What is Design Automation? A Clear Guide to Its Benefits and Uses

This article is dedicated to design automation, a cluster of practical software applications that aim to optimize engineering tasks. These applications aim to capture knowledge and encapsulate it in software procedures that spare humans doing repetitive operations once a well-established process is consolidated within a design team.

For instance, you can think about the answer to the following question even if you are not a designer: once you have created a basic shape, would you prefer to clone copies automatically, or would you like to spend time recreating them individually? A naïve example of solution is illustrated at the end of this section.

Automation simplifies work and enhances design intelligence. Design automation embeds engineering knowledge into workflows, eliminating tedious manual tasks and repetitive procedures. This process is not only internal to design departments, but it brings product manufacturing, product quality and product design nearer to each other. Therefore, it gives a competitive advantage to its adopters.

Design automation thrives on digitalization. It combines traditional programming (Software 1.0) with machine learning (Software 2.0). While classical code follows explicit instructions, Machine Learning (ML) detects patterns in input data. The explosion of digital data—rising from 10⁹ bytes in the 1960s to an estimated 10²⁴ bytes by 2025—fuels ML practical applications in modern manufacturing.

Design automation creates collaboration. Designers can quickly respond to colleagues across departments by embedding knowledge into automated systems. For example, they can provide sales teams with cost analyses of custom products.

This process doesn’t replace skilled staff. While it accelerates design, it doesn’t enable those without CAD expertise to create valid designs. Designers remain central to decision-making, ensuring that they enhance rather than replace their role.

What is Design Automation?

Design automation uses software to streamline and optimize engineering design processes. By delegating trivial tasks to software, it reduces human errors, improves the product quality and of life for design workers, and reduces project costs.

Design automation enables engineers to capture design rules, generate variations, and integrate with simulation workflows. We will focus, therefore, on software applications. Check the Neural Concept site to get an idea of how, in general, automation is transforming mechanical engineering, for instance, with physical devices.

How Does It Work? CAD Automation in Engineering

When modifying an existing design to meet new requirements, it takes time to adjust the model and associated documentation. The solution is to embed predefined rules into the Computer-Aided Engineering (CAD) environment. These rules ensure that modifications happen systematically and without repetitive manual updates.

The process begins by defining the logical framework that governs the design.

Then, using the established parameters, the 3D model is created. It extends beyond basic dimensions and constraints by incorporating engineering rules that automatically adjust the model when specifications change.  Many CAD environments provide built-in tools to facilitate this rule definition, reducing the implementation complexity.

User Interface

After integrating these rules, a user friendly interface will clarify adjustable parameters and their impact on the final design. The interface embeds domain knowledge, enabling individuals with limited CAD expertise, such as junior designers or technical sales personnel, to configure variations without requiring direct input from senior engineers. Design automation supports omni-channel sales by enabling customer self-service for product customization. This "mass customization" concept is key to the implementation of Industry 4.0.

For instance, automation can extend beyond dimensional adjustments to influence material selection. If a component exceeds a set length, the system can automatically change materials to maintain structural integrity.

By integrating these capabilities, engineering teams will reduce rework with no added value. At the same time, they will improve design consistency and accelerate the customization process. Engineers can dedicate themselves to higher-value development tasks rather than wasting time on tedious, routine modifications.

Historical Evolution

Design automation work has evolved from traditional methods such as manual drafting to software. Let's reconstruct this history.

Before Software

Engineers initially created technical drawings by hand with drafting tools such as T-squares, compasses, and protractors on large sheets of paper. Drafting machines (see figure) were devices mounted on a drawing board. They combined a protractor with articulated arms, allowing precise and efficient technical drawing without separating rulers and triangles. It was widely used before CAD systems became standard. This manual process required precise measurements, careful line work, and extensive revisions when changes were needed. Each modification meant redrawing parts of the design, increasing the time and effort required to refine a concept.

