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Example: CAD Integration - Dimension Optimization Linked with SOLIDWORKS

Example: CAD Integration - Dimension Optimization Linked with SOLIDWORKS

By using the SOLIDWORKS API, we can automate dimension changes and reduce operations.

To change dimensions through dimensional optimization, there are two methods: one is to directly modify the CAD model and remesh it, and the other is to change the mesh itself (morphing). Many CAD systems support scripting languages for manipulating CAD models, allowing external programs to operate on CAD models using these scripts. Here, we will introduce an example of dimensional optimization using a VBA macro that links SolidWorks (SolidWorks Japan Co., Ltd.) with 'AMDESS'. [Contents] ■ Overview ■ Analysis Model ■ Optimization Conditions ■ Results ■ Discussion *Detailed information about the case study can be viewed through the related links. For more information, please feel free to contact us.

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[Technical Column] The Theory of OPTISHAPE-TS

[Technical Column] The Theory of OPTISHAPE-TS

An explanation of the theory used in the optimization function! A column discussing "H1 Gradient Method."

Since the release of the initial version, we have frequently received questions from customers about the theories underlying our optimization. From the user's perspective, it is understandable to feel hesitant about using software without a clear understanding of the theoretical background. In this technical column, we will explain the theories used in the optimization features of OPTISHAPE-TS as clearly as possible. Please feel free to download and take a look. [Contents] ■ Episode 1: Introduction to Non-Parametric Optimization *For more details, please refer to the PDF document or feel free to contact us.

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The theory of OPTISHAPE-TS "Gradient Method in Finite-Dimensional Spaces"

The theory of OPTISHAPE-TS "Gradient Method in Finite-Dimensional Spaces"

An explanation of gradient methods when the design variables are a finite number of real numbers! Introduction to the column.

In the previous article, we explained the gradient method, which is one of the solutions to optimization problems. In this article, we will discuss the gradient method in finite-dimensional spaces, specifically when the design variables are a finite number of real numbers. Please feel free to download and take a look. [Contents] ■ Episode 15: What is H1 Gradient Method Part 8 "Gradient Method in Finite-Dimensional Spaces" *For more details, please refer to the PDF document or feel free to contact us.

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[Technical Column] The Theory of OPTISHAPE-TS: Infinite Dimensions and Function Spaces

[Technical Column] The Theory of OPTISHAPE-TS: Infinite Dimensions and Function Spaces

To deepen your understanding of H1, we will explain norms and inner products in function spaces!

Last time, I explained the completeness of spaces. Since H1 is a function space, this time I will explain norms and inner products in function spaces to deepen your understanding of H1. Please feel free to download and take a look. [Contents] ■ Episode 12 What is the H1 Gradient Method? Part 5 "Infinite Dimensions and Function Spaces" *For more details, please refer to the PDF document or feel free to contact us.

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[Technical Column] Theory of OPTISHAPE-TS: Bead Optimization

[Technical Column] Theory of OPTISHAPE-TS: Bead Optimization

You can change the characteristics of a structure without increasing the thickness of the thin plate! Introducing a widely used method.

This time, we will introduce bead optimization, which is one of the non-parametric optimizations of OPTISHAPE-TS. A bead refers to a small uneven shape applied to a thin plate structure. Since it can change the characteristics of the structure without increasing the thickness of the thin plate, the process of generating beads is a widely used method in various applications. [Contents] ■ Episode 19: Bead Optimization *For more details, please refer to the PDF document or feel free to contact us.

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The theory of OPTISHAPE-TS: Manufacturing constraints in H1 gradient method.

The theory of OPTISHAPE-TS: Manufacturing constraints in H1 gradient method.

A method with a very high degree of freedom compared to parametric optimization! Introduction to a technical column.

This time, we will introduce the theory of manufacturing constraints in non-parametric shape optimization using OPTISHAPE-TS. Non-parametric optimization, including shape optimization, offers a much higher degree of freedom compared to parametric optimization, which results in unique shapes. Please download and check out the continuation of the column. [Contents] <Chapter 20: Manufacturing Constraints in H1 Gradient Method> ■ Limitations due to penalty terms in the H1 gradient method ■ Limitations based on the evaluation function *For more details, please refer to the PDF document or feel free to contact us.

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What is compliance in the theory of OPTISHAPE-TS?

What is compliance in the theory of OPTISHAPE-TS?

An explanation of compliance in linear elastic analysis! Introduction to the technical column.

Non-parametric optimization of OPTISHAPE-TS (shape optimization, topology optimization, bead optimization) can commonly evaluate compliance in linear elastic analysis. For those who use it regularly, it may seem like a trivial matter to say, "Ah, compliance," but occasionally, I receive questions from others asking, "What is compliance?" In this article, I would like to explain compliance in a bit more detail. Please feel free to download and take a look. [Contents] ■ Episode 21: What is compliance? *For more details, please refer to the PDF document or feel free to contact us.

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Theory of OPTISHAPE-TS Compliance Sensitivity Part 1

Theory of OPTISHAPE-TS Compliance Sensitivity Part 1

About the problem with two-dimensional design variables! Introducing the linear elastic problem of a cantilever beam as an example.

In the previous article, we discussed compliance in linear elastic problems. By the way, some of you may have heard that "the sensitivity of compliance is the strain energy," but how is this derived? Therefore, starting from this time, I would like to take a few sessions to look at the derivation of the sensitivity of compliance. Please feel free to download and take a look. [Contents] ■ Episode 22: Sensitivity of Compliance Part 1 "Problems with Two-Dimensional Design Variables" *For more details, please refer to the PDF document or feel free to contact us.

