Rotary knifes/cutters play an important role in manufacturing of finished products. The rotary cutters tend to lose their cutting material over time. Hence to compensate, pressure applied by cylinder over rotary cutter needs to be changed. But this change in pressure needs to be optimum as too high pressure can lead to loss of material and too low pressure can stop cutting operation. Present application provides methods and systems for real time estimation of pressure change requirements for rotary cutters. The system first determines minimum and maximum usage limit for rotary cutter based on historical rotary cutter usage data and real-time pressure value using first trained model. The system, upon determining that minimum usage limit is reached, determines time for next pressure change based on physical parameters using second trained model. Thereafter, system compares estimated time with estimated maximum usage limit and displays notification to change pressure based on comparison.

A dental machining system for manufacturing a dental restoration including: a dental tool machine (1) which has: a dental blank holder for holding at least one dental blank (2) relatively movably with respect to one or more dental tools (3); one or more driving units (4) each for movably holding at least one dental tool (3) for machining the dental blank (2), a control unit for controlling the dental blank holder and the driving units (4) based at least on a temporal trajectory of the dental tool (3) relative to the dental blank (2) and a spatial amount of material removal from the dental blank (2) along the temporal trajectory. The control unit executes a trained artificial intelligence algorithm.

A tool state learning device has a storage unit for storing an arbitrary image captured by an imaging device imaging a machined surface of an arbitrary workpiece cut using an arbitrary tool, and a teacher data acquisition unit for acquiring, as input data, an arbitrary image stored in the storage unit, and acquiring, as a label, the state of the tool annotated according to prescribed levels indicating the degree of wear of the tool on the basis of the arbitrary image. The tool state learning device also has a tool state learning unit for using the acquired label and input data to perform supervised learning, and generating a learned model in which a machined surface image of the machined surface of a workpiece imaged by the imaging device is inputted and the state of the tool that cut the machined surface of the workpiece is outputted.

A tool presetting and/or tool measuring apparatus device includes a carrier unit and a spindle unit, in particular motor spindle unit, which is supported by the carrier unit, which is at least configured for a rotation of an object inserted in the spindle unit, in particular a tool and/or a tool holder, around a spindle axis of the spindle unit and which comprises at least one drive unit for generating the rotation movement of the object, wherein the spindle unit includes a coupling unit which is configured, in particular for the purpose of changing a maximally achievable spindle torque, to drive the spindle unit in at least two differing modes.

The wear status of a micro-endmill tool may be inferred by monitoring the chip production rate of the tool in operation. Chips may be extracted from a work area, captured on an adhesive surface, imaged, and counted to determine the chip production rate. When the rate of chip production falls, the feed rate of the micro-endmill may be increased to a level suitable for the current state of tool wear. In this manner, costly and inconvenient work stoppages to evaluate the wear status of a tool are eliminated.

A measurement device for a chip removing machine, and methods of obtaining frequency response functions, obtaining stability charts and selecting operational parameters of a chip removing machining tool are disclosed. The device includes an engagement portion at the rear end for engagement with the machine and a measurement portion at the front end. The measurement portion is without a tool tip and includes a planar front end surface perpendicular to the centre axis. The front end surface has a coupling point aligned with the centre axis for receiving mechanical excitation. The front end surface further includes a plurality of seats for receiving one accelerometer each for measuring a response of the received mechanical excitation. When an accelerometer is received in one of the seats, and abutts against three contact surfaces thereof, it is positioned and oriented three dimensionally and around three axes of rotation in relation to the coupling point.

The present invention provides a cross-axis and cross-point modal testing and parameter identification method for predicting the cutting stability, which is used to improve the accuracy of existing prediction methods of cutting stability. The method firstly installs a miniature tri-axial acceleration sensor at the tool tip, and conducts the cross-axis and cross-point experimental modal tests respectively. The measured transfer functions are grouped according to different measuring axes, and the dynamic parameters are separately identified from each group of transfer functions. Then, the contact region between the cutter and workpiece is divided into several cutting layer differentiators. After that, together with other dynamic parameters, all the parameters are assembled into system dynamic parameter matrices matching with the dynamic model. Finally, dynamic parameter matrices including the effects of cross-axis and cross-point model couplings are obtained. Moreover, the acceleration sensor in the method only needs to be installed once.

A method, system and computer-usable medium are disclosed for monitoring and controlling a machining process of parts. Data as to dimensions of produced parts are gathered during a production process. The parts are produced based on part control plan. The data of the dimensions are plotted as to statistical information related to a distribution curve. Determination is made if a trend in the dimensional data approaches an upper control limit and a lower control limit. Corrective action is taken if the trend approaches either the upper control limit or the lower control limit.

A method, system and computer-usable medium are disclosed for monitoring and controlling a machining process of parts. Data as to dimensions of produced parts are gathered during a production process. The parts are produced based on part control plan. The data of the dimensions are plotted as to statistical information related to a distribution curve. Determination is made if a trend in the dimensional data approaches an upper control limit and a lower control limit. Corrective action is taken if the trend approaches either the upper control limit or the lower control limit.

A cutting apparatus includes a blade changing unit demounting an old cutting blade from a blade mount and mounting a new cutting blade to the blade mount. The blade changing unit includes a blade holder for holding a support base of each cutting blade and a moving portion for moving the blade holder in the axial direction of a boss portion of the blade mount in the condition where each cutting blade is held by the blade holder, thereby mounting the new cutting blade to the boss portion or demounting the old cutting blade from the boss portion. The cutting apparatus further includes a control unit controlling the blade changing unit. The control unit measures a signal indicating a force applied to the moving portion in mounting or demounting, and determines the condition of the blade changing unit and blade mount according to the signal measured by the measuring portion.