OptiY
Encyclopedia
OptiY is a design environment providing modern optimization strategies and state of the art probabilistic algorithms for uncertainty, reliability, robustness, sensitivity analysis, data-mining and meta-modeling.
in Germany. OptiY GmbH is founded in 2006 to start the commercialization of the software.
is the process of extracting hidden patterns from data. Data mining identifies trends within data that go beyond simple data analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of processes and target opportunities. Data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of applications such as manufacturing, marketing, fraud detection and scientific discovery etc.
, the system complexity can be reduced and the cause-and-effect chain can be explained,.
has to be performed. Thereby, the output distributions will be calculated from input distributions based on the deterministic simulation model by any simulation system. The realistic system behaviors can be derivate from these output distributions,.
is a process to win the mathematical relationship between design parameters and product characteristics. For each point in the parameter space, there is a corresponding point of the design space. Many model calculations should be performed to show the relationship between input and output systematically (Full Factorial Design). For a high computing effort of the product model, it is practically infeasible. Adaptive response surface methodology can be used to solve this problem.
. Criteria conflict each other. Trying to minimize a criterion, other criteria may be maximized. There is not only one solution, but also a Pareto optimal solution frontier. Multi-objective optimization finds all Pareto solutions automatically with a single run. The multiple decision making support tool is also available to select one best suitable solution from them.
History
The predecessor of OptiY is the program USAN. It is a software platform for modeling, simulation and optimization of mechatronic systems based on a long time research and development of the institute for electro-mechanical design at the Dresden University of TechnologyDresden University of Technology
The Technische Universität Dresden is the largest institute of higher education in the city of Dresden, the largest university in Saxony and one of the 10 largest universities in Germany with 36,066 students...
in Germany. OptiY GmbH is founded in 2006 to start the commercialization of the software.
Features
OptiY is an open and multidisciplinary design environment, which provides direct and generic interfaces to many CAD/CAE-systems and house-intern codes. Furthermore, a complex COM-interface and a user-node with predefined template are available so that user can self-integrate extern programs for ease of use. The insertion of any system to an arbitrary process chain is very easy using the graphical workflow editor. Collaborating different simulation model classes is possible as networks, finite-element-method, multi-body-system, material test bench etc.Data mining
Data miningData mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
is the process of extracting hidden patterns from data. Data mining identifies trends within data that go beyond simple data analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of processes and target opportunities. Data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of applications such as manufacturing, marketing, fraud detection and scientific discovery etc.
Sensitivity analysis
Local Sensitivity as correlation coefficients and partial derivatives can only used, if the correlation between input and output is linear. If the correlation is nonlinear, the global sensitivity analysis has to be used based on the variance-relationship between input- and output-distribution as Sobol index. With sensitivity analysisSensitivity analysis
Sensitivity analysis is the study of how the variation in the output of a statistical model can be attributed to different variations in the inputs of the model. Put another way, it is a technique for systematically changing variables in a model to determine the effects of such changes.In any...
, the system complexity can be reduced and the cause-and-effect chain can be explained,.
Probabilistic simulation
The variability, uncertainty, tolerance and error of the technical systems play an important part by the product design process. These cause by manufacturing inaccuracy, process uncertainty, environment influences, abrasion and human factors etc. They are characterized by a stochastic distribution. The deterministic simulation cannot predict the real system behaviors due to the input variability and uncertainty, because one model calculation shows only one point in the design space. Probabilistic simulationProbabilistic design
Probabilistic design is a discipline within engineering design. It deals primarily with the consideration of the effects of random variability upon the performance of an engineering system during the design phase. Typically, these effects are related to quality and reliability...
has to be performed. Thereby, the output distributions will be calculated from input distributions based on the deterministic simulation model by any simulation system. The realistic system behaviors can be derivate from these output distributions,.
Reliability analysis
The variability of parameters causes often a failure of the system. Reliability analysis investigates the boundary violation of output due to input variability. The failure mechanisms of components are known in the specification for the product development. They are identified by measurement, field data collection, material data, customer-specifications etc. In the simulation, the satisfaction of all product specifications is defined as constraints of the simulation results. The system reliability is given, if all constraints scatter insight the defined boundaries. Although a nominal parameter simulation shows that all values of the constraints are located in reliable boundaries, the system reliability however cannot be warranted due to input variability. A part of the constraints variability, which violates the defined boundaries, is called the failure probability of the solution. Reliability analysis computes the failure probability of the single components and also of the total system at a given time point.Meta-modeling
Meta-modeling or Surrogate modelSurrogate model
Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the air flow around the wing for...
is a process to win the mathematical relationship between design parameters and product characteristics. For each point in the parameter space, there is a corresponding point of the design space. Many model calculations should be performed to show the relationship between input and output systematically (Full Factorial Design). For a high computing effort of the product model, it is practically infeasible. Adaptive response surface methodology can be used to solve this problem.
Multi-objective optimization
In development process of technical products, there are frequently design problems with many evaluation goals or criteria as low cost, high quality, low noise etc. Design parameters have to be found to minimize all criteria. In contrast to a single optimization, there is another order structure between parameter and criteria spaces at a multi-objective OptimizationMultiobjective optimization
Multi-objective optimization , also known as multi-criteria or multi-attribute optimization, is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints....
. Criteria conflict each other. Trying to minimize a criterion, other criteria may be maximized. There is not only one solution, but also a Pareto optimal solution frontier. Multi-objective optimization finds all Pareto solutions automatically with a single run. The multiple decision making support tool is also available to select one best suitable solution from them.
Robust design optimization
Variability, uncertainty and tolerance have to be considered for design process of technical systems to assure the highly required quality and reliability. They are uncontrollable, unpredictable and cause the uncertainty satisfaction of the required product specifications. The design goal is assuring of the specified product functionalities in spite of unavoidable variability and uncertainty. The approach solving this problem is robust design of the product parameters in the early design process. Thereby, optimal product parameters should be found. Within, the system behavior is robust and insensitive in spite of unavoidable variability. E.g. the consistent variability und uncertainty leads only to the smallest variability of the product characteristics. So, the required product specifications will be always satisfied.External links
- OptiY University
- Analysis and Model Based Optimization of an Electromagnetic Valve Actuator
- Probabilistic Optimization of Polarized Magnetic Actuators by Coupling of Network and Finite Element Models
- Robust Design and Optimization of Thick Film Accelerometers in COMSOL Multiphysics with OptiY
- Robust Design Optimization with OptiY
- Meta-Modeling With OptiY - Winning Mathematical Surrogate Models from Measurement Data or Complex Finite Element Analysis
- Sensitivity Study, Design Optimization and Tolerance Analysis of a Car Suspension in RecurDyn