CNC Machining Trends in 2020

The book, The Goal, stresses the necessity of identifying the primary bottleneck in operation and determining a way to eliminate it or increase its throughput. In the novel, the bottleneck is easily identified by identifying where production parts are stacking up in the process. Once identified, another machine is introduced in that process step to run parallel jobs and better streamline the workflow.

This approach is useful for production, but with higher demands in custom low-volume manufacturing, it is often difficult to truly understand critical bottlenecks without good data. This is where novel sensors, feedback systems, and predictive software help bridge these awareness gaps by aggregating data over various jobs to separate signals from noise on the shop floor.

Machine Monitoring Combines Hardware and Software

Manufacturing, even in small shops, is adopting the industrial internet of things, or IIOT, which utilizes a software dashboard to aggregate and report on data coming from various operations. MTConnect has built a series of hardware integrations for machines that monitor and output machine data, which can be used to schedule work more effectively. MTConnect’s hardware is installed on machining centers or can use integrated sensors, which are becoming more standard with new equipment.

The company Amper has a novel alternative to MTConnect’s retrofits. Amper has an IIOT device that wraps around the shop equipment’s power cable and monitors electric current. It then uses artificial intelligence (AI) and machine learning to build a signature library for that machine. These signatures could be when this machine is idle or when it moving, and when it is cutting material.

Once this data is gathered, trends can be identified by floor managers and company leads. This information, along with software-augmented suggestions, can help better apply resources to operations that strategically increase overall output. More importantly, the effect of shop floor decisions can be measured and improved upon.

Another important trend in machine monitoring is the improvement of preventative maintenance, which means using predictive models and data to ensure downtime is scheduled and not a surprise. This is historically difficult without IIOT-enabled feedback loops, such as in a generational shop where machines are from various OEMs and were purchased over time.

Tool Presetters and In-Machine Measurement

Tool presetters measure the cutting faces of a tool and ensure the machine program’s assumption of tool length and diameter is accurate to reality. Presetters can be stand-alone units or integrated within the machine platform. The benefit of having a presetter installed within a CNC machine is that the tool can automatically check tools during changes. For example, a presetter may identify broken or worn endmills and drills during a change and prevent upstream rejections. Tool presetters can provide feedback and in-situ correction for most new machining CAM software and platforms.

Presetters, combined with machine probe kits like those from Renishaw, significantly mitigate risks during operation. These sensors provide concrete, in-situ feedback for what the CAM program has predicted digitally. Vertical and horizontal machining units with CMM touch-trigger probes can double-check manual indication of part setups and provide minor corrections to maintain repeatable, consistent quality over multiple cycles. Probes can also provide accurate measurements of features, and savvy operators will utilize them to check for features during multi-operation jobs to ensure the part and program are matched appropriately. This use is extremely valuable for shops that have separate programmers and operators over multiple shifts to reduce scrap.

While in-situ IIOT feedback loops are finding a common home in larger shops, even smaller machine shops are now considering digital probes, wifi connections, and embedded machine monitoring a standard for their equipment. In 2020 and beyond, more integration will happen across digital and physical machining platforms to build better decision-making tools for high-throughput machining businesses.