Software 1.0 & Software 2.0

Later, designers started using Computer-Aided Design (CAD) systems, which streamlined the process. The first discernible design automation was the introduction of parametric modeling into CAD software in the late 20th century. Designs could be adjusted based on predefined rules. This phase, aligned with Software 1.0, relied on explicit instructions. This means that programmers created code to streamline repetitive actions. This ensured the efficiency of the CAD design process, but it required a human-defined logic to drive every step.

The transition to Software 2.0 means a shift from rule-based processes to AI-driven. Machine learning and deep learning enable systems to “learn.” This means inferring patterns and optimizing designs. AI also creates entirely new configurations based on datasets. This is the world of Generative Design.

Instead of engineers explicitly programming every rule, AI models learn from data and adapt to constraints. Generative design explores many possibilities based on functional requirements and creates counterintuitive but efficient solutions. Additive manufacturing is particularly suitable for transforming generative ideas into physical parts.

Key Components of Design Automation

Design automation software accelerates product development by reducing manual work and reducing errors. Integrated with CAD systems, these tools generate models, manage assemblies, and run simulations to improve design quality and consistency with the inputs given to prototyping and manufacturing.

Software Tools

Design automation software is widely used across industries to streamline product development processes. Popular CAD software includes AutoCAD, SolidWorks, CATIA, and Autodesk Revit.

These Computer-Aided Design platforms offer capabilities for automating design tasks, such as generating 2D and 3D models for virtual and physical prototypes, managing complex assemblies, and simulating real-world conditions. They often feature parametric design tools, enabling designers to create flexible and adaptable models.

Some tools are specifically tailored for specific industries, such as electrical design automation (EDA) tools like Altium Designer, which are used for PCB layout and circuit design.

Other tools, like Siemens NX, integrate product lifecycle management (PLM) for comprehensive workflow control.

Integration with CAD Systems

CAD software serves as the foundation for digital modeling. Design automation tools enhance CAD capabilities, including as

• part generation,
• dimensioning,
• assembly configurations.

Procedures built into CAD ensure that design changes are automatically reflected across all related components, significantly reducing the risk of human error. As project time proceeds from concept to manufacturing, the cost of errors grows proportionally!

Additionally, automation tools can interact with CAD systems to perform simulations, structural analysis, and optimization, streamlining the entire design-to-manufacturing process. This CAD-CAE integration fosters faster iterations, improved design consistency, and more effective product development across various engineering applications, ranging from automotive to energy engineering.

Library elements in CAD, like the illustrated crown gear in FreeCAD, speed up design. Library elements provide predefined, parametric components that users can insert and modify without starting from scratch. Design automation involves scripting, rule-based modeling, or generative. So, if a library element allows parameter input, adapts dynamically, and supports reuse, it can be considered parametric modeling. FreeCAD’s Gear Workbench, for example, generates gears based on user-defined values. A tool like FreeCAD can support real design automation with Python scripting, not just parametric modeling.

Benefits of Design Automation in Engineering

What are the benefits of design automation? In a previous article, we described what is Manufacturing Automation with applications and advantages. We will now concentrate on the advantages during the preliminary phase of design.

Saving Time

Repetitive actions like part generation, assembly configurations, and error checking slow down the design process. For example, a parametric model can generate multiple variations of a part, that would require engineers or optimization algorithms to explore design alternatives with manual rework. Automated assembly configurations can adjust parts based on predefined constraints, reducing manual alignment and fit checks.

Consistency and Accuracy - Design and Manufacturing

Human error is often a factor, especially when handling large amounts of data and intricate geometries. Design automation reduces potential mistakes by applying predefined rules and guidelines, resulting in more accurate and standardized designs. Checks for compliance with design rules further ensure consistency throughout the project, from the concept phase to manufacturing.

