Introduction: A New Era of Engineering

Artificial intelligence is moving beyond chatbots, image generators, and general-purpose digital assistants. In engineering, AI is beginning to influence how products are conceived, designed, tested, optimized, and prepared for manufacturing.

Industrial AI represents this next stage of digital transformation. Rather than generating content alone, it combines artificial intelligence with engineering knowledge, scientific principles, simulation technologies, and industrial data to support decisions that affect real-world products and systems.

Dassault Systèmes is pursuing this vision through its CATIA design and engineering portfolio, the 3DEXPERIENCE platform, and its Virtual Twin technology. The objective is to connect human expertise with intelligent digital capabilities throughout the product development lifecycle.

For engineers, CAD designers, simulation specialists, and manufacturing organizations, this approach could change not only how quickly products are developed, but also how engineering knowledge is applied and reused.

What Is Industrial AI?

Industrial AI is the application of artificial intelligence to engineering, manufacturing, scientific research, infrastructure, and other complex industrial environments.

Traditional AI applications can generate text, recognize patterns, or produce images. Industrial applications face additional requirements: their outputs must be relevant to physical systems, engineering constraints, safety considerations, and manufacturing requirements.

For example, generating a visually convincing mechanical component is not enough. The component may also need to satisfy dimensional constraints, withstand specified loads, fit into an existing assembly, and remain feasible to manufacture.

Industrial AI aims to connect intelligent automation with this engineering context.

Key capabilities include:

• Engineering knowledge integration: Using established engineering rules, product information, and organizational expertise.
• Design automation: Accelerating repetitive modeling and product development activities.
• Simulation-assisted decision-making: Evaluating design alternatives using modeling and simulation workflows.
• Knowledge reuse: Making existing engineering information easier to find, understand, and apply.
• Lifecycle collaboration: Connecting design, systems engineering, simulation, and manufacturing within a shared digital environment.

The distinction is important. Industrial AI is not simply about adding an AI chatbot to CAD software. It is about integrating AI into engineering workflows where context, verification, and traceability matter.

Why Is Dassault Systèmes Focusing on Industrial AI?

Modern products are increasingly multidisciplinary. An electric vehicle, for instance, combines mechanical structures, electrical systems, embedded software, thermal management, sensors, and safety requirements.

These interconnected systems create significant challenges for engineering teams. A change to one component can affect several other parts of the product, requiring additional analysis, validation, and coordination.

Dassault Systèmes is addressing this complexity by connecting CATIA design capabilities with simulation, systems engineering, product data, and Virtual Twin technologies through the 3DEXPERIENCE ecosystem.

The broader strategy is to move from isolated digital tools toward a connected engineering environment in which people and AI-powered systems can collaborate using a common product context.

This approach is particularly relevant to industries such as automotive, aerospace, industrial equipment, high technology, and advanced manufacturing, where product complexity and development costs are substantial.

CATIA and the Evolution of AI-Powered Engineering

CATIA has a long history in 3D design, surface modeling, mechanical engineering, and complex product development. Its role is expanding as AI becomes more closely integrated with design and engineering processes.

The goal is to help engineers spend less time navigating software, searching for information, and performing repetitive operations—and more time evaluating alternatives, solving technical problems, and improving product performance.

Three concepts are particularly important to this evolution:

1. Virtual Companions: AI-powered collaborators that support engineering, scientific, and business activities.
2. Generative Experiences: AI-driven capabilities that help transform inputs and engineering intent into structured design or engineering outputs.
3. Virtual Twins: Digital representations of products and systems that provide context for design, simulation, analysis, and optimization.

Together, these concepts form an approach in which AI can assist with more than individual commands. It can help coordinate tasks and connect engineering information across different stages of product development.

Virtual Companions: More Than Traditional AI Assistants

One of the most notable elements of Dassault Systèmes’ Industrial AI strategy is its introduction of Virtual Companions on the 3DEXPERIENCE platform.

Unlike a conventional assistant that primarily answers questions, these AI-powered companions are designed to work within industrial workflows, use relevant organizational knowledge, and support multi-step activities.

