Engineering has always been one of the most forward-thinking and innovative fields of applied science. Now, advancements in AI computers promise to further enhance the field with greater accuracy, reactivity, and analytical insights.
- What is AI in Engineering?
- How is AI Used in Engineering?
- The Benefits of AI in Engineering
- Challenges and Considerations
- What Are the Future Trends for AI in Engineering?
- Closing Thoughts
What is AI in Engineering?
Artificial intelligence (AI) is the field of computer systems that can think, reason, and analyze information, much like humans. Given how broad and multifaceted the field is, almost every subsection of AI is useful for engineers, from generative AI to machine vision to system monitoring and more.
How AI Is Transforming Engineering and Industrial Workflows
The engineering field covers so many topics and disciplines that it is nearly impossible to cover all of them in a single article. The following applications are the most notable and widespread:
Faster Innovations and Product Development with Generative AI
AI can be used in every step of product development, from initial ideation to final optimizations. At the start of development, generative AI programs like Leo can be given written descriptions or visual renderings to develop concept images of the new product for everything ranging from handheld electronics to car interiors.
In the final stages of design, AI can also be used to review the assembly process and bill of materials to identify points of waste, quicken production times, and reduce overall costs.
AI-Powered Machine Vision: Improving Assembly Lines and Safety
By training AI algorithms on what a finished product or design element should look like, engineers can use machine vision to let computers “see” the same way humans do and help spot irregular patterns, shapes, or activities.
For example, cameras monitoring an assembly line can feed information to industrial AI box PCs, letting them spot defective products before they get packaged and shipped to customers. It can also be used to detect anomalous activity on a worksite, such as unauthorized intruders or workers entering areas they aren’t supposed to.
Predictive Analytics in Engineering: Reducing Maintenance Costs
AI’s ability to analyze historical data and compare it to real-time information allows it to recognize patterns and trends within it. This is particularly helpful for maintenance purposes, as AI can help engineers identify potential issues before they spiral into major problems. By doing so, AI-powered analytics lower maintenance costs and overall downtime. Any industry heavily relying on consistent performance, such as the energy sector, can benefit from this improved reliability.
How AI-Enhanced Robotics are Reshaping the Industrial Sector
Artificial intelligence can enhance both autonomous and collaborative robots. “Cobots” often work alongside engineers in roles that require inhumanly high levels of speed, precision, and consistency or to perform extremely repetitive tasks. When equipped with advanced sensors and trained by AI algorithms, cobots can quickly adapt to the patterns and habits of their human counterparts and learn how to better work with them.
AI and Industry 4.0: The Future of Smart Manufacturing
AI seamlessly fits into the broader evolution that experts are dubbing Industry 4.0. These technologies, such as the Internet of Things and advanced robotics, are meant to streamline industrial processes and boost efficiency and can seamlessly interact with AI.
For example, IoT sensors can feed machine learning algorithms information from an entire factory floor, work site, or vehicle fleet. This helps engineers better understand how their workflows operate and identify potential points of improvement. From there, engineers empowered with AI-derived analytics can adjust their operations accordingly.
The Key Benefits of AI in Engineering and Industrial Applications
When properly implemented and effectively used, AI in engineering can lead to numerous payoffs, including:
- Greater Efficiency: AI’s ability to collect and analyze information from massive data sets provides insights that unaugmented humans might miss. Engineers can turn these insights into effective changes to their workflows and products to remove waste and save time.
- Faster Product Development: AI’s generative capabilities can assist during the research and development phase, helping to get new products and services to market faster.
- Cost Savings: By removing inefficiencies and optimizing their workflows, engineering teams can save money, whether that be spending less on raw materials, fixing wasteful processes, or being better prepared for mechanical breakdowns.
- Safer Workspaces: AI’s ability to support robots and detect early warning signs of failure via advanced analytics and machine vision can help create safer, more secure work environments that don’t put workers at risk.
Challenges of AI in Engineering and How To Overcome Them
As with any new technology, there are challenges to overcome when implementing AI for engineering teams.
Accepting and Implementing AI Effectively
First and foremost is the training and familiarization process. Generative AI is still a relatively new technology, and many companies are still trying to figure out how to implement it effectively. Employees need to understand that AI is a tool meant to supplement their own knowledge and experience, not replace it entirely.
Sorting Through AI Hallucinations
Another issue is AI “hallucinations,” where the algorithm invents false information or suggests an impossible solution. While rare, these issues are more reason for engineers to temper the AI’s suggestions with their own education and training.
Addressing Upfront Costs
Lastly, there is the question of cost. The high-end GPUs that AI computers rely on are undeniably expensive, so companies must budget carefully and work closely with computer manufacturers to develop solutions that best suit their needs and get the most return on their investment. Partnering with an experienced original equipment manufacturer (OEM) is the best way to ensure this happens.
Future Trends: What’s Next for AI in Engineering?
Looking to the future, it’s all but inevitable that generative AI will continue to expand and be used in new ways, including within the engineering field. While much of AI’s usage has been in text-based formats, we will likely see it applied to non-text formats more frequently, such as block diagrams, 3D models, and flow charts.
An Uncertain Regulatory Landscape
Another major factor for AI in the future is regulation. During the Biden administration, new regulations concerning safety testing and risk assessments were implemented, but these were quickly overturned by President Trump. With the regulation landscape in flux, any engineering team or company that implements AI will need to be prepared to adapt their efforts to these changes.
Embracing Specialized AI Models
Lastly, we will likely see a shift away from generalist AI models like ChatGPT’s iterations in favor of specialized models that are fine-tuned or developed from the ground up for specific applications and roles, especially as training AI becomes faster and more efficient.
Closing Thoughts
Like other sectors, engineering teams across multiple disciplines can reap vast rewards by implementing AI in their workflows. From power grid management to product development, engineers are exploring this exciting new tool for their own needs.
Is your engineering team considering implementing AI? Explore Cybernet’s range of AI-powered industrial computers, or contact our team to talk to our experts today. As an experienced OEM, we can also collaborate to develop a custom solution perfectly tailored to your needs.