This article has been adapted from the “AI For Engineering: Your Roadmap to Getting Started” eGuide.

Though it probably comes as no surprise, artificial intelligence (AI) is here and already transforming organizations around the world. But how is AI impacting engineering, specifically? AI can seem like a heady and complex topic. How can you go beyond the hype and build a roadmap for modernizing your engineering processes with AI-powered solutions? Luckily for you, Altair knows AI and, more importantly, how to make AI for engineering design easy to understand and adopt for your business. Without further ado, let’s demystify AI’s complexities in engineering and equip you with the knowledge and insight you need to champion AI for engineering design.

Product and System Design and Development

AI is an indispensable technology that’s transforming how engineers design, develop, and monitor systems and processes. This section explores the various ways AI is impacting the engineering process from initial design to manufacturing to in-service operation to post-operation reuse and recycling. 

Simulation, Modeling, and Analysis

Addressing the need to efficiently predict complex system behavior under various conditions without using physical prototypes, organizations can deploy AI-augmented engineering methods that recognize patterns and shapes, quickly simulate models, emulate experts, and identify potential system failures or performance issues.

Fast Physics Predictions

Overcoming slow and resource-intensive traditional solver simulations, engineers can use models trained on past simulation data from any source to rapidly predict physics outcomes like stress, deformation, and flow rates up to 1,000x faster or more compared to traditional simulations, facilitating quicker evaluation of numerous design variations without extensive computational costs.

Design Optimization

Meeting optimization requirements for performance, cost, and weight, engineers can use AI for engineering design to rapidly explore vast design spaces, produce efficient designs, and reduce development time.

Generative Design

To efficiently deliver design innovations, engineers can leverage simulation methods that use machine learning algorithms to explore a multitude of design possibilities based on defined goals and constraints.

Design Errors and Correction

To prevent flawed products and costly downstream corrections, AI for engineering design tools can detect inconsistencies and errors early in the design cycle by comparing design data with accepted standards, empowering engineers to make timely, informed modifications.

Data-Driven Manufacturing

Tackling challenges in sustainable manufacturing, waste reduction, process-efficiency improvement, and time to market, users of all skill levels can leverage operational data through AI-enabled, self-service data analytics, design, and simulation platforms with low- and no-code workflows.

Reduced Order Modeling and System Identification

Simulating detailed 3D models comes with a hefty computational price tag, but with AI for engineering design, engineers can transform these high-fidelity models into efficient 1D, reduced order models (ROMs) for system-level study in a variety of applications: digital twin deployment, system identification, faster optimization, multidisciplinary analysis, improved real-time simulation accuracy, and more.

Collaboration

Addressing the need for efficient data and knowledge integration across numerous project phases and domain silos, AI for engineering design can automate repetitive tasks and streamline decision-making by promoting real-time collaboration between all stakeholders including design, development, engineering, testing, manufacturing, and partner alliances.

Strategic Operations

In addition, AI for engineering design offers an array of capabilities that can augment traditional engineering practices across all engineering domains. For large and small organizations alike, AI assists with critical goals like:

Energy Efficiency and Sustainability

By using machine learning capabilities to analyze data from energy systems, engineers can develop energy-saving strategies that optimize energy consumption, reduce waste, and deliver more sustainable product designs.

Predictive Maintenance 

To reduce downtime and prevent costly breakdowns, AI- powered predictive maintenance systems can process sensor data from equipment to find patterns indicative of potential failures.

Supply Chain Optimization 

To enhance cost-effectiveness in supply chains, AI can analyze supply chain data to optimize inventory levels, improve procurement processes, and predict demand fluctuations, thereby mitigating risks and potential bottlenecks.

Quality Control 

Facing challenges to improve product quality and achieve Six Sigma efficiency, organizations are leveraging AI-powered solutions to capture, monitor, and analyze data from digital streaming and embedded sensors in equipment on the factory floor. Deploying AI in this way enables lean manufacturing, continuous process improvement, and end-product flaw reduction.

Improved Productivity and Streamlined Processes 

To inform operators with precision and efficiency on engineering tasks during assembly, inspection, and maintenance, organizations can take advantage of AI-augmented automated manufacturing systems to streamline processes.

What Are the Benefits and Challenges of AI for Engineering?

AI is becoming integral to engineering, transforming traditional practices and enabling unprecedented efficiencies. As organizations seek to adopt AI for engineering design, it’s important to weigh both the benefits and challenges associated with this transformative technology.

Benefits of AI in Engineering

AI offers a multitude of benefits within engineering, including:

• Enhanced Efficiency and Optimized Designs: AI increases process efficiency and accelerates design innovation, enabling smarter decision-making.
• Cost Reduction and Innovation: AI reduces operational costs and fosters innovation, enabling advancements in various engineering domains.
• Customization and Collaboration: AI facilitates tailored solutions and promotes collaboration across disciplines, enhancing team dynamics and outcomes.

Challenges of AI in Engineering

At the same time, AI can also present challenges to teams and organizations. These include:

• Maturity and Implementation: As a relatively new technology, AI faces broad maturity and implementation challenges.
• Security and Privacy: Robust security measures and privacy protocols are essential to safeguard data and maintain trust.
• Workforce Impact: While AI for engineering design augments human capabilities, managing workforce transitions and mitigating bias are critical for effective integration.

Conclusion

By leveraging AI, engineers can overcome challenges, unlock new opportunities, and drive progress in their respective fields.

That said, since AI for engineering design is a relatively new technology at the enterprise level, there are known implementation issues to overcome. Confidence comes with the maturity of any technology – and AI is no different. Companies need a trusted partner like Altair that delivers AI-powered solutions with the infrastructure and security measures that enable customers to leverage AI safely and privately.

Companies using AI for engineering will also need to consider the impact of AI on their workforces. Processes will shift, but adopting this extremely powerful technology can only augment and assist the skills and knowledge of an experienced human workforce, not replace them. AI needs a human copilot whose oversight is essential to mitigate bias and improve the quality of AI predictions.

After evaluating its advantages and challenges, companies of all sizes are increasingly implementing AI for engineering design. Thanks to solutions like Altair’s that offer low- and no-code AI-embedded workflows, even small companies can easily leverage AI to augment productivity and efficiency within any engineering process.

To read the full eGuide, visit “AI For Engineering: Your Roadmap to Getting Started”.