The platform’s announced Virtual Companions include AURA, LEO, and MARIE.

AURA — The Business Expert

AURA focuses on business knowledge, organizational information, and enterprise know-how.

In an engineering organization, important information is often distributed across documents, projects, processes, and teams. Finding the right information at the right time can be difficult, particularly in large organizations with established products and extensive engineering histories.

AURA is designed to help users discover and apply this knowledge more effectively.

Potential applications include finding relevant project information, reusing established practices, and helping teams navigate organizational knowledge.

LEO — The Engineering Expert

LEO focuses on engineering activities and technical problem-solving.

Its intended role is to assist engineering teams with understanding existing designs, navigating product structures, and carrying out engineering tasks through connected CATIA and SIMULIA workflows.

For example, an engineer working on a mechanical assembly might need to understand how components relate to one another, identify relevant design information, or evaluate possible modifications.

An AI companion operating within the engineering environment could help coordinate these activities while using the available product context.

The intended benefit is not to remove the engineer from the process. Instead, it is to extend engineering capacity and reduce the effort required for routine or information-intensive work.

MARIE — The Scientific Expert

MARIE focuses on scientific knowledge and research-oriented activities.

Scientific and engineering problems frequently require knowledge that extends beyond geometric design, including material behavior, physical principles, and complex relationships between variables.

A scientific companion can help users access relevant expertise and support research workflows in which scientific understanding is essential.

Its role complements the broader Industrial AI vision by connecting scientific reasoning with engineering and industrial applications.

Availability and supported capabilities can vary by platform release, subscription, and deployment. Consult the official product documentation for current details.

Generative Experiences: Turning Engineering Intent into Action

Another important element of the CATIA Industrial AI approach is Generative Experiences.

In conventional workflows, engineers often translate design intent into a series of individual operations. They create geometry, define relationships, configure assemblies, document requirements, and check whether the resulting design satisfies the intended constraints.

Generative Experiences aim to automate parts of these processes by converting different forms of input into structured engineering outputs.

Depending on the available capabilities, this can include:

• Generating editable mechanical parts from multimodal inputs.
• Creating assemblies based on predicted mechanical interfaces.
• Generating engineering rules from existing knowledge.
• Transforming engineering documentation into structured systems models.
• Capturing engineering knowledge in reusable templates.

Consider an engineer who needs to create a component from a combination of written requirements, reference images, and existing technical information.

An AI-supported workflow could help interpret the input, propose a suitable model, and generate an editable starting point. The engineer would then review the geometry, verify dimensions, evaluate constraints, and confirm that the result meets the intended requirements.

This distinction is essential: generating a model is only one stage of engineering. Validation, manufacturability, performance, and compliance remain critical.

Virtual Twins: The Engineering Context Behind Industrial AI

AI can be powerful, but its usefulness in engineering depends heavily on the quality and relevance of the information available to it.

This is where Virtual Twin technology becomes important.

A Virtual Twin is a digital representation of a physical product, system, or process. Depending on the application, it can incorporate 3D geometry, engineering properties, simulation results, system relationships, and other relevant information.

Within the 3DEXPERIENCE environment, Virtual Twins can help connect design decisions with engineering analysis and product lifecycle information.

How Virtual Twins Support AI

Imagine developing a new electric vehicle component.

A conventional design process might involve creating the geometry, preparing simulation models, reviewing performance, and modifying the design when issues are discovered.

An AI-supported Virtual Twin workflow could help engineers explore alternatives, identify relevant information, coordinate simulation activities, and compare possible solutions earlier in the development process.

The workflow might include:

1. Creating or modifying the initial 3D design.
2. Connecting the model with relevant engineering requirements.
3. Evaluating alternative configurations using appropriate simulations.
4. Reviewing performance, material, and manufacturing constraints.
5. Refining the design based on engineering results.
6. Maintaining traceability between requirements, decisions, and validated outputs.

The potential benefit is a more connected process in which design and validation inform one another continuously.

Virtual Twins do not automatically guarantee that a product will perform as expected. Their value depends on model quality, appropriate assumptions, reliable data, and suitable validation methods.

