CNC machining operations play a crucial role in the production of various parts and are commonly recognized as the driving force behind modern manufacturing processes. Industries such as automotive, medical, aerospace, gas and oil, and warehousing services rely heavily on CNC machining to fabricate components for diverse applications. As manufacturing methodologies continually evolve and new technologies are introduced, it becomes crucial to take into account the future of CNC machining operations.

Machine learning (ML) is a field focused on computer algorithms that enable computers to learn from data and past experiences, allowing them to discover patterns and make predictions autonomously, without the need for human intervention. ML, along with its applications in various domains, is considered a fundamental aspect of artificial intelligence.

The way a machine learns, adapts, and optimizes output can also be influenced by real-time data, analytics, and deep learning. Data sets are essential for operators to understand how a machine works and, eventually, how a whole floor of machines works together. Due to the development of affordable, reliable, and resilient sensors and acquisition, novel implementations of machine learning approaches for tool condition monitoring can be presented. Machine learning systems are capable of completely examine data and identify various types of areas which should be modified.

Here are some ways in which AI and machine learning can be applied in CNC machining:

• Process Optimization
• Predictive Maintenance
• Intelligent Tooling
• Quality Control
• Adaptive Control
• Production Planning and Scheduling

Machine learning is becoming more and more integrated into manufacturing environments. Machine learning is proliferating throughout society and business.

However, much of today’s published ML research is focused on the machine learning algorithm. Building and then deploying ML systems (or applications) into complex real-world environments requires considerable engineering acumen and knowledge that extend far beyond the machine learning code, or algorithm. Clearly, there are strong benefits to deploying ML systems in manufacturing. Therefore, an explicit articulation of best practices, and a discussion of the challenges involved, is beneficial to all engaged at the intersection of manufacturing and machine learning. As such, we have highlighted five best practices, discovered through our research, namely the following:

1. Focus on the data infrastructure first.
2. Start with simple models.
3. Beware of data leakage.
4. Use open-source software.
5. Leverage computation.

Within the broader machine learning community, there is a growing acknowledgment of the benefits of a strong data infrastructure. The rise of deep learning has led to much focus, from researchers and industry, on its application in manufacturing.

However, it is best to start with “simple”, classical ML models. The work presented here relied on these classical ML models, from naïve Bayes to random forests. These models still achieved positive results.

There are several reasons to start with simple models:

• Simple models are straightforward to implement. Modern machine learning packages allow the implementation of classical ML models in only several lines of code.
• Simple models require less data than deep learning models.
• Simple models can produce strong results, and in many cases, outperform deep learning models.
• Simple models cost much less to train. Even a random forest model, with several terabytes of data, will only cost a few dollars to retrain on a commercial cloud provider. A large deep learning model, in contrast, may be an order of magnitude more expensive to train.
• Simple models allow for quicker iteration time. This allows users to rapidly “demonstrate the practical benefits” of an approach, and subsequently, avoid less-productive approaches.

The benefits, and even preference for simple models, are becoming recognized within the research communities. In fact, more complicated methods can yield over-optimization. Recent interviews with machine learning engineers across a variety of companies showed that most of the engineers prefer the use of simple machine-learning algorithms over more complex approaches. Namely, one should focus on the data infrastructure first; begin modelling with simple models; be cognizant of data leakage; use open-source software; and leverage advances in computational power.

Collecting more data would improve results and build confidence in the methods developed here. In addition, the ML system should be deployed in the production environment and iterated upon there. Finally, the sharing of the challenges, learnings, and best practices should continue, and we encourage others within manufacturing to do the same. Ultimately, understanding these broader challenges and best practices will enable the efficient use of ML within the manufacturing domain.