Isight provides the tools to create simple, yet powerful, simulation workflows which automatically link processes and software applications in an optimised system. With Isight, you can improve efficiency, accelerate product design and reduce manual errors.

Main Features

Design of Experiments

Isight enables engineers to quickly assess the impact of various design variables based on a set of objectives.

Design Optimisation

Isight provides a comprehensive selection of parallelised optimisation techniques that can be applied to a variety of problems.

Data Machining

This process enables simulation models to be calibrated by minimizing any variety of different error measures using optimisation techniques.  

Quality Methods

Isight provides stochastic methods that account for variation in product designs and their operating environment.

Functionalities of SIMULIA Isight

Interfaces

Work quickly and efficiently with highly visual and intuitive interfaces, wizards, and drag-and-drop capabilities.
  • Design Gateway: Quickly create integrated simulation process flows that couple simulation programs regardless of discipline, programming language, or format using this intuitive graphical user interface.
  • Runtime Gateway: Visualise results with engineering process flows, graphs and tables. The user can choose between two execution engines: a desktop execution engine, or a distributed execution engine based.
  • Component Development Environment: Add custom techniques, including DOE, approximation, approximation error analysis, optimization and Monte Carlo sampling methods, and random variable distributions.

Process Integration

Build simulation process flows and exchanging data with external sources.
  • Abaqus: Quickly and automatically exchange data with Abaqus input, .cae, .odb, and .dat files.
  • Dymola: Quickly and automatically exchange data with Dymola input dsin.txt files and output with .mat files.
  • CATIA V5: Exchange data with CATIA V5 models, update a CATIA V5 model, and export native and neutral file formats.
  • Microsoft Office Components Word™ and Excel™: Send Isight parameter values and results directly to a preformatted Microsoft Word document—an excellent way to quickly share results with others in the organisation.
  • Email: Send Isight parameter values and results directly to a preformatted email message that will be automatically sent at a
    predetermined point in a process flow.
  • MATLAB: Integrate MATLAB® files with Isight to allow parameters to be read from or written to MATLAB® scripts.
  • Mathcad: Extract Mathcad parameters and to execute a Mathcad worksheet.
  • COM (Microsoft Component Object Model): Directly communicate with COM objects.
  • Data Exchanger, OS Command, and Simcode: Data Exchanger enables easy data exchange between ASCIIbased files. The XML component enables easy data exchange between XML formatted files.
  • Calculator: Perform calculations, unit conversions, data transformations and vector and matrix operations as part of the process flow for all parameter types including strings.
  • Script: Execute a script of Java commands to manipulate parameters, interact with files, or invoke other programs.
  • Database: Interface with a SQL-compliant relational database (Oracle, DB2, Access, or MySQL Server 2000) to support retrieval and storage of input and output data.
  • Grid Support: Enable parallel submission of optimisation, Monte Carlo, and DOE jobs on multiprocessor machines.

Design Optimisation

Optimise the simulated behaviour characteristics in terms of performance, performance variance, and reliability.
  • Design of Experiments (DOE): Access a full suite of methods including Central Composite, Data File, Full Factorial, Fractional-Factorial, Box-Behnken, Latin Hypercube, Optimal Latin Hypercube, and more.
  • Optimisation and Target Solver: Define your optimisation problem in terms of variables and multiple weighted and scaled objectives and constraints using various algorithms.
  • Data Matching: Calibrate simulation models by minimizing any variety of different error measures using optimisation techniques.
  • Approximations: Create an approximation model for any task, any application component, or from a data file.
  • Monte Carlo Analysis: Leverage simple random sampling, descriptive sampling, eight standard distributions, and distribution truncation.
  • Six Sigma: Use probabilistic analysis to measure the quality of a design given uncertainty or randomness of a product or process.
  • Taguchi Method: Improve the quality of a product or process by striving to achieve performance targets and minimizing performance variation.

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