Industry World Models: Connecting AI with Physical Reality

A further concept in Dassault Systèmes’ Industrial AI strategy is the Industry World Model.

General-purpose AI systems learn patterns from data. Industrial engineering, however, requires reasoning that is grounded in the behavior of physical systems and the constraints of specific industries.

Industry World Models are intended to provide this context by combining industrial knowledge, engineering relationships, scientific principles, and Virtual Twin information.

This approach is particularly important when the AI is supporting decisions that affect product performance or manufacturing feasibility.

For example, an AI system proposing changes to a mechanical component should consider more than its visual appearance. Relevant factors may include loading conditions, interfaces with neighboring components, material properties, manufacturing processes, and applicable requirements.

Combining AI with engineering models and simulation can help make these recommendations more useful and easier to evaluate.

However, the reliability of a recommendation still depends on the quality of the underlying data, the validity of the models, and the verification process.

Industrial AI and MODSIM: Bringing Design and Simulation Closer Together

Modeling and simulation are fundamental to modern product development.

Traditionally, design and simulation activities can involve separate tools, data preparation steps, and specialist workflows. This can introduce delays when design changes require models to be updated and analyses to be repeated.

Dassault Systèmes’ MODSIM approach brings modeling and simulation closer together within its engineering ecosystem.

When combined with Industrial AI, this creates opportunities to explore design alternatives and evaluate performance more efficiently.

For example, a designer developing a lightweight bracket could use an AI-supported workflow to explore different geometries. Appropriate simulation tools could then help assess stress distribution, deformation, or other relevant performance criteria.

The engineer can compare alternatives and make informed decisions based on the results.

AI may accelerate exploration, while physics-based simulation provides a means of evaluating whether proposed solutions satisfy the required conditions.

This combination is particularly valuable when several competing objectives must be balanced, such as weight, stiffness, cost, manufacturability, and durability.

Practical Applications Across Industries

Industrial AI is relevant to a wide range of engineering disciplines.

Automotive Engineering

Automotive development involves mechanical design, electrical systems, software, safety requirements, and manufacturing constraints.

AI-supported engineering could help teams navigate complex product structures, explore design alternatives, and connect component changes with relevant validation activities.

Virtual Twins can also support the evaluation of product configurations before physical prototypes are built.

Aerospace and Defense

Aerospace programs involve complex assemblies, stringent requirements, and extensive validation processes.

Industrial AI could assist with engineering knowledge reuse, requirements management, design exploration, and the coordination of technical information.

Human oversight and rigorous verification remain essential because aerospace products must satisfy demanding safety and certification requirements.

Industrial Machinery

Manufacturers of machinery and industrial equipment frequently develop customized products with many interacting components.

AI-supported design and assembly workflows could help reduce repetitive modeling activities and improve the reuse of existing engineering solutions.

This can be particularly useful when product families share common components but require customer-specific configurations.

Product Design and Manufacturing

Industrial designers and manufacturing engineers must balance appearance, performance, cost, and production feasibility.

By connecting design tools with engineering knowledge and simulation, Industrial AI could help teams identify potential problems earlier and explore alternatives more efficiently.

Systems Engineering

Modern products increasingly combine mechanical, electrical, electronic, and software components.

AI-supported systems engineering could help organize requirements, establish relationships between system elements, and maintain consistency as designs evolve.

This is especially relevant to software-defined products, where changes in software behavior can influence hardware architecture and overall system performance.

Will Industrial AI Replace Engineers?

The more useful question is how AI will change

Computer-Aided Engineering (CAE) is transforming how engineers design, test, and optimize products. By using advanced computer simulations and numerical methods, engineers can predict how a product will behave under real-world conditions before manufacturing a physical prototype.

From automotive crashworthiness and aircraft structural integrity to fluid dynamics, heat transfer, and electromagnetic performance, CAE helps companies evaluate complex engineering challenges in a virtual environment.

Traditionally, product development relied heavily on physical prototypes and repeated laboratory testing. Today, CAE allows engineering teams to investigate thousands of design variations, identify potential weaknesses earlier, and make more informed decisions throughout the product development process.

