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Remanufacturing and Advanced Machining
3. Signal interpretation. To make a tool “smart,” i.e., able to react adequately
to work conditions, transmission and integration of signals are required. In
this respect, the possibility of different sensors’ communication with each
other during machining seems to be the most promising, as well as selflearning probabilistic neural network. In ultrahigh-precision micromilling,
a smart machining platform is able to significantly reduce vibrations.
Möhring et al. (2020) developed a smart milling tool with indexable inserts that
comprises a cyber-physical system. It is based on wear and force measurements with
determined VB values of wear land at the flank face and SB cutting edge misalignment at the rake face. The data obtained from the measurements are filtered, converted, and stored, and then compared with the reference process and previously
collected measurement data. This way, the user receives all the necessary data to
determine the service life and wear conditions of a tool.
Smart cutting tools have autonomous sensing and self-learning capabilities and
are able to operate in-process sensoring and actuation (Cheng et al., 2017). With
these capabilities, they ensure improved quality of a machined part and its surface
roughness, reduced fabrication costs, and higher manufacturing productivity. The
following characteristics of smart cutting tools should be listed (Cheng et al., 2017):
• Plug-and-play
• Autonomous operation
• Self-condition monitoring
• Self-positioning adjustment
• Self-learning
• Compatibility with highly automated CNC environments
The authors (Cheng et al., 2017) describe in detail four types of smart toolings they
developed, namely:
1. Cutting force measurement-based smart cutting tool, which employs
a piezoelectric film or a surface acoustic wave sensor as the sensoring
element.
2. Cutting temperature-oriented smart cutting tool, focused on a controlled
internal cooling system, reducing the cutting temperature around the cutting edge in order to extend tool life and produce a better surface finish on
the workpiece.
3. Fast tool servo employed to position a cutting tool operating in a dynamic
cutting and actuation scenario with a high precision accuracy and wide
bandwidth, for precision machining of complex geometrical features in
particular.
4. Smart fixtures and smart collets to measure cutting force, cutting temperature, tool positioning and actuation in process, separately or combined.
Smart collets and smart fixtures are essential as they enable smart machining by machining system devices.
Remanufacturing and Advanced Machining
3. Signal interpretation. To make a tool “smart,” i.e., able to react adequately
to work conditions, transmission and integration of signals are required. In
this respect, the possibility of different sensors’ communication with each
other during machining seems to be the most promising, as well as selflearning probabilistic neural network. In ultrahigh-precision micromilling,
a smart machining platform is able to significantly reduce vibrations.
Möhring et al. (2020) developed a smart milling tool with indexable inserts that
comprises a cyber-physical system. It is based on wear and force measurements with
determined VB values of wear land at the flank face and SB cutting edge misalignment at the rake face. The data obtained from the measurements are filtered, converted, and stored, and then compared with the reference process and previously
collected measurement data. This way, the user receives all the necessary data to
determine the service life and wear conditions of a tool.
Smart cutting tools have autonomous sensing and self-learning capabilities and
are able to operate in-process sensoring and actuation (Cheng et al., 2017). With
these capabilities, they ensure improved quality of a machined part and its surface
roughness, reduced fabrication costs, and higher manufacturing productivity. The
following characteristics of smart cutting tools should be listed (Cheng et al., 2017):
• Plug-and-play
• Autonomous operation
• Self-condition monitoring
• Self-positioning adjustment
• Self-learning
• Compatibility with highly automated CNC environments
The authors (Cheng et al., 2017) describe in detail four types of smart toolings they
developed, namely:
1. Cutting force measurement-based smart cutting tool, which employs
a piezoelectric film or a surface acoustic wave sensor as the sensoring
element.
2. Cutting temperature-oriented smart cutting tool, focused on a controlled
internal cooling system, reducing the cutting temperature around the cutting edge in order to extend tool life and produce a better surface finish on
the workpiece.
3. Fast tool servo employed to position a cutting tool operating in a dynamic
cutting and actuation scenario with a high precision accuracy and wide
bandwidth, for precision machining of complex geometrical features in
particular.
4. Smart fixtures and smart collets to measure cutting force, cutting temperature, tool positioning and actuation in process, separately or combined.
Smart collets and smart fixtures are essential as they enable smart machining by machining system devices.