Artificial Intelligence: A Digital Manufacturing Catalyst
January 8, 2025 | News | No Comments
Time to read: 6 min
Artificial intelligence often gets a bad rap. When management wants to start using AI to make the factory more productive, people assume computers will take their jobs. But the truth is, AI isn’t here to replace critical frontline workers. Artificial intelligence is a manufacturing catalyst tool that enables workers to accomplish higher-value tasks with greater accuracy, fewer errors and fewer accidents. Instead of a dystopian nightmare, AI and digital manufacturing can be a boon to a factory’s workforce.
AI works best when it has access to large amounts of data to find patterns and insights. With digital manufacturing increasing the sources of data throughout a factory floor, AI is primed to support workers by finding insights to improve production automatically. With AI, machines break less often, higher-quality goods are produced, and people are spared from many mundane tasks, freeing them to do higher-value work. Embracing AI on the factory floor gives workers the opportunity to stay a step ahead of the competition.
What is AI?
Artificial intelligence has become a buzzword, but what does the term really mean? AI by definition tries to replicate human decision-making and intelligence — and true AI is indistinguishable from human intelligence. In practice, artificial intelligence looks for patterns and signals in large datasets to deliver insights that can be hard to discover otherwise.
Computers are exceptional at processing large amounts of data, which makes them much better at spotting these insights than humans. While artificial intelligence covers a wide array of technologies, machine learning and neural networks are two popular implementations of artificial intelligence.
Machine Learning
On the road to true artificial intelligence, machine learning is a great first stop. Machine learning recognizes patterns in data and then applies those learnings over time to make better decisions. With machine learning, an AI algorithm can be trained on how to do a specific task like identifying pictures of cats or detecting vibrational frequencies that indicate that a bearing is failing. With machine learning, the algorithm processes a large amount of data with known answers, learns from that data, and then can make better predictions as it processes new data.
Neural Networks
Machine learning builds artificial intelligence in a way that works specifically for computers. Computers typically work in a very linear way. Data is fed in from one direction, is processed, and comes out at the end. The human brain, however, differs from computers with the brain’s network of interconnected synapses. These connections allow many parts of the brain to process and analyze data simultaneously before arriving at an answer.
Based on the work of neuroscientists, computer programmers have attempted to replicate these connections through neural networks. Neural networks function by breaking complex tasks down into parts that can “learn” new behavior through interaction. These types of models tend to be more flexible and responsive but can be harder to design.
How Does AI Enable Digital Manufacturing?
AI, machine learning, neural networks…what does any of that have to do with manufacturing? AI enhances digital manufacturing by processing the vast amounts of data generated before, during, and after manufacturing to help inform better decision-making and increase efficiencies. In short, artificial intelligence makes factories better.
Here are three key areas where AI enables digital manufacturing:
- Predictive maintenance
- Higher-quality parts
- Increased efficiency
Predictive Maintenance
Unexpected downtime can wreak havoc in a manufacturing environment. Even with good maintenance programs, machines can still fail at unpredictable times, causing missed ship dates and extra labor costs to get things running again. With artificial intelligence, data sources like motor currents, vibrational frequencies, and amplitudes, or even sounds can be used to monitor equipment as it operates and predict failures before they occur.
Just like the well-trained mechanic who has worked on a piece of machinery for many years, AI learns from datasets to find trends. An example of this may be slight increases in current on a motor over time that show corrosion building on the electrical contacts. Or the appearance of a certain vibrational frequency that shows up and grows in amplitude over time which indicates that a bearing needs to be regreased. In either case, purchasing now knows in advance, has time to get parts in stock, and maintenance can schedule repairs at an advantageous time.
Maintenance has evolved from reactive to preventative, to predictive. Reactive maintenance occurs after a machine has broken. Preventative maintenance is done on a fixed schedule to keep things operating correctly — like changing air filters every three months. Predictive maintenance, enabled by AI, fixes things strategically when needed so that it never breaks at all.
Higher-Quality Parts
Quality control is critical in every manufacturing environment. As pressures to scale and accelerate production increase, maintaining quality can be a challenge. AI is uniquely positioned to help in this regard because of all of the data present throughout the manufacturing cycle.
Starting in the quotation phase of manufacturing, artificial intelligence can analyze a natively digital file of a part or assembly to be manufactured and perform a DFM analysis. Based on these results, recommendations can be quickly made to tweak the design so that manufacturing is more straightforward.
With a digital manufacturing environment, AI is enabled to assist on the shop floor too. Whether analyzing production routes to manage load and increase efficiency or modifying batch sizes to meet lead times, AI can process the data from many variables on a shop floor. With computer vision systems, artificial intelligence can provide critical quality control functions to ensure product quality remains high and bad parts are rejected quickly. Trends in part quality can also be identified, and AI can make process change recommendations to keep part quality high.
In a world of increasing lead times and supply chain constraints, AI is the partner we’ve been waiting for. It takes large teams to wrestle with the amount of data from suppliers and vendors that change daily — and the impacts of those changes often reverberate through the entire system. With AI, these impacts are more easily identified, so plans can be formulated to circumvent issues as they arise.
Increased Efficiency
AI is not replacing workers but instead helps them produce greater results. Artificial intelligence helps workers make better decisions with clearer information. And better decision-making means getting more done with less effort than they could before.
No longer do workers have to make decisions based on a hunch. Instead of asking purchasing to keep spare parts on hand, inventory can be reduced and ordered based on AI predictions about when a given part will need to be replaced. That part can then be changed out at a scheduled, regular time without the disturbances that accompany an unexpected breakdown.
In addition to better decision-making from clear insights, AI frees workers to focus on higher-value work. Many current jobs that require mundane or highly repetitive tasks can offload those tasks to AI. Humans are much better at creative problem solving, whereas computers are better at doing those repetitive tasks. Letting computers do what they do best paves the way for humans to achieve more. When leveraged properly, AI is a catalyst to enable workers and manufacturing environments to achieve greater results with less effort.
AI and the Future of Manufacturing
So, now you understand the benefits of artificial intelligence and digital manufacturing. AI enables factory workers to accomplish more by delivering greater insights into manufacturing data. Our robot overlords aren’t yet coming to take our jobs — but the robots are tools that can make manufacturing much more efficient.
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