How AI and Digital Twins Might Shape the Future of CAM

For decades, CAM systems have been a cornerstone of CNC machining, offering complex tools to translate designs into precise instructions for machines. However, the pressure on this complex link in the parts production chain has been increasing. One major concern is the skills gap; finding, training and retaining machinists to the required level of expertise is increasingly difficult. 

Furthermore, amidst global competition, manufacturers are facing rising material and labor costs, while customers are asking for more complex and precisely machined parts at lower prices. Under these dynamic conditions, operational flexibility and cost-efficiency are paramount. 

That said, let’s learn how more sophisticated digital twins with AI enhancements are addressing current pain points with CNC machine programming and what benefits we can look forward to in the future.

Limitations of Conventional CAM Systems

Historically, CAM systems focused on geometric approaches to toolpath generation, with little consideration for machine-specific constraints. Today, we have two main approaches taken in traditional CAM systems.

1. Machine-agnostic programming. In this method, the machine is considered only as an afterthought. Programming is performed without factoring in specific machine characteristics, and the program is later translated into machine language through a postprocessor. This translation step converts program data into machine-specific code, which is then sent to the machine.  

However, this approach often leads to challenges. Since the machine wasn’t considered from the beginning, errors are likely. For example, there might be an unforeseen tool path or geometrical constraint, leading to collisions and damaged parts or tools. In the best case, such errors are caught in time. But reprogramming is nonetheless time-consuming and inefficient. In the worst case, they result in a damaged machine, requiring costly repairs.  

2. Early machine consideration. A newer and more common approach involves considering the machine earlier in the programming process. This is achieved using geometric models that provide a virtual representation of the machine that the programming software can take into consideration. While this method represents progress, it has its limitations. These representations, sometimes known as “thin digital twins,” lack a smart understanding of the nuances of how the machine behaves in various real-world scenarios. Therefore, the programmer still needs to know the machine well enough to recognize, for instance, when to use special functions such as tool control point (TCP) or tilted plane for controlling the coordinate output. Furthermore, they leave unanswered questions about how to account for different user needs, interactions and preferences. 

Another problem is that while these systems enable users to make programming decisions specific to the machine they are working with, programming becomes cumbersome and error prone. For example, any time a user inadvertently makes a choice that is incompatible with the machine’s capabilities, the system has to flag an error, leaving the user to troubleshoot and adjust.  

The bigger issue, however, is inflexibility. If programming decisions are tied to a specific machine, then as soon as it’s necessary to change the machine, perhaps due to machine availability or other production requirements, the user will be forced to reprogram the G code entirely. This makes the entire workflow rigid and inefficient.  

Enter True Digital Twins and AI: A Smarter Approach

The new approach is to have a flexible CAM system in which the properties of machines, the implicit knowledge on how to program them, and the behaviors and preferences of the operators are part of an overall intelligent solution. 

In this approach, an advanced digital twin is built that captures the precise geometry, capabilities and behaviors of the machine including information about tooling and workholding setups. This enables the CAM system to make informed programming decisions without requiring user intervention. Furthermore, the machine awareness of the shopfloor realities embedded in the CAM system can be updated with data from actual operational experience using the machine learning capabilities embedded into these modern solutions. This marks a fundamental shift in how CNC machines are used. 

An advanced digital twin captures the precise geometry, capabilities and behaviors of the machine tool including information about tooling and workholding setups.

When programming with the new breed of machine-aware smart digital twins, the system is largely machine-agnostic during the initial stages. Users define a sequence of steps or processes for how they will transform the raw stock piece into the finished part. The system then automatically adapts these processes to the chosen production machine, using the understanding provided by the detailed digital twin. This is made possible by a machine-aware AI engine, which intelligently handles decisions on behalf of the user.  

This approach not only simplifies programming but also ensures flexibility and accuracy, enabling users to focus on defining the desired outcome rather than grappling with machine-specific constraints. For example, such systems “know” if a machine can cut a certain part, and support the choice of machine for each job based on cycle time, machine running cost and cost of equipment information.

By investing in an AI-empowered, digital-twin-based CAM solution, manufacturers can benefit from:  

• Machine-aware optimization. The toolpath program adapts to the full capabilities of the machine, optimizing tool paths for reduced cycle times, extended tool life and enhanced performance.
• Adaptive machining. The CAM system handles milling on a lathe, turning on a mill and seamless transitions between machines without reprogramming.
• Automatic machine swapping. Programs automatically update to accommodate changes in machine setups or production volumes.
• Stock-aware tool paths. Dynamic adjustments of tool paths occur based on the real-time state of the stock, minimizing air cuts and maximizing machining efficiency.

One example of a system that offers all these benefits is Hexagon’s Esprit Edge. It uses patent-pending machine-aware algorithms and a digital twin of the CNC machine, tooling and workholding elements to deliver CNC programming, optimization and simulation functions that help the programmer exploit the full capabilities of the machine.

Addressing the Skills Gap

Emerging smart digital twin CAM solutions also help address the manufacturing skills gap. For example, with a modern interface and automation tools, Esprit Edge makes training new employees easier. Traditional CAM programming often requires skilled machinists or programmers with expertise across multiple machines in a shop. New programming alternatives reduce this dependency by taking over much of the decision-making, enabling less experienced employees to achieve results that previously required expert knowledge. At the same time, the software empowers skilled workers to focus on higher-value tasks, such as fine-tuning processes or optimizing output.

While the programming system automates much of the process, it’s not about replacing expertise entirely — just like in metrology, in which complete automation often falls short. Skilled professionals still play a crucial role in refining the final 10% of the process. By automating repetitive tasks, the system enables skilled labor to concentrate on areas where their expertise can make the most impact, maximizing the value of the workforce.