However, simulation is not a complete replacement for physical testing. Reliable engineering depends on accurate models, validated simulation results, and physical tests where necessary.

1. What Is CAE (Computer-Aided Engineering)?

CAE refers to the use of computer-based tools to analyze, simulate, and optimize engineering designs and processes.

CAE software uses mathematical models and numerical methods to predict the behavior of components, assemblies, and complete systems under specified operating conditions.

The most common CAE disciplines include:

• Finite Element Analysis (FEA): Evaluates stress, strain, deformation, vibration, fatigue, and structural integrity.
• Computational Fluid Dynamics (CFD): Simulates fluid flow, turbulence, pressure distribution, and aerodynamic performance.
• Thermal Analysis: Predicts heat transfer, temperature distribution, and thermal expansion.
• Electromagnetic Simulation: Analyzes electric and magnetic fields, motors, sensors, and electromagnetic compatibility.
• Multiphysics Simulation: Examines how multiple physical phenomena interact within the same system.
• Optimization and Durability Analysis: Helps engineers improve performance, reduce material usage, and estimate product life.

By integrating these capabilities into product development, companies can detect design weaknesses earlier and reduce the number of costly development iterations.

2. The Evolution of CAE Technology

CAE has evolved from specialized numerical calculations into an essential part of modern digital engineering.

Early Engineering Analysis

Early computer-based engineering analysis focused primarily on structural calculations and finite element methods. These techniques enabled engineers to solve increasingly complex problems that would have been difficult or time-consuming to evaluate manually.

Growth of Commercial Simulation Software

As computing power improved, commercial CAE platforms expanded into nonlinear structural analysis, fluid dynamics, thermal simulation, crash analysis, and other specialized disciplines.

Industries such as aerospace, automotive, energy, and heavy manufacturing began using simulation more extensively to support product development and safety engineering.

Integrated Digital Engineering

Modern CAE tools increasingly integrate with CAD, product lifecycle management (PLM), high-performance computing, and cloud-based workflows.

Engineers can now transfer design geometry into simulation environments, automate repetitive analyses, compare multiple design alternatives, and collaborate across engineering teams.

The Future: AI-Assisted Simulation

Artificial intelligence and machine learning are beginning to support engineering simulation through faster design exploration, surrogate models, automated optimization, and improved workflows.

These technologies can reduce the computational effort required for selected tasks, although high-fidelity physics-based simulation and engineering validation remain essential for many applications.

3. Leading CAE Software Used in Industry

Several established software platforms support different engineering disciplines. The right choice depends on the physics being analyzed, the required accuracy, the industry, and the existing engineering workflow.

3.1 ANSYS

ANSYS is a widely used engineering simulation ecosystem covering multiple disciplines, including structural mechanics, computational fluid dynamics, thermal analysis, and electromagnetics.

Its products support applications ranging from component-level stress analysis to complex multiphysics engineering problems.

Key capabilities:

• Structural analysis using finite element methods.
• CFD for aerodynamic and fluid-flow investigations.
• Thermal and coupled-field simulation.
• Electromagnetic analysis for electrical and electronic systems.
• Design exploration and optimization.

Typical applications: Automotive engineering, aerospace, energy, electronics, industrial machinery, and product development.

Official website: https://www.ansys.com/

3.2 Abaqus (Dassault Systèmes)

Abaqus is particularly well known for advanced finite element analysis and challenging nonlinear engineering problems.

It is frequently used when material behavior, contact interactions, large deformation, impact, or complex structural responses require detailed investigation.

Key capabilities:

• Nonlinear structural analysis.
• Large deformation and contact simulation.
• Impact and dynamic event analysis.
• Advanced material modeling.
• Applications involving damage and failure, depending on the selected model and solver capabilities.

Typical applications: Automotive crashworthiness, aerospace structures, rubber and elastomer components, composite materials, and industrial product development.

Official website: https://www.3ds.com/products/simulia/abaqus

3.3 MSC Nastran (Hexagon)

MSC Nastran has a long history in computational structural analysis and is widely used in industries where structural performance, vibration, and durability are critical.