Cost Reduction, TTM, and Creativity

To summarize, part generation and assembly configurations eliminate time-consuming manual adjustments, while error-checking tools detect issues early, reducing costly redesigns. Additionally, they streamline the development process, allowing companies to shorten TTM (time to market) and allow manufacturing to launch products faster and adapt quickly to new opportunities. In competitive industries, faster product launches can translate directly into a significant market advantage because of a stronger connection and consistency between product concept and product manufacturing.

In addition to reducing labor costs and improving resource allocation, we emphasize the vital aspect of freeing engineers to dedicate their time on higher-value tasks such as R&D or pure creativity.

Design Automation is Revolutionizing Engineering Tools and Applications with Deep Learning

Neural Concept Shape uses data-driven deep learning to recognize 3D shapes, enabling rapid design optimization. Its predictive AI analyzes constraints and past designs to generate efficient geometries. By automating design iterations, the Neural Concept platform with 3D Deep Learning capability enhances performance while reducing manual effort. The generative approach explores multiple configurations, ensuring optimal solutions for aerodynamics, thermal efficiency, and structural integrity.

Applications of Deep Learning Across Key Industries

Neural Concept’s AI accelerates design in automotive engineering, aerospace, and other sectors ranging from energy to biomedical and civil. It optimizes aerodynamics, enhances structural integrity. Designers quickly iterate designs, improving performance and reducing costs. AI-driven simulations enable rapid, data-driven decisions, cutting development time while ensuring manufacturability and efficiency.

Success Stories for Design Automation

We will present success stories showing how automation is revolutionizing the aerospace industry and other sectors such as automotive and energy.

General Electric – Jet Engine Design

General Electric’s aviation division used design automation tools to optimize the development of jet engines. By automating the design of complex components such as turbine blades and structural parts, GE reduced the time spent on repetitive actions. Integrating parametric design tools allowed for faster iterations, reducing the design cycle. GE achieved greater precision in its models, resulting in higher-performance engines and speedier time-to-market, improving the aerospace industry’s competitiveness. Please explore GE Aviation Digital Thread to know more.

Vehicle Platform Design

Automotive companies apply design automation tools to streamline its vehicle platform design and could generate optimized component designs and make real-time adjustments based on performance simulations. The comparison between AI prediction and the CFD result provides insight into the value of using this technology in the automotive design processes. An example that highlights the advantage of using AI in automotive design processes, for motorsport or manufacturing, can be seen in the figure. The result was obtained in 0.3 seconds instead of 3 hours!

According to the Design Representative of Subaru's Car Production Technology Department, Neural Concept "has a very high capability for capturing 3D shape features, excelling in accurately reading shape features and simplifying feature quantities. [...] we found that NC allows for much faster forming predictions than traditional CAE, while also delivering sufficiently accurate results."

This results in streamlined production and a substantial reduction in design and prototyping costs. Additionally, companies can improve their vehicle models’ overall quality and durability, ensuring more consistent production standards and reducing recalls.

Siemens – Power Plant Design

Siemens used design automation tools to construct power plants, particularly the generation of pipe layouts reducing the need for manual input. This led to fewer design errors and minimized construction delays, reducing project costs. Siemens also optimized compliance checking, ensuring the plant designs adhered to regulatory standards without requiring extensive manual review studies. See Siemens Digital Industries—Energy.

Present Challenges and Future Perspectives

Adopting design automation tools must address several key challenges to ensure smooth implementation and maximize the benefits of automation.

Implementation Costs

Implementation can require a significant upfront investment, for instance

• costs for software licensing
• hardware upgrades
• potentially even custom development to tailor the tools to specific business needs.

Often, companies must invest in consulting or support services by software vendors or service providers to ensure the proper setup and deployment of the tools.

Learning Curve and Integration with Existing Systems

With proper training programs and support, teams can adapt quickly, and the productivity gains over time will justify the learning curve. As with any transition, engineering teams must undergo training and adapt to design automation tools. Learning can be time-consuming for teams accustomed to manual design processes. The complexity of the tools and the requirement to learn new workflows can initially slow down productivity.

