Machining with sensor technology Sandvik eliminates the “blind spot” at tool engagement

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Swedish cutting-tool specialist Sandvik Coromant is making metal cutting both observable and controllable with sensor-based tools.

Industrial automation has made rapid progress in robotics and connected analytics, as Sandvik Coromant also notes. In machining, however, there has so far been a “blind spot”: the moment when the cutting tool meets the workpiece. Although companies have automated monitoring, connected machines and installed dashboards, this moment still often depends on the operator’s intuition or on post-process inspection. Sandvik Coromant has therefore developed a solution to bring greater transparency to this critical stage. The following explains why sensor-based tools are the next step in machining automation.

Fast programs and robots are no longer enough

Expectations for “smart” manufacturing are high. According to Deloitte’s “2025 Smart Manufacturing and Operations Survey”, 92 percent of the companies surveyed believe that this type of manufacturing will be the most important factor in remaining competitive over the next three years.

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Modern machining processes have a positive impact on productivity and capacity. However, simply running faster programs or installing robots around a machine is not enough if the cutting process itself still relies on instinct or overly conservative parameters. The prerequisite for genuine automation is therefore the ability to detect in real time what is actually happening when the tool engages with the workpiece — and to take countermeasures before damage occurs and downtime escalates.

With cutting tools, adapters or toolholders equipped with integrated or mounted sensors, the situation changes, according to Sandvik Coromant. These systems record key machining parameters during the process. They can detect cutting forces and vibrations, identify surface chatter at the tool tip and transmit this information in real time to an operator interface or the machine control system.

Anomalies can be identified quickly and appropriate corrective action can be taken. This may involve a brief machining interruption, an adjustment of parameters or a tool change. The decisive point, however, is that reproducible interventions within the machining process — in other words, workflows that reliably deliver the same results when repeated — ensure continuity across all shifts.

Conventional tool-change philosophies lead to a dead end

According to Sandvik Coromant, tools with sensors increase productivity by stabilising the cut and reducing unplanned downtime. Once a process is genuinely secure, metalworking companies can safely extend unattended production windows. The focus shifts from high staffing requirements in production to sustainable time savings.

Another advantage of sensor-controlled cutting tools, according to the Swedish company, is longer tool life. Many manufacturers set conservative replacement intervals because they fear unexpected failures. However, this approach means that the potential service life of the tools is not fully utilised and costs rise unnecessarily.

Other manufacturers simply exceed the recommended intervals, which can then lead to tool breakage and higher costs for scrap and recovery time. With live signals from the process, however, decisions are based on evidence.

The skills shortage adds further pressure

For example, an indexable insert is replaced only when the signal signature shows that it has reached the end of its service life — not because a counter has expired or because someone merely assumes that it is time for a change. Across a machine fleet and over a production year, this approach has been shown to result in longer cutting times, fewer interruptions and higher utilisation, without the need to hire additional staff.

According to the World Manufacturing Foundation, 74 percent of companies are also struggling to recruit the skilled workers they need. As this problem is expected to intensify in the future, companies must provide internal training to upskill their workforce. However, metalworking managers can make a number of mistakes if they do not use data as a learning tool, Sandvik Coromant points out.

Sensor-equipped tools preserve machining expertise

If the instinctive understanding of an optimal cutting process is not captured in systems, know-how can be lost when new employees join the company and experienced operators leave. According to Sandvik Coromant, sensor-based tools help turn years of experience into explicit, training-ready data that new employees in production can rely on.

By storing signal curves, threshold values and event logs, companies create a reference source that guides parameter selection and supports troubleshooting across shifts and sites. When knowledge is not held only by a few individuals but is also stored in data and models, decisions become repeatable and verifiable.

Production managers receive traceable cutting data that supports audits and customer documentation. Engineers, meanwhile, gain a more solid basis for continuous improvement because the process history becomes a dataset rather than an anecdote. Above all, machine operators can focus on process optimisation instead of listening for unusual noises.

Detecting machining errors in real time

In many manufacturing companies, visualisation and automation are still frequently confused, Sandvik Coromant has found. A chart on a tablet may be useful and undoubtedly provide valuable insights, but it still requires a person to recognise a problem and respond under pressure.

To achieve “real” automation, in which the system automatically maintains the process limits, sensor-based tools are required. If chatter exceeds a defined range, for example, or if cutting forces rise so sharply that a failure is likely, the control system immediately interrupts the machining process, retracts the tool, changes the feed rate or initiates a tool change, as Sandvik Coromant explains.

This allows component quality, tools and equipment to be protected immediately — before defects are detected by skilled personnel.

Machining monitoring becomes part of the machine

Insights into the ongoing cutting process effectively close the feedback loop and ensure stable, repeatable cycles as well as operating times that do not require staff supervision. In practice, this enables companies to plan unattended manufacturing reliably and produce around the clock.

The control system detects conditions outside the specified limits and automatically applies the configured protective measures instead of relying on a person to notice a trend after the fact. By bridging the gap between sensor and control system, monitoring becomes a machine-internal process that consistently protects the cutting process.

To meet the different stages of development within manufacturing companies, the sensor-based tool solution CoroTurn Plus has been designed with two complementary functional levels. The following example from machining practice illustrates how this works.

How smart Sandvik Coromant systems work together

When Sandvik Coromant’s Coroturn Plus system transmits live data to Coroplus Viewer on a PC or tablet, operators receive passive real-time insights into surface chatter and cutting forces. Audible alarms can also be received when threshold values are exceeded.

In addition, users can identify trends compared with reference processes, receive warnings when limits are exceeded, review values and deviations, and mark events in order to accelerate root-cause analysis. Over time, the collected signals show when an indexable insert has reached the end of its service life. This enables employees to replace the insert at the right time and avoid both premature changes and serious machine failures caused by tool breakage.

Machine-integrated protection is also part of the concept

According to Sandvik Coromant, the next stage is machine-integrated protection, in which Coroturn Plus is combined with Coroplus Connected. In this mode, the same signals are forwarded to the machine’s CNC. Users then define threshold values for chatter, tool load and vibrations via the software or by using NC code.

If an unexpected event occurs, the control system automatically initiates protective measures. These include stopping after a blockage, optional pauses and overriding feed rate and cutting speed. According to Sandvik Coromant, this enables improved, machine-controlled decision-making and therefore “real” automation of machining processes.

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