How to take advantage of AI's impact on manufacturing
Artificial intelligence is reshaping manufacturing at every level, transforming traditional automation into an era of data-driven productivity. AI is driving increased productivity and innovative manufacturing practices. Dr. Asif Rana, President of Nexus Connected Worker at Hexagon Manufacturing Intelligence, writes how these innovations can be leveraged by integrating robotics-driven hardware automation with AI-led software solutions.
Despite many recent advancements, AI remains an unfamiliar concept to many within the manufacturing industry. Industry professionals and experienced engineers are in the midst of a major transition.
Dr. Asif Rana, President of Nexus Connected Worker at Hexagon Manufacturing Intelligence
As seasoned experts retire, their invaluable historical knowledge and insights risk being lost, underscoring the need to integrate artificial intelligence into manufacturing so that the next generation can capture and build on this wealth of knowledge and insight.
The most significant advantage of AI lies in its ability to rapidly analyze vast amounts of data and uncover patterns and trends that might otherwise go undetected by humans. This presents a remarkable opportunity for businesses to enhance their decision-making processes by leveraging AI.
The Benefits of Large Language Models (LLMs)
Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.
At Hexagon, company officials say their goal with AI is twofold: create the best possible products for real industrial outcomes and -- as importantly -- continuously improve the way that we work.
LLMs represent a breakthrough in natural language processing. By training on massive datasets of text data, they can produce human-like insights and text. This ability to understand and generate text makes them incredibly versatile. When focused on a particular domain like manufacturing, LLMs can become powerful AI assistants, capable of understanding complex manufacturing processes and providing valuable insights.
This includes predictive maintenance apps, computer vision quality inspection tools, production planning assistants, and more. With LLMs' increasing capabilities and decreasing deployment costs, their adoption is only increasing. We can expect to see significant growth in their use across the manufacturing industry as more manufacturers realize their potential.
Driving Predictive Insights
One of the biggest values of LLMs in manufacturing is their ability to analyze massive amounts of data and identify patterns. This allows manufacturers to gain predictive insights that can greatly streamline their operations.
Hexagon has been working seriously to incorporate AI into their solutions since 2012.
For example, LLMs can forecast equipment failures before they happen based on past maintenance logs, error logs, sensor data, and more. By processing thousands of data points across machines, the LLMs can identify signals that predict imminent failures. Manufacturers can then proactively schedule maintenance at optimal times. This ability to predict and prevent equipment failures can significantly reduce unplanned downtime, leading to increased productivity and reduced costs.
In addition, LLMs can also predict future demand with high accuracy based on historical trends, economic indicators, and emerging customer preferences. By processing both structured and unstructured data, the LLM can uncover demand drivers difficult for humans to manually piece together. Manufacturers can align production schedules and inventory levels to meet demand. This reduces overproduction and excess inventory costs, leading to more efficient operations and improved profitability.
To embed AI into their work at Hexagon, they examined how it could enhance internal functions like sales, marketing, operations and more.
In essence, as the next evolutionary step of AI, the LLMs now enable manufacturers to easily spot issues and opportunities earlier. This enables proactive optimization rather than reactive firefighting. with its ability to provide insights in minutes. For reference, MaintainX and Vanti's generative AI solutions are good examples of how this technology can be leveraged for predictive insights.
Automating Quality Control
In manufacturing, maintaining quality control is crucial, but time-consuming. LLMs can automate parts inspections to catch defects rapidly. Computer vision LLMs can scan manufactured items on the production line and detect any deviations from design specs. These models can be fine-tuned on thousands of images of passing and failing parts to learn subtle visual defects.
For example, AI inspection can catch microscopic cracks or inadequacies human inspectors would likely miss. By integrating AI into the quality control process, manufacturers can significantly improve their defect detection rates and reduce the time and resources spent on manual inspections.
AI capabilities can be embedded directly into hardware and software.
In addition to analyzing machine sensor data to detect quality issues, AI can also analyze machine data to determine if there are any abnormalities. The LLMs can help identify patterns leading to defects across sensors for temperature, vibration, pressure, and more. Anomalies in readings can flag potential defects.
With this automated quality inspection, detection rates are improved and manual oversight is reduced. Products with fewer uncaught defects lead to lower warranty costs and higher customer satisfaction. Freeing up human inspectors enables them to focus on higher-level process improvements.
How to make AI in manufacturing a success
For AI and LLMs to truly transform manufacturing, they must first be tailored to specific domains. It is not just about connecting to the right data sources, but also about developing tools for prompting that are specific to the challenges and processes of each industry.
Domain specificity ensures that AI solutions are relevant, practical, and capable of addressing the unique demands of different manufacturing environments. This demonstrates the need for industrial LLMs (or domain-specific LLMs) for the proper and accurate application of LLMs in manufacturing.
Hexagon officials believe AI is empowering more people and enabling more industries by making physical data actionable back in the real world.
Furthermore, standardized development and operational processes are necessary for the widespread and successful adoption of AI in manufacturing. Establishing common frameworks and protocols for implementing AI technologies is critical to ensure compatibility, interoperability, and security across different systems and platforms. Standardization also facilitates easier adoption and integration of AI technologies, helping manufacturers navigate the transition to AI-powered operations with greater ease and efficiency.
Maximizing the potential benefits of AI
The AI transformation in manufacturing is set to introduce a new level of innovation with the advent of LLMs. To keep up with this rapid advancement, manufacturing leaders must make informed decisions.
Preparing for this shift means implementing enterprise-wide AI transformation initiatives to standardize AI development and operations processes, which will create the foundation for fully leveraging AI's benefits. The integration of AI into the manufacturing industry promises to bridge the gap left by retiring experts and propel the sector into an era of unprecedented efficiency and innovation.
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