It supports static, dynamic, thermal, and other structural analysis workflows, including linear and nonlinear applications.

Key capabilities:

• Structural stress and deformation analysis.
• Modal and vibration analysis.
• Dynamic response calculations.
• Buckling and fatigue-related engineering workflows.
• Structural optimization and large-model analysis.

Typical applications: Aerospace, automotive, energy, transportation, and complex mechanical structures.

Official website: https://hexagon.com/products/msc-nastran

3.4 COMSOL Multiphysics

COMSOL Multiphysics is designed for modeling and simulating physical systems in which different physical phenomena interact.

For example, an engineer can study how heat generation affects electrical performance, how fluid flow influences heat transfer, or how chemical reactions interact with temperature and pressure.

Key capabilities:

• Coupled multiphysics simulation.
• Heat transfer and fluid-flow analysis.
• Electromagnetic and structural modeling.
• Chemical reaction and transport modeling.
• Custom equations, models, and simulation applications.

Typical applications: Electronics cooling, battery development, energy systems, chemical processing, sensors, and scientific research.

Official website: https://www.comsol.com/comsol-multiphysics

3.5 OpenFOAM

OpenFOAM is a free, open-source computational fluid dynamics (CFD) software toolkit. Its open-source nature gives users access to its code and enables extensive customization.

It is used by researchers, universities, engineering consultants, and industrial teams for a broad range of fluid-flow and related simulation problems.

Key capabilities:

• Fluid-flow and turbulence simulation.
• Heat transfer and reacting-flow analysis.
• Custom solver development.
• Flexible computational workflows.
• Integration into research and engineering automation pipelines.

Typical applications: Aerodynamics, thermal management, process engineering, energy systems, marine engineering, and academic research.

OpenFOAM can reduce software licensing costs, although users may need additional expertise in numerical methods, setup, scripting, and solver configuration.

Official website: https://www.openfoam.com/

4. Comparison of Leading CAE Software

These tools are not direct substitutes in every situation. Some focus on specific engineering disciplines, while others offer broader simulation environments. Many organizations use several CAE tools together to cover their complete analysis requirements.

5. How CAE Works: From CAD Geometry to Engineering Results

A typical CAE workflow involves several stages.

Step 1: Import or Prepare the CAD Model

Engineers begin with a three-dimensional model created in a CAD system. Depending on the analysis, unnecessary geometric details may be removed to simplify the simulation.

Step 2: Define Materials and Physical Properties

The model is assigned relevant properties, such as elastic modulus, density, thermal conductivity, heat capacity, or fluid viscosity.

For advanced simulations, engineers may need nonlinear material data, temperature-dependent properties, or experimental material characterization.

Step 3: Create the Computational Mesh

The geometry is divided into smaller computational elements or cells. In structural FEA, these elements approximate the behavior of the physical structure. In CFD, the mesh represents the domain through which fluid-flow equations are solved.

Mesh quality and resolution can significantly influence accuracy and computational cost.

Step 4: Apply Loads and Boundary Conditions

Engineers define operating conditions, including forces, pressure, temperature, supports, fluid inlets, outlets, and other constraints.

Incorrect boundary conditions can produce misleading results, even when the software solves the equations correctly.

Step 5: Run the Simulation

The solver calculates the model response using the selected numerical methods. Complex simulations may require substantial computing resources, especially when nonlinear behavior, transient events, or detailed fluid dynamics are involved.

Step 6: Evaluate and Validate the Results

Engineers review results such as stress distribution, deformation, temperature, pressure, velocity, and fatigue life.

The findings should be checked for numerical convergence, physical plausibility, sensitivity to assumptions, and consistency with analytical calculations or experimental measurements where appropriate.

Step 7: Optimize the Design

The analysis results can guide changes to geometry, materials, wall thickness, cooling channels, or other design parameters. The revised model can then be simulated again to determine whether the changes improve performance.