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Example: Injection Molding - Simultaneous Filling of Multi-Cavity Molds

Example: Injection Molding - Simultaneous Filling of Multi-Cavity Molds

By collaborating with 3D TIMON, we automatically suppress variations and improve yield.

In general, even runners of equal length have different flow lengths for inner and outer paths, leading to filling imbalances. Here, we will introduce an example of optimizing runner diameter by integrating AMDESS and 3D TIMON* to improve filling balance. *3D TIMON is a plastic injection molding CAE software developed by Toray Engineering Co., Ltd. *For more details, please refer to the related links or feel free to contact us.

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[Example] Generation of CAD model from topology optimization results of a chair.

[Example] Generation of CAD model from topology optimization results of a chair.

Utilize the generated surface! It is possible to conduct verification analysis by generating a mesh.

We will introduce a case where we smoothed the surface of the shape obtained from topology optimization analysis and created a CAD model for verification analysis, as well as STL data for 3D printing. The generated curved surface can be treated as a solid body in CAD software, allowing for verification analysis by generating a mesh. Additionally, by outputting the STL data after smoothing the surface, it can be produced using a 3D printer. [Work Contents] ■ Initial STL ■ Fold settings ■ Editing of small holes ■ Smoothing ■ Editing of thin members ■ Generated curved surface *For more details, please refer to the PDF materials or feel free to contact us.

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[Example] Topology optimization considering manufacturing requirements

[Example] Topology optimization considering manufacturing requirements

Initial design proposals and significant contributions to cost reduction! Introducing topology density variation limitation functions such as 'cross-section'!

Topology optimization is a method for determining the necessity or redundancy of materials (member layout) that contributes to lightweight and high-rigidity product design by seeking structures that maximize rigidity under a certain weight limit. While it allows for significant structural changes compared to the initial structure, it can also result in outcomes that are difficult to manufacture or lead to complex structures with high manufacturing costs. To avoid such situations, the topology optimization in 'OPTISHAPE-TS' utilizes a topology density variation limitation function, enabling optimization while satisfying manufacturing requirements. [Contents] ■ Overview ■ Topology Density Variation Limitation Function ■ Discussion *Detailed case information can be viewed through the related links. For more information, please feel free to contact us.

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[Column] The Theory of OPTISHAPE-TS: Norm Spaces and Inner Product Spaces

[Column] The Theory of OPTISHAPE-TS: Norm Spaces and Inner Product Spaces

Explanation of spaces where norms and inner products are defined! Introduction to a technical column.

In the previous article, we explained the concept of "space" in modern mathematics. The concept of a set exists as "a collection of specific things," and among those, we specifically call those that can determine some kind of relationship between the elements belonging to it "space." Additionally, we introduced "linear spaces" and "metric spaces" as concrete examples of spaces. In this article, we will further discuss spaces where norms and inner products are defined. Please feel free to download and take a look. [Contents] ■ Episode 10: What is the H1 Gradient Method? Part 3 "Norm Spaces and Inner Product Spaces" *For more details, please refer to the PDF document or feel free to contact us.

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[Technical Column] The Theory of OPTISHAPE-TS: "Optimization of Functions"

[Technical Column] The Theory of OPTISHAPE-TS: "Optimization of Functions"

What does "optimizing a function" mean? An explanation from the perspective of the difficulties it entails.

In the previous article, I briefly explained non-parametric optimization. In that context, I mentioned that non-parametric optimization is a method for optimizing functions. In this article, I will explain what "optimizing a function" means, to deepen your understanding of the challenges it presents. Please feel free to download and take a look. [Contents] ■ Episode 2: The Challenges of Non-Parametric Optimization Part 1 "Function Optimization" *For more details, please refer to the PDF document or feel free to contact us.

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[Technical Column] The Theory of OPTISHAPE-TS: "Completeness"

[Technical Column] The Theory of OPTISHAPE-TS: "Completeness"

An explanation of the important property of completeness among the characteristics of space! Introduction to a technical column.

In the previous article, we explained normed spaces and inner product spaces. A normed space is a space equipped with a norm that generalizes the concept of size, while an inner product space is a space equipped with an inner product. In this article, we will explain the important property of completeness among these spaces. Please feel free to download and take a look. [Contents] ■ Episode 11: What is the H1 Gradient Method? Part 4 "Completeness" *For more details, please refer to the PDF document or feel free to contact us.

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[Technical Column] Optimization Algorithm Using the H1 Gradient Method

[Technical Column] Optimization Algorithm Using the H1 Gradient Method

An overview of the structural optimization algorithm used in OPTISHAPE-TS! Introduction to the technical column.

In the previous articles, we explained the theoretical background of the H1 gradient method. Since the discussion became mathematically complex, in this article, we will introduce a more approachable topic: an overview of the structural optimization algorithm used in OPTISHAPE-TS. Please feel free to download and take a look. 【Contents】 <Chapter 17: Optimization Algorithm Using the H1 Gradient Method> ■ Solve the state equations and calculate the value of the evaluation function ■ Solve the adjoint equations and calculate the sensitivity of the evaluation function ■ Calculate the variation of design variables using the H1 gradient method ■ Calculate the weighting coefficients for the variations ■ Update the design variables ■ Satisfy the unsatisfied constraint functions *For more details, please refer to the PDF document or feel free to contact us.

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