According to the recent Grandview Research Report, CNC Machines Market Size will be worth $100.86 Billion by 2025. This implies a rising demand by OEMs and other users for controlling budgets, minimizing the human workforce, and reducing rejections in mass production. This subsequently indicates an upswing in CNC applications that typically include cutting, drilling, milling, knurling, deformation, facing, boring, moulding, turning, etc.
Computer Numerically Controlled (CNC) machines are used in the production of industrial components with minimum or low human intervention. CNC machines are highly productive and reduce the cost, provided used efficiently. These machines have high accuracy and flexibility in manufacturing but are costlier than conventional machines. Precision spindle bearings and the spindle itself is the heart of these machines. However, 26% of the downtime in the automotive industry is due to spindle and tool failure.
Spindle, a critical subsystem, is a rotating axis with a taper at one end where the tool holder or work holder fits. The spindle includes a rotor shaft, bearings, and clamping system. Spindle operates at different at high operating RPMs based on all kinds of material that are cut with various types of tools. Its uptime and reliability are vital. Breakdown of spindle impacts throughput and hence monitoring it is extremely necessary.
The spindle can either hold a job or a tool mounted vertically or horizontally. The cutting zone where the spindle operates is a harsh environment caused by chips and coolant in the machine. A plug and play device that can withstand this environment and sense the behaviour of single or multiple spindles working in parallel would be most useful. Source of vibrations are many in a machine, vibrations from bearing, cutting tool, etc.
For a new CNC machine, installing a quickly installable sensor that captures real-time harmonics (vibrations) of spindle enables the Maintenance team to carry out a spectral analysis. In the case of a legacy CNC machine, remotely analysing vibrations on spindle bearings ensure the sustainability of the quality of production. The spindle bearings operate without load, or under very light load, and at high speeds. The vibrations observed in all the respective cases differ and confirm its independent fingerprint. The high-frequency vibrations of spindle bearings need to be examined in real-time as they may produce undesirable effects such as defective output to surface finish and accuracy, untimely wear and tear of internal mechanical parts, reduced tool life, unwanted acoustics and physiological harm to operators.
Condition monitoring for a spindle involves capturing every real-time data linked to machine tool failures, unintended human behaviour, machine overloading, and is ideal for further analysis and diagnostics. Health monitoring of spindle not only enhances the overall performance of the CNC machine but also potentially detects its inefficient functioning. Spindle failure due to higher spindle speeds, feed, and rapid traverse rates, acceleration rates and erring precision in its positioning can also be prevented by tracking vibration data of bearings. Rework and rejection of the finished product, and extra inventory of spares can thus be controlled. Prevention of a batch of damaged goods, which is certainly unacceptable for a manufacturer especially when raw material is not cheap, and burdening him with unnecessary wastage can be regulated.
It is not easy to implement vibration monitoring for a spindle as multiple factors which include the type of spindle, frequency range to be measured, spindle rotational speed, and position of mounting of the sensor on spindle need to be considered. Besides this sensitivity and deployment of the sensor, technology implemented, data capturing mechanism, threshold settings, measurement routine, and methods of data analysis are important to extract precise performance of the system.
The Fast Fourier Transform used in reliability optimization focuses on the source of the most probable root cause of spindle vibration. This provides an insight to implement corrective measures and accordingly plan Predictive Maintenance. Timely remedial steps prevent possible downgrading and limit breakdown that results in poor quality and quantity output.
Though Condition Based Monitoring (CBM) expertise in a plant enables the CNC machine to produce to its optimum performance, it may be difficult to detect uniform wear failures or fatigue. The CBM team may also face a challenge to convert streaming data obtained from the sensors into a predictive maintenance schedule.
Infinite Uptime’s patented Industrial Data Enabler (IDE) is a compact monitoring device that picks up vibrations, acoustics, and temperature at the point of occurrence. The dashboard software through its unique algorithm forecasts the failure of any rotating equipment like motors, gearboxes, fans, pumps, spindles, etc. The IDE empowers CBM personnel and potentially ensures maximum availability of the system. The IDE is a device transforming any conventional plant into a DIGITAL FACTORY.