Minimizing Wasted Effort

One of the critical advantages of these new systems is the ability to produce high-quality machine programs. The postprocessor, which converts program data into machine instructions, is often a weak point in CAM systems. Traditional systems might generate programs that are 90% accurate, leaving users to manually tweak the remaining 10% — a tedious and error-prone process. This lack of precision also requires extensive testing and dry runs, in which operators run the machine without a workpiece to validate the program.

However, if a high-fidelity digital twin ensures that the generated programs are accurate and reliable right from the start, then it is possible to reduce or even eliminate the need for dry runs. This not only saves time but also minimizes machine downtime and maximizes productivity.

By automating repetitive tasks, the programming system enables skilled labor to concentrate on areas where their expertise can make the most impact.

For example, a company in the oil industry reported that the combination of a more accurate digital twin with AI-empowered machine awareness enabled it to cut the time needed to switch production from one machine to another from two days to a matter of minutes. This was possible due to the machine swap capability of Esprit Edge, whereby the program created for one machine is adapted automatically to the new machine without the need for tedious reprogramming.

Pooling Expertise to Everyone’s Advantage

Traditionally, many CAM systems treated digital twins as bespoke projects tailored to the needs and preferences of each individual customer. But that paradigm has changed. New solutions come with a globally curated library of thousands of digital twins that users can download and easily configure to their needs by toggling options. Such solutions have been made possible by industry-wide cooperation between machine OEMs and CAM solutions providers and by solutions that are cloud-native by design. Cloud-based capabilities also enable teams to share data and best practices, fostering collaboration and standardizing workflows across facilities.

This centralized approach ensures consistency and continuous improvement. For example, when a digital twin is developed, refined or enhanced by anyone within Hexagon or its broader partner network, those improvements are automatically shared with the global community. This means that all users benefit from updates, bug fixes and enhanced accuracy in real time, fostering a collaborative ecosystem.

Learning from Operators and What Worked

When we talk about AI, many people think of machine learning, and the trend is clearly towards systems where the CAM will be able to learn from the best practices and experiences of individual manufacturers or the wider user community. For example, solutions are emerging that predict the most likely next programming step based on a programmer’s past input. AI-enhanced process planning solutions are also being developed in which AI helps the user to define the optimal sequence of steps for the production of a part.

With AI-machine learning, both job shops and high-volume manufacturers can start to automate based on their own best practices. Newly available systems can learn from even a small number of past scenarios or they can be trained on thousands of past jobs. Either way, these systems will be able to recommend the validated manufacturing recipe that closely fits the current job or select strategies that reflect the knowledge and proven norms of individual manufacturing companies. Note, that this technology can handle brand new parts and not just slight modifications. The machine learning system captures the methods used for all the individual part features previously programmed, then it uses that knowledge to propose a solution for the combination of features found in the new part.

These are just some of the ways that AI is being used to make advanced digital twin-based CAM solutions smarter. The benefits are significant and including improved productivity (AI-driven optimization reduces programming time, accelerates production cycles and increases throughput) and faster onboarding (AI-powered guidance ensures new CAM users can quickly become proficient, reducing the learning curve for advanced machines).

To illustrate the potential of these new solutions, Hexagon believes that its ProPlanAI solution, an add-on to Esprit Edge, could empower companies to reduce their CAM programming time by as much as 75% by reducing the number of decisions the programmer needs to make. This massive time saving can be attributed to the automation of thousands of decisions. After all, the machining process for a single feature might require the user to make some 50 or more decisions on the machining strategy and more than 30 decisions for the selection and description of the cutting tools. Considering a part could have 50 features, that amounts to several thousand decisions which the user no longer has to make due to the automation built into ProPlanAI.

The Road Ahead: Self-Learning Systems and Real-Time Feedback

While the first generation of AI empowered planning and CAM solutions already provide significant advancements, the future of CAM promises even greater innovation. Emerging trends include:

• Self-learning CAM systems. CAM solutions will continue to evolve, using machine learning to refine programming strategies and improve over time without manual intervention.
• Real-time machine feedback. A digital twin that receives data from the machine and uses this data to make its model more precise and accurate in real time is known as a bi-directional digital twin. Such systems will become increasingly common and enable dynamic responses to changing machining conditions.
• Universal accessibility. Simplified interfaces and AI-driven guidance will make advanced CAM tools accessible to all users, addressing skill shortages in the industry.
• End-to-end connectivity. Integrated cloud platforms will connect every aspect of the manufacturing process, ensuring seamless data flow and operational excellence. For example, on Hexagon’s Nexus cloud platform, part inspection data can be used to rate the quality of production processes and thereby inform the prediction engine that recommends the best processes.

Digital twins and AI are not just buzzwords; they are transformative technologies poised to redefine CAM and the broader manufacturing landscape. Embracing AI-driven, machine-aware CAM systems solves the problems of both machine-agnostic and machine-specific approaches while giving manufacturers a variety of competitive advantages. 

AI in CAM will certainly maximize throughput and improve quality. And, as AI reduces the need for complicated and repetitive CAM programming, CNC programmers will be able to turn their attention to more value-adding activities such as optimization of the process or set-up, trying different strategies or tools. As AI handles the most demanding and tedious technical tasks, it will also make working in manufacturing more interesting for creative-types.

Indeed, we could be looking forward to a renaissance in manufacturing as everyone from designers to the machine operators begin to exploit the new creative possibilities of AI-empowered manufacturing.