6. The Impact of CAE on Modern Industry

6.1 Reducing Physical Prototype Costs

Physical prototypes and destructive tests can be expensive, especially when testing complete vehicles, aircraft structures, or large industrial systems.

CAE allows engineering teams to investigate many design variations virtually before committing to physical manufacturing and testing.

For example, virtual crash analysis can help automotive engineers evaluate structural deformation, energy absorption, and occupant-protection concepts before conducting physical crash tests.

This approach can reduce the number of prototypes required and identify design problems earlier. Nevertheless, physical crash testing and other validation activities remain important for confirming simulation predictions and meeting applicable safety requirements.

6.2 Lightweight Design and Topology Optimization

Weight reduction is a major engineering objective in automotive and aerospace development.

Topology optimization and related computational design methods help engineers identify where material may be reduced or redistributed while meeting specified constraints.

When combined with CAE, these methods can support lighter structures without sacrificing required stiffness, strength, durability, or safety.

Potential benefits include:

• Reduced vehicle mass and improved energy efficiency.
• Lower aircraft structural weight.
• More efficient material usage.
• New geometries suited to advanced manufacturing methods.
• Better trade-offs between performance, cost, and durability.

AI-assisted methods can help explore design alternatives, but optimized results must still satisfy engineering constraints and be verified through appropriate analysis and testing.

6.3 Improved Product Reliability and Durability

Products often operate under demanding conditions, including extreme temperatures, repeated mechanical loading, vibration, pressure, moisture, and corrosive environments.

CAE enables engineers to investigate these conditions during development and identify potential failure mechanisms.

Examples include:

• Evaluating fatigue life in rotating machinery.
• Studying thermal stress in electronic components.
• Analyzing vibration in aerospace structures.
• Predicting pressure-related stresses in industrial equipment.
• Assessing the behavior of materials under impact or large deformation.

These analyses help engineers make better reliability decisions and improve product durability. They cannot guarantee zero failures, but they can reduce uncertainty and help identify risks before products enter service.

6.4 Faster Product Development

Simulation enables engineers to compare design alternatives without manufacturing a separate physical prototype for every variation.

Automated workflows can run multiple analyses, compare results, and identify promising configurations. This can shorten development cycles and allow teams to investigate more alternatives within the same project timeline.

The greatest benefits typically occur when simulation is integrated early into the design process rather than used only to investigate problems after a design is nearly complete.

6.5 Better Thermal and Energy Performance

Thermal simulation and CFD are essential in applications where heat transfer, airflow, or fluid movement influences product performance.

Engineers can investigate battery cooling, electric motor temperatures, electronic device cooling, HVAC systems, engine thermal management, and industrial heat exchangers.

By understanding temperature distribution and fluid behavior, engineering teams can improve energy efficiency, reduce overheating risks, and develop more effective cooling solutions.

6.6 Supporting Sustainable Engineering

CAE can contribute to sustainability by reducing material waste, limiting unnecessary prototype production, and improving the energy efficiency of products.

For example, aerodynamic optimization can help reduce drag, while thermal analysis can support more efficient cooling systems.

The environmental benefit depends on the complete product lifecycle, including manufacturing processes, computing resources, material selection, product use, and end-of-life considerations.

7. CAE Applications in the Automotive and Aerospace Industries

Automotive Engineering

Automotive manufacturers use CAE throughout vehicle development, including:

• Crashworthiness and occupant safety.
• Body structure strength and stiffness.
• Suspension and chassis performance.
• Aerodynamics and drag reduction.
• Battery thermal management in electric vehicles.
• Noise, vibration, and harshness (NVH).
• Fatigue life and durability.

In electric vehicles, simulation is particularly important because battery packs, electric motors, power electronics, and cooling systems must work together under changing operating conditions.

Aerospace Engineering

Aerospace applications demand careful evaluation of structural integrity, aerodynamic performance, weight, and environmental loads.

CAE supports:

• Aircraft structural and fatigue analysis.
• Aerodynamic simulation.
• Thermal protection and heat transfer studies.
• Vibration and dynamic response analysis.
• Composite material evaluation.
• Turbomachinery and propulsion development.

