization levels (machines, operators, etc.)
  • buffer saturation
  • the flow of materials and semi-finished products
  • the efficiency of the production system

The models can be easily connected to optimization algorithms by considering the output objectives (throughput, quality yield), and constraints (machine capacities, failure rates, shift patterns, and other factors affecting the total performance and efficiency of production systems).

Therefore, they allow you to evaluate the effects of different scheduling policies, batch and production flow management and operators, maintenance policies, the impact of system modification interventions (modification of the layout, replacement of machinery, interventions aimed at reducing the average return time of certain events, etc.), the effect of adding new products online, etc. and to identify the best compromise solutions in the face of conflicting objectives (e.g. costs vs. productivity).


 

MapleSim

Models for predictive maintenance based on data processing that can be implemented on the machine

Develop asset-level condition monitoring and predictive maintenance models

The combination of technologies for numerical and symbolic calculation (Maple) and a multi-domain simulation systems based on Modelica (MapleSim), allows you to create efficient simulation models that integrate mechanical, electrical, hydraulic, etc. components and their controls. Based on the ability to import 3D CAD models it is possible to accurately model articulated systems (kinematic chains) taking into account masses and inertia. The combination of these features enables accurate modeling that emulates the behavior of real systems.

The efficient generation of C code according to the functional mockup interface (FMI) standard allows you to export the models in the form of a functional mockup unit (FMU) and to support both model exchange and co-simulation by integrating other technologies. The resulting models can be integrated into the machine in the logic using software in the loop (SIL) by creating a digital twin for asset condition monitoring and predictive maintenance.


 

Ansys

Simulation will become an in-product experience in which the digital twin is an inherent part of the product’s design and operation, working alongside artificial intelligence and machine learning algorithms

As the product moves from design and manufacturing into operations, simulation can continue to play a pivotal role in delivering the best possible results in the field.

By using remote sensors to gather data on a product’s working conditions, analysts create a virtual replica — a digital twin — of that product and then apply the same physical forces and other environmental conditions to the digital model. Applying simulation as part of a digital twin can provide vital insights in the form of virtual sensors, in situations where no physical sensor exists or would even be possible. Simulation can also run what-if studies for optimal performance and can predict critical failure or maintenance requirements.
Simulation is also increasingly applied to the manufacturing phase, where it significantly improves the efficiency, cost-effectiveness and flexibility of production. With the rise of mass customization of products — made possible by additive manufacturing, or 3D printing — simulation helps ensure that the finished product has the optimal shape and is made accurately, cost-effectively and with a high degree of consistency over time.


 

modeFRONTIER

Prediction of the best configurations to obtain a good part on the first attempt through simulations guided by multi-objective optimization – the simulation-driven digital twin


 

ViveLab Ergo

Interaction between operator and machine / automation through efficiency and ergonomics simulation systems


 

CETOL 6σ

Forecasts of the functionality of the assembled product based on dimensional specifications (chain of tolerances) and integration with metrological checks by the quality department

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Our Expertise in Industry 4.0 - EnginSoft’s approach to Digital Manufacturing

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Insights

Some software solutions for Industry 4.0 - EnginSoft’s approach to Digital Manufacturing

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.

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ViveLab Ergo

Ergonomic verification in 3D virtual space

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industrialPhysics

Digital engineering, simulation and augmented reality for machines, production lines design and plant logistics

industrialPhysics is an innovative simulation platform for the digital design and virtual commissioning of complex production machines, lines and plants.

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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.

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Maple

Advanced numeric and symbolic solution with a powerful maths engine

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

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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

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