For instance, transitioning from 2D CAD tools (e.g., AutoCAD) to BIM tools like Autodesk Revit presents a significant learning curve. Revit is a powerful tool that enables designers to design buildings in a 3D, data-rich environment, greatly enhancing accuracy, collaboration, and overall project efficiency. Teams familiar with traditional CAD workflows must adapt to new concepts like parametric modeling, where every element in the design (walls, windows, HVAC systems, etc.) is interconnected and changes automatically when the design is updated. See Autodesk BIM Case Studies.

The point is that a fully automated tool that a new user would use would require zero or little learning! This is why the savviest organizations promote the spin-off of a "methodology team" that oversees changes and delivers turnkey solutions to production teams!

Integrating new tools with legacy systems can present challenges. Compatibility issues, data transfer problems, and the need for system updates or rewrites are headaches that may arise and lead to additional time and resource investments.

Future of Design Automation with Artificial Intelligence

The future of design lies in data-driven, real-time simulation, and no-code/low-code platforms. Machine learning will optimize complex systems, minimizing human intervention, while cloud collaboration and (in a time scale to be defined) quantum computing could redefine problem-solving. These advancements will accelerate innovation, making design smarter, faster, and more tailored across industries such as aerospace, automotive, biomedical, chemical, civil, and semiconductor.

Who are the Users of a Design Automation Solution?

Design automation is transforming engineering, much like LLMs is impacting on writing and editing.

Just as AI-powered tools enable users to create text and images without specialized expertise, design automation allows individuals without advanced CAD skills to generate designs that meet industrial standards.

But it’s not just for beginners!

Experienced CAD users also profit from design automation. These solutions:

✔ Boost productivity by reducing repetitive tasks

✔ Enhance design consistency and accuracy

✔ Streamline workflows for complex design requirements

Whether you’re new to CAD or an experienced professional, design automation enables you to operate more quickly, intelligently, and efficiently.

Conclusion

Design automation streamlines engineering workflows by embedding expert knowledge into software-driven processes. Automation traditionally relied on predefined rules (Software 1.0), with AI and deep learning (Software 2.0), companies like Neural Concept push it beyond rule-based systems. Neural Concept enhances design efficiency by detecting patterns in corporate datasets, leading to quicker design changes and even generating novel designs through generative approaches. This shift enables designers to move beyond incremental ameliorations from manual parameter adjustments. It allows AI to propose innovative solutions while ensuring manufacturability and performance remain intact.

The role of Neural Concept extends beyond efficiency gains. The platform promotes real-time collaboration and democratizes engineering insights. By integrating AI with traditional CAD environments, Neural Concept enables faster iterations, automatic adaptation to design constraints, and fewer communication bottlenecks across teams.

Designers and simulation experts no longer need to manually rework models or rely solely on predefined rules. AI-driven predictive models refine designs dynamically. This is not just about speed but also about augmenting human expertise with intelligent, adaptive tools.

FAQ

What does a design automation engineer do?

As design automation expert users, you develop tools and workflows to streamline engineering resources for repetitive design tasks. You will integrate computer aided design, simulation, and AI to cut a significant amount of learning curve for final users with a solution that reduces complexity for the casual user.

How does design automation benefit engineering and the manufacturing industry?

Design automation tools reduce manual effort, minimizes errors, accelerates design iterations, and enables experts to focus on innovation to stay afloat in today's market, leveraging well-consolidated engineering knowledge of the company’s design and manufacturing processes stored and used when needed.

How does design automation impact collaboration across engineering teams?

Design automation tools create seamless communication, allowing designers, production, and sales teams to use standardized design processes, improve data consistency, and, thanks to aligned design and manufacturing, happy customers. These tools can also automatically create the documents and data required to sell and manufacture custom products.

What industries can profit from design automation?

The aerospace, automotive, machinery, construction, electronics, and medical device industries use some type of design automation solution to optimize product development and manufacturing.

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