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Structural response

Simulations can be performed in both linear and non-linear settings to analyze deformation. Linear simulations assess small, recoverable deformations, while non-linear simulations capture large, permanent deformations once the material exceeds its elastic limit. This approach helps identify both elastic behavior and non-linear plastic deformation until the material reaches its maximum strength, beyond which failure analysis is required.

Polymer simulation

In addition to metals, understanding the viscoelastic and hyperelastic properties of polymers is crucial, as they can relax over time and undergo significant deformation without failure. These behaviors must be accounted for in simulations using advanced material testing techniques.

While metals typically fail in a ductile manner, polymers may crack or fracture under stress. Our simulations can predict these failure modes, helping to identify potential cracking points before they occur, as illustrated in the image.

Highly non-linear problems

Non-linear structural simulations account for complexities such as material non-linearity (where material properties change with stress or strain), geometric non-linearity (significant deformations or large displacements), and boundary condition non-linearity (non-linear supports or contact interactions).

Carbon-fiber optimization

Carbon Fiber Reinforced Polymer (CFRP) Materials are subjected to various uncertainties during manufacturing and engineer can usually only have approximations regarding stiffness and strength along different directions. Optimization algorithm is employed to find the set of parameters that better validate experiments.

Electromagnetic simulation

An electromagnetic simulation is a computational technique used to model and analyze electromagnetic fields and their interactions with objects or materials. This type of simulation employs mathematical methods and numerical algorithms to solve Maxwell's equations, which describe how electric and magnetic fields behave and interact.

Application:

Modelling of electric motors
Antenna design
Electromagnetic compatibility (EMC)
Electromagnetic interference (EMI)

Static thermal simulations

Static heat transfer simulation refers to a simulation where the temperature distribution in a system is calculated assuming that it is in steady-state. This type of simulation is useful when the system being studied is expected to operate under steady-state conditions.

Transient thermal simulations

Transient heat transfer simulation refers to a simulation where the temperature distribution in a system is calculated over time. This type of simulation is useful when the system being studied is expected to experience changes in operating conditions periodically.

Structural response

Involves measuring the response of a structure to dynamic excitation using sensors (like accelerometers) and then extracting modal parameters (natural frequencies, mode shapes, damping ratios) from this response data.

Multibody dynamics experiment

Multibody dynamics experiment involve measuring body positions, velocities, accelerations, forces (both internal and external), and energy distributions to understand and optimize the behavior, stability, and performance of interconnected mechanical systems.

DropTower crash test

Drop height is up to 2 [m] with high speed 20.000 fps camera capability. This test is used to gain understanding over the behavior of the product during impact at high speeds.

Material testing

Material testing in combination with modelling plays a vital role in simulations. Tensile machines are equipped with force sensors and extensometers.

Capabilities:

Tensile / Compression tests
Relaxation tests
Creep tests

Material modelling

In 3DX calibration tool, all kinds of material tests can be calibrated from quasi-static to high strain rate applications:

Metals
Polymers
- Hyper-elasticity
- Visco-elasticity/plasticity
- High fidelity PRF models
Hyper/Crushable foams

Anomaly detection in machinery

Anomaly detection involves identifying patterns in data that do not conform to expected behavior. In machinery, this is crucial for early identification of potential issues before they lead to failures. Machine Learning models can be trained to recognize normal operating conditions and then detect deviations from these conditions that may indicate anomalies. Common techniques include:

Supervised Learning: Where models are trained on labeled data to distinguish between normal and abnormal states.
Unsupervised Learning: Where models identify anomalies based on the assumption that anomalies are rare and significantly different from normal data.
Semi-supervised Learning: A combination of both, using a large amount of unlabeled data along with a small amount of labeled data.

Condition monitoring

Condition monitoring is the process of continuously or periodically measuring and analyzing machine performance and health. ML enhances condition monitoring by:

Predictive Maintenance: Using historical and real-time data to predict when machinery is likely to fail, allowing for maintenance to be performed just in time.
Sensor Data Analysis: Leveraging data from various sensors (vibration, temperature, pressure, etc.) to monitor the health of machinery.
Trend Analysis: Detecting gradual degradation in machinery performance over time.

Fault diagnosis

Fault diagnosis involves identifying and locating faults in a system. ML techniques help in automating and improving the accuracy of fault diagnosis by:

Classification Models: Using supervised learning algorithms to classify types of faults based on features extracted from sensor data.
Pattern Recognition: Identifying fault patterns in large datasets that might be missed by traditional methods.
Root Cause Analysis: Determining the underlying causes of detected faults using data-driven approaches.

In summary, the application of ML in engineering for anomaly detection, condition monitoring, and fault diagnosis significantly enhances the efficiency and reliability of machinery operations, enabling proactive maintenance and reducing unexpected downtimes.

Modal response

Modal analysis is a technique used to study the dynamic characteristics of a structure by identifying its natural frequencies, mode shapes, and modal damping. During modal analysis, the structure's response to dynamic excitation (like a harmonic force, impact, or other disturbances) is observed and analyzed.

Harmonic analysis

Harmonic analysis solves linear dynamic problems with periodic loading by focusing on the steady-state response. Using the modal superposition approach, the system response is calculated by combining modal contributions across a frequency sweep. This simulation provides insight into resonance issues, fatigue hotspots or excessive vibration.

Random vibration

Products exposed to random vibrations, requiring a non-deterministic approach to analyze their performance. Unlike deterministic loads, random vibrations are assessed statistically using the Power Spectral Density (PSD) method, which describes the load’s nature over time rather than predicting exact values.

Drop & Impact analysis

Drop simulation is a type of analysis used to simulate the behavior of a product or object when dropped from a certain height or impact force. The goal of drop simulation is to determine the product's ability to withstand impact and identify potential weak or failure points.

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