Because aerospace components may face severe operating conditions and strict certification requirements, simulation results must be supported by appropriate verification, validation, and physical testing.

8. Understanding the Difference Between CAD, CAE, CAM, and FEA

CAD, CAE, and CAM form complementary parts of the digital engineering workflow.

FEA and CFD are not separate alternatives to CAE in the broad sense. They are important methods and disciplines used within the wider engineering simulation field.

A simplified digital product-development workflow looks like this:

CAD design → CAE simulation → Design optimization → CAM programming → Manufacturing → Physical validation

In practice, this process is iterative. Simulation may reveal a design issue that requires changes in CAD, followed by another round of analysis before manufacturing proceeds.

9. The Future of CAE: AI, Automation, and Digital Twins

CAE technology continues to evolve as computing power, automation, and data-driven methods improve.

AI-Assisted Engineering Simulation

AI can help engineers identify promising design configurations, automate repetitive tasks, and create approximate models that predict selected simulation outcomes much faster than a full numerical solver.

These methods are especially useful when exploring large design spaces. However, AI-generated predictions must be assessed for accuracy and applicability, particularly when they are used outside the conditions represented in their training data.

High-Performance and Cloud Computing

Large-scale simulations can require significant processing power and memory. High-performance computing (HPC) and cloud-based r

What’s New in Moldex3D 2025?

1. Smarter Simulations with AI & Machine Learning

Moldex3D 2025 integrates deep learning algorithms to transform raw simulation data into actionable intelligence. Key AI-powered features include:

• AI Optimization Wizard: Automatically recommends optimal process parameters for single or multi-objective designs, drastically reducing trial-and-error.
• Gate & Mold Design Discovery: Predicts ideal gate locations, injection machine selection, and clamping force distribution based on historical data.
• MoldiBot Virtual Assistant: Provides instant troubleshooting and real-time recommendations during simulation setup and analysis.

2. Unmatched Precision in Injection Molding Simulation

The 2025 release enhances accuracy with:

• Advanced Thermal Pin Simulation: Accurately models temperature distribution in thermal pins, preventing warpage and cooling defects.
• Crystallinity Prediction: Simulates post-crystallization effects in warpage and annealing, enabling early defect prevention.
• Wire Debonding Analysis (IC Packaging): Identifies risks of wire detachment in semiconductor packaging, improving reliability.

3. Faster Preprocessing & Workflow Automation

• Automatic Multi-Component Matching: Simplifies mold assembly simulations with intelligent component recognition.
• One-Click Cooling Channel Repair: Automatically detects and fixes disconnections, overlaps, and intersections in cooling lines.
• iSLM Auto Launch: Upload CAD files and launch simulations with a single click, reducing manual setup time by up to 70%.

4. Enhanced Visualization & Reporting

• 3D Fiber & Flake Orientation Visualization: Delivers clearer insights into material behavior.
• Interactive DOE (Design of Experiments): Adjust parameters in real-time and instantly see predicted outcomes.
• Smart Verification Templates: Standardize CAE reports with predefined evaluation criteria and automated result validation.

5. Seamless Integration & Scalability

• Expanded API Support: Enables custom automation for Shell, injection machines, and validation templates.
• Cloud & High-Performance Computing (HPC) Compatibility: Faster simulations with distributed computing.
• 66% Smaller File Sizes: Optimized storage and faster data transfer.

Why Upgrade to Moldex3D 2025?

Manufacturers adopting Moldex3D 2025 will benefit from:
 30% Faster Time-to-Market with AI-driven optimization.
 Higher First-Pass Yield due to precise defect prediction.
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Availability & More Information

Moldex3D 2025 is now available for global customers. For detailed feature breakdowns, case studies, and demo requests, visit:

 https://www.moldex3d.com/products/moldex3d-2025

About CoreTech System (Moldex3D)

Since 1995, CoreTech System has been at the forefront of plastic injection molding simulation, helping industries worldwide optimize design, reduce waste, and improve manufacturing efficiency. With Moldex3D, companies across automotive, medical, electronics, and consumer goods sectors achieve higher product quality and faster innovation cycles.