When the number of machining operations using a tool attached to a machining head of a machine tool has reached a predetermined number of machining operations for replacement, without a determination unit determining that an abnormality has occurred in the tool, then the tool is replaced with a replacement tool that is held in a tool magazine. Further, the tool removed from the machining head is returned to the tool magazine and stored as an auxiliary replacement tool. Then, if the determination unit determines that an abnormality has occurred in the replacement tool, the replacement tool is replaced with the auxiliary replacement tool.

The present disclosure is directed to a system and method to identify and track parts of a semiconductor processing chamber, as well as the status of the parts, and store status information in a centralized location as status changes over time.

In an information processing device according to the present invention, a statistics estimation unit estimates a value of a state quantity by using a statistical model constructed based on values of past state quantities of a target device. A physical estimation unit estimates a value of a state quantity by using a physical model constructed based on design data of the target device. A specification unit specifies a value to be used to manage the target device from the value estimated by the statistics estimation unit and the value estimated by the physical estimation unit based on deterioration of the target device with time.

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 lifetime estimation device for a robot including a linear-motion mechanism including a guide member and at least one slider moving along the guide member includes: a load calculation unit that calculates, at predetermined time intervals, a load acting on each slider on a basis of a program for operating the robot and geometric parameters of the robot and a load mounted on the robot; a travel-distance calculation unit that calculates travel distances of the slider at the time intervals; a lifetime calculation unit that calculates a lifetime of the linear-motion mechanism on a basis of the loads calculated by the load calculation unit and the travel distances calculated by the travel-distance calculation unit; and a display unit that displays the calculated lifetime.

A numerical control device is intended for a machine tool that machines a workpiece using a multi-edge tool including a plurality of edges of different specifications, the numerical control device including: a tool information memory that stores edge type numbers in association with tool type numbers; a tool type-edge type selection command decoding unit that prefetches a plurality of blocks of a machining program, decodes a tool type selection command for selecting one of the tool types and/or an edge type selection command for selecting one of the edge types in the plurality of prefetched blocks, and generates internal information including the tool type selection command and/or the edge type selection command that have been decoded; and a tool selection unit that selects one tool with which the number of times of tool replacement is minimized during execution of at least the plurality of prefetched blocks.

A tool-life prediction method, applicable to a machine tool having a machining end, includes steps of capturing a plurality of measurement data from a tool of the machining end, transforming each of the plurality of measurement data into a corresponding complex process capability index (Cpk), utilizing an artificial neural network being trained to generate a tool-life prediction scale with respect to the Cpk, and then based on the tool-life prediction scale to determine a remaining tool life of the tool. In addition, a tool life prediction system is also provided.

A life expectancy prediction system for a target tool includes a processing machine body, a detector to detect a state data, a learned model storage unit to store learned models generated by executing machine learning using training datasets, including an explanatory variable and an objective variable, the explanatory variable being the state data and the objective variable being a number of first remaining machining times, the learned model storage unit being to store the learned models, each for each of the tools and a remaining machining times prediction unit to select, based on the state data, one learned model and predict a number of second remaining machining times, using the one learned model and the state data.

A method is provided for assessing wear of a drill bit throughout its use for carrying out the drilling of elements to be drilled constituted by at least one layer and at least one material. The wear of the drill bit expresses its capacity to perform a drilling that meets at least one criterion of quality of a drilling. The method includes at least: measuring or detecting at least one parameter having an effect on the wear of the drill bit, the parameter being chosen from: the depth of drilling performed by the drill bit, and the entry of the drill bit into the element to be drilled; and determining at least one state of wear of the drill bit, each state of wear being determined as a function of one of the parameters and being characteristic of one of the criteria of quality of a drilling.

An operational control method is provided for a machine tool that includes a plurality of tool holders with a cutting-tool. The machine tool relatively moves the tool holders with respect to a workpiece to process the workpiece. The operational control method includes determining whether an abnormality has occurred in the cutting-tool during a processing or not, determining whether a simultaneous processing in which at least two or more of the tool holders perform the processings to an identical workpiece is being performed or not and whether the cutting-tool having the detected abnormality is the cutting-tool that is performing the simultaneous processing or not when the abnormality is detected, and causing the tool holder with the cutting-tool having the detected abnormality to take an abnormality avoidance operation and another of the tool holders to take a response operation based on a determination result at the determining of the simultaneous processing.