Generative AI has been a prime technology driver in 2023. With the public release of ChatGPT from Open AI in 2022, generative AI innovations rapidly penetrated both consumer and enterprise sectors. According to Gartner's research, generative AI is becoming a general-purpose technology whose impact will be similar to steam engines, electricity and the Internet.
In generative AI, foundation models are trained on a broad set of unlabeled data that can be used for different tasks to generate new content—text, images, video, music, speech, software code and product design.
While manufacturing sectors already leverage Artificial Intelligence (AI), generative AI is poised to shift the paradigm. Unlike conventional AI, generative AI not only enables machines to recognize patterns for anomaly detection, etc., but it can also create new solutions independently by leveraging advanced machine learning techniques.
AI TECHNOLOGIES IN MANUFACTURING—A ROUNDUP
AI is not new in manufacturing. Manufacturers have used AI to solve industrial problems and gain efficiencies. A roundup of a few existing AI applications in manufacturing will set the stage to comprehend why generative AI will be a game changer.
Robots Augment Humans
In factories, collaborative robots (cobots) augment the human workforce as they can learn new tasks. Cobots can be trained to develop spatial awareness to detect and avoid obstacles.
Robotic process automation (RPA) software is another technology that helps tackle repetitive tasks like order processing. This improves efficiencies by replacing manual data entry and the time spent searching for inputting mistakes.
Digital Twins Improve Agility
Digital twin technology leverages innovations in AI and machine learning, the Internet of Things (IoT) connectivity and sensor technology to create an exact, real-time replica of a physical product or component. Manufacturers use digital twins to improve productivity and streamline the entire product lifecycle: design, development and maintenance.
Predictive Maintenance Reduces Downtime
Predictive maintenance (PdM) leverages AI/machine learning algorithms to enable manufacturers to reduce equipment downtime with timely prediction of failures. Compared to traditional corrective and preventive maintenance models, PdM has helped companies increase uptime by 10 to 20 percent while reducing maintenance costs by 5 to 10 percent and maintenance planning time by 20 to 50 percent.
Autonomous Manufacturing Enhances Flexibility
Unlike automated machinery and processes, autonomous manufacturing is relatively new in manufacturing. Autonomous manufacturing improves operational flexibility in factories by leveraging advances in AI, robotics and ubiquitous IoT connectivity. It empowers manufacturers to use intelligent, data-driven technologies to enhance product quality and optimize production.
HOW GENERATIVE AI CAN BE A GAME CHANGER
While AI technologies like predictive maintenance, digital twins and robotics have proven highly effective in manufacturing, generative AI, for the first time, introduces a creative twist to these processes. The paradigm shift with generative AI comes from the fact that manufacturers can now use AI to create new solutions that accelerate innovation and market readiness.
Generative AI models are essentially machine learning models. When trained with a given dataset, these models learn the patterns and structure inherent in that dataset. Once trained, the model can process requirements to generate new data with similar characteristics based on specified parameters.
For example, a generative model trained on a dataset of gas turbine engines can generate engine designs that meet your specific requirements but do not necessarily replicate any existing machine. The model will create new outputs based on the specified criteria, allowing you to refine the outcome iteratively. This gives an enormous boost to solving problems.
A Creative Force in System Design
Generative AI models can aid engineers in creating multiple design iterations and refining the design to optimize for efficiency, cost and performance. Generative AI models analyze vast datasets and consider various design parameters to generate innovative and optimized designs that meet specific criteria. The result is high-quality, robust and resource-efficient products less likely to be achieved through traditional methods.
For example, using generative AI, an aerospace company designed aircraft components that reduced the overall weight by 15 percent without compromising structural integrity. This improved fuel efficiency and reduced manufacturing costs.
Streamline Production Processes
Generative AI can also apply to production processes. In the manufacturing industry, generative AI models can assist in streamlining production processes by optimizing workflows. It can provide innovative solutions by identifying bottlenecks and suggesting improvements in real time. This can help reduce downtime and enhance overall operational efficiency.
CONCLUSION
This integration of generative AI in manufacturing design and production marks a transformative leap forward for the industry. It can revolutionize operations and unlock many unforeseen benefits. These benefits may well surpass the outcomes manufacturers achieved through conventional AI and machine learning solutions like predictive maintenance.
To maximize the benefits of generative AI, it must be used with caution and under expert supervision. Since models generate the designs based on the dataset they have been trained with, sometimes the output may differ from reality. Designers and architects should leverage the option of iteratively refining the parameters and criteria until the model generates something practically viable and clear of any intellectual property issues.
Like any technology, when used responsibly and with a clear understanding of its limitations, generative AI has the potential to become a game changer in manufacturing.
Sravani Bhattacharjee has worked as a tech leader at Cisco, Honeywell and other companies where she delivered many successful innovations to the market. As the principal of Irecamedia, she collaborates with Industrial IoT innovators to create compelling vision, strategy and content that drives awareness and business decisions
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