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As the push for digitalization continues across all industries, data is increasingly becoming the lifeblood of modern manufacturing. Standing at the cusp of the AI revolution, this has never been truer. Different areas of the manufacturing process already produce and leverage huge quantities of data in a variety of ways. But the sheer volume of this data means that many optimizations and key insights are left on the table.

While there are concerns about AI replacing workers, applying AI to part manufacturing doesn’t mean automating away people and processes. Instead AI-powered programs can act as a force multiplier, improving efficiency and productivity by augmenting existing systems. An example of this is a copilot in a computer-aided manufacturing (CAM) system, which can automatically generate toolpath suggestions by analyzing the 3D model of a part. Combining traditional production processes with smart data collection, AI and the comprehensive digital twin will be instrumental in achieving the next generation of data-driven manufacturing.

The Need for Industrial-Grade AI

While AI offers many benefits to part manufacturing, it must be applied with care. In the consumer space, the occasional error or hallucination might be acceptable. But in industry, where vast sums of money and even lives might be at stake, any mistake in production could have disastrous consequences.

To reap the benefits of AI in industry, the AI itself must be industrial grade. Answers returned by the model must be robust, reliable and repeatable so users don’t second-guess every result. Some features that set industrial-grade AI apart from other AI include continuous testing frameworks to ensure models are still giving expected results, automated processes that can check for correctness and software designed to keep humans in the loop for critical tasks. With a strong foundation in place, industrial-grade AI can then be leveraged in three ways to enhance part manufacturing: to optimize manufacturing processes, analyze manufacturing data and processes and generate manufacturing gains.

AI Optimizes Manufacturing

AI can accelerate many tasks in a machine shop or other production environment to reduce waste in labor and materials while improving production efficiency. AI is now being applied in many areas, including:

• Natural language processing (NLP) for interacting with maintenance manuals, production data and more through tools such as Siemens Industrial Copilot
• Energy optimization to generate data-driven insights that enhance the understanding of energy usage across production processes
• AI-driven CAM operation editing for faster completion of jobs

These are just a few of the ways AI is even now helping improve production efficiency. And as shops continue to invest in digitalization, the benefits of AI will also increase.

Analyzing Data for Bigger Gains

Connecting more advanced AI with shopfloor, design and production data will enable optimizations of everything from workflows to ergonomics through powerful analytics. Connecting all this information within tools like Siemens Insights Hub allows AI to be applied to everything from quality control reports to shopfloor production schedules for deeper analysis, which in turn unlocks new optimizations.

One big way AI can help improve production efficiency is through predictive quality. By analyzing defect data and correlating it with the production and performance data available from smart machines, it is possible to build an AI model that can identify key indicators of defects early in the manufacturing process. Catching these errors early will decrease waste of both time and materials. For example, chatter during a machining operation results in a sub-par surface finish and reduced tool life through uneven tool wear and tool breakage. Chatter marks are visible on machined surfaces, often showing as wave-like patterns or regular marks. AI algorithms can analyze data from various sensors measuring vibration, acoustic emissions, forces, current and more in real time to detect the onset of chatter. This allows for immediate adjustments to machining parameters before chatter becomes severe and affects part quality.

In addition to analyzing huge data sets, AI can expedite time-consuming analysis of specialized data and use cases, such as improving ergonomics for human workers. Repetitive motions can be physically taxing, especially if they require bending or reaching in awkward ways. While there is a certain amount of intuitive analysis that any person can do when it comes to repeated motion, assessing the long-term impact can be harder. By applying an AI model trained on ergonomics data and information about the mobility of the human body, we can assess the ergonomics of a particular set of movements from a single picture. AI-driven human simulation can analyze high-risk scenarios effectively. This information can then be fed back into the comprehensive digital twin to quickly and easily design a workstation that is both healthy and efficient to use, with parts and tools placed in intuitive, easy-to-reach locations.

Generating Manufacturing Gains

One of the newest and most well-known forms of AI is generative AI, with its unprecedented ability to converse in a human-like way. In industry, generative AI is positioned to stand as a bridge between people and technology, making complex tools easier to use. Going forward, generative AI will likely be a key component of no- and low-code platforms, allowing users to program complex machinery through NLP. 

An AI-driven copilot can also significantly accelerate the creation of CNC programs, calculation of speeds and feeds, and validation of tool paths. Today, using CAM software to go from a 3D model to usable G-code can be a complex and time-consuming task requiring significant expertise in both CNC machining and the specific software. While the need for a human CNC expert isn’t going to change any time soon, AI, in the form of a CAM copilot, has the ability to speed up this process by making the tools more accessible while automating many of the labor-intensive manual steps. A CAM copilot can help to automate the creation of machining strategies for CNC machines, cutting programming time from hours to minutes.

By simply selecting a feature on the 3D model, a CAM copilot can produce several suggested combinations of operations, tools, feed rates and more for user approval before automatically filling in all those values within the software. At the same time, it can be trained to understand the production machines, instantly validating if a given design and tool path could be safely produced on a particular machine. 

These types of generative AI tools can also serve as a knowledge base, learning from expert users and past work to use manufacturing methods based on the shop’s best practices. A strong industrial-grade AI deployment keeps proprietary knowledge secure and makes it more easily accessible to new hires and veteran employees alike, while also ensuring that valuable know-how isn’t lost as employees move to new roles or retire.

Analyze, Optimize and Generate with Industrial AI

As the digitalization of manufacturing continues, it will become increasingly important that companies big and small are able to leverage their data to achieve quality, sustainability and efficiency goals. AI is and will increasingly be an important way of analyzing, optimizing and generating manufacturing improvements. With everything from simple insights to full-featured assistance, AI will be a vital part of bringing data-driven manufacturing to life as it can turn otherwise unused data into a goldmine for improving efficiency across the board.