A method of monitoring a milling machine includes deploying an untrained machine learning model for determining one or more anomalies in time series data. During operation of the milling machine, first time series data representing a rotational speed of a milling head of the milling machine and at least one further operating parameter of the milling machine are obtained by the untrained machine learning model. The untrained machine learning model is trained, during operation of the milling machine, based on the obtained first time series data. Second time series data representing the rotational speed of the milling head of the milling machine and the further operating parameter are obtained by the trained machine learning model during operation of the milling machine. One or more anomalies in the second time series data are determined by the trained machine learning model during operation of the milling machine.

An abrasive tool for a floor grinder, wherein the tool extends along a wear direction (D) from a grinding surface (G) to a mounting surface (M), wherein the abrasive tool comprises at least two sections (410, 420, 430) separated by a surface (P) transversal to the wear direction (D), where the at least two sections comprise respective abrasive materials associated with different grit sizes, where the section located closer to the grinding surface (G) is associated with a larger grit size compared to the section located closer to the mounting surface (M).

The present disclosure is directed to calibrating position detection for a tool. The tool can use a sensor to detect a first value of a parameter. The tool can use a motor to extend the working member of the tool towards a working surface. The tool can include a base. The tool can detect, with the working member in contact with the working service, a second value of the parameter. The tool can determine a z-axis position of the working member relative to the working surface.

An automated container cutting system for cutting a container includes a cutting platform and a cutting tool held by the cutting platform. The cutting tool is configured to cut the container. The automated container cutting system includes a force feedback sensor operatively connected to the cutting tool such that the force feedback sensor is configured to measure resistive force exerted on the cutting tool. The automated container cutting system includes at least one processor communicatively coupled to the force feedback sensor. The processor is configured to receive resistive force data from the force feedback sensor. The resistive force data represents resistive force exerted on the cutting tool as the cutting tool pierces a wall of the container. The at least one processor is configured to determine whether the cutting tool has penetrated through the wall of the container using the received resistive force data.

A method is provided for monitoring a milling method for a milling machine provided with a milling tool comprising cutting teeth, the method including: determining measured values of a first parameter corresponding to a bending of the milling tool as a function of a second parameter corresponding to an angle of rotation of the milling tool in a rotating frame of reference of the milling tool and analyzing the measured values as a function of at least one monitoring criterion.

Disclosed herein is a duplicator assembly for forming a first void, which can be layered, in a laminated material of a part that matches a second void in a scarf repair guide. The duplicator assembly comprises an arm that comprises a first end portion and a second end portion. The first end portion is spaced apart from the second end portion. The duplication assembly also comprises a probe that is fixed to the first end portion of the arm and configured to trace the second void in the scarf repair guide. The duplication assembly further comprises a milling tool that is fixed to the second end portion of the arm such that the milling tool is co-movably coupled with the probe via the arm.

A control device for machine tools according to an aspect of the present disclosure controls a machine tool which cuts the workpiece by way of the cutting tool by causing the spindle and the feed axis to cooperatively act, the control device including: a feed command calculation unit which calculates a feed command for causing the feed axis to relatively move in relation to the workpiece; an oscillation phase calculation unit which calculates an oscillation phase that is a phase of oscillation to cause the feed axis to reciprocally move based on a rotation number of the spindle; an oscillation phase compensation unit which compensates the oscillation phase; an oscillation amplitude calculation unit which calculates an oscillation amplitude that is an amplitude of oscillation causing the feed axis to reciprocally move based on the feed command; an oscillation command calculation unit which calculates an oscillation command causing the feed axis to reciprocally move, based on the oscillation amplitude and the oscillation phase after compensation by the oscillation phase compensation unit; and a command synthesizing unit which calculates a synthetic command for driving the feed axis by superimposing the feed command and the oscillation command.

This control system takes into account the thermomechanical aspects of materials to quickly and easily determine optimal cutting conditions and to automatically control machining to preserve the integrity of the workpiece. This system includes an acquisition module configured to acquire values of a set of input parameters relating to cutting conditions and material properties of the piece, and a microprocessor configured for determining at least one operating cutting parameter representative of a cutting signal from the machining apparatus using a set of output parameters of an integrity model previously constructed during a learning phase. The integrity model connects the set of input parameters to the set of output parameters comprising specific cutting coefficients representative of the material integrity of the piece, and establishes at least one fatigue threshold of the at least one cutting operating parameter. The fatigue threshold allows control of the progress of cutting operations.

An automated container cutting system for cutting a container includes a cutting platform and a cutting tool held by the cutting platform. The cutting tool is configured to cut the container. The automated container cutting system includes a force feedback sensor operatively connected to the cutting tool such that the force feedback sensor is configured to measure resistive force exerted on the cutting tool. The automated container cutting system includes at least one processor communicatively coupled to the force feedback sensor. The processor is configured to receive resistive force data from the force feedback sensor. The resistive force data represents resistive force exerted on the cutting tool as the cutting tool pierces a wall of the container. The at least one processor is configured to determine whether the cutting tool has penetrated through the wall of the container using the received resistive force data.

The disclosure provides a method and system for injecting cutting fluid during milling under different working conditions. By analyzing influence of an airflow field in a milling area under different working conditions on injection of cutting fluid, an influence rule of a helical angle and a rotation speed of a cutter on the flow field is quantitatively analyzed, an optimal target distance of a nozzle, an angle between the nozzle and a milling cutter feeding direction and an angle between the nozzle and the surface of a workpiece are comprehensively determined, the nozzle is set according to a determined setting manner, and lubricating oil is sprayed to the milling area by utilizing the nozzle.