A machining accuracy diagnosing device includes a diagnosing information acquiring, an opening time setting unit, a cutting time setting unit, and a machining accuracy influence amount predicting unit. The diagnosing information acquiring unit acquires at least one of a room temperature inside the plant, a set temperature of a temperature regulating device, an air temperature outside the plant, a wind speed outside the plant, an opening degree of the door or the shutter, and a machine body temperature. The opening time setting unit sets an opening time of the door or the shutter. The machining accuracy influence amount predicting unit predicts an influence amount of opening of the door or the shutter on the machining accuracy based on the acquired information for diagnosis, the set opening time of the door or the shutter, and a set scheduled machining start time and a set scheduled machining end time.

Provided is a thermal displacement correction method using a machine learning method but making it possible to, on a user side, calculate a thermal displacement amount appropriate to a machine tool of the user and correct the thermal displacement. In a machine tool on a target user side, a thermal displacement amount between workpiece and tool corresponding to a temperature at a preset measurement point is calculated based on a parameter defining a relation between the temperature and the thermal displacement amount, and a positioning position for workpiece and tool is corrected in accordance with the calculated thermal displacement amount. On a manufacturer side, operational status information of the machine tool on the target user side is obtained, an operational status identical to the obtained operational status on the target user side is reproduced with a machine tool of a same type as the machine tool on the target user side based on the obtained operational status information, a temperature at a measurement point identical to the measurement point on the machine tool on the target user side and a thermal displacement amount between workpiece and tool are measured during reproduction, and the parameter is calculated by machine learning based on the measured temperature and thermal displacement amount. The parameter in the machine tool on the target user side is updated with the calculated parameter.

Provided is a thermal displacement correction method using a machine learning method but making it possible to, on a user side, calculate a thermal displacement amount appropriate to a machine tool of the user and correct the thermal displacement. In a machine tool on a target user side, a thermal displacement amount between workpiece and tool corresponding to a temperature at a preset measurement point is calculated based on a parameter defining a relation between the temperature and the thermal displacement amount, and a positioning position for workpiece and tool is corrected in accordance with the calculated thermal displacement amount. On a manufacturer side, operational status information of the machine tool on the target user side is obtained, an operational status identical to the obtained operational status on the target user side is reproduced with a machine tool of a same type as the machine tool on the target user side based on the obtained operational status information, a temperature at a measurement point identical to the measurement point on the machine tool on the target user side and a thermal displacement amount between workpiece and tool are measured during reproduction, and the parameter is calculated by machine learning based on the measured temperature and thermal displacement amount. The parameter in the machine tool on the target user side is updated with the calculated parameter.

A machining center processes a section not to be corrected of a workpiece and a crankshaft bearing hole in a different position from the section not to be corrected and which is separated from an upper deck surface of the workpiece. The machining center supplies a coolant to the hole during processing and detects a temperature of the coolant. A control apparatus estimates the temperature of the coolant when a predetermined time elapses from a start of the processing to be a temperature of the workpiece, calculates a deformation amount of the workpiece due to thermal expansion, corrects a position of the hole with respect to the upper deck surface based on the deformation amount, and starts processing of the hole after the predetermined time. The predetermined time ends when a difference between a temperature near the hole and the temperature of the coolant falls within a predetermined range.

A machining center processes a section not to be corrected of a workpiece and a crankshaft bearing hole in a different position from the section not to be corrected and which is separated from an upper deck surface of the workpiece. The machining center supplies a coolant to the hole during processing and detects a temperature of the coolant. A control apparatus estimates the temperature of the coolant when a predetermined time elapses from a start of the processing to be a temperature of the workpiece, calculates a deformation amount of the workpiece due to thermal expansion, corrects a position of the hole with respect to the upper deck surface based on the deformation amount, and starts processing of the hole after the predetermined time. The predetermined time ends when a difference between a temperature near the hole and the temperature of the coolant falls within a predetermined range.

A thermal displacement compensation system detects a state quantity indicating a state of a machine, infers a thermal displacement compensation amount of the machine from the detected state quantity, and performs a thermal displacement compensation of the machine based on the inferred thermal displacement compensation amount of the machine. The thermal displacement compensation system generates a learning model by machine learning that uses a feature quantity, and stores the generated learning model in association with a combination of specified conditions of individual difference of the machine.

A thermal displacement compensation system detects a state quantity indicating a state of a machine, infers a thermal displacement compensation amount of the machine from the detected state quantity, and performs a thermal displacement compensation of the machine based on the inferred thermal displacement compensation amount of the machine. The thermal displacement compensation system generates a learning model by machine learning that uses a feature quantity, and stores the generated learning model in association with a combination of specified conditions of individual difference of the machine.

A thermal displacement correction system performs thermal displacement correction in cooperation with a thermal displacement correction device and a computer connected via a network. The thermal displacement correction system that corrects thermal displacement caused by processing performed by a machine comprises: the thermal displacement correction device connected to the machine; and the computer connected to the thermal displacement correction device via the network. The computer comprises: a data acquisition unit that acquires environmental data on an external environment of the machine via the network; a correction value inference unit that calculates a correction value using the environmental data; and a correction value output unit that outputs the correction value to the network. The thermal displacement correction device comprises: a correction value acquisition unit that acquires the correction value via the network; and a correction execution unit that performs thermal displacement correction using the correction value.

A thermal displacement correction system performs thermal displacement correction in cooperation with a thermal displacement correction device and a computer connected via a network. The thermal displacement correction system that corrects thermal displacement caused by processing performed by a machine comprises: the thermal displacement correction device connected to the machine; and the computer connected to the thermal displacement correction device via the network. The computer comprises: a data acquisition unit that acquires environmental data on an external environment of the machine via the network; a correction value inference unit that calculates a correction value using the environmental data; and a correction value output unit that outputs the correction value to the network. The thermal displacement correction device comprises: a correction value acquisition unit that acquires the correction value via the network; and a correction execution unit that performs thermal displacement correction using the correction value.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using subtractive manufacturing systems and techniques and a determined stock allowance include, in one aspect, a method including: obtaining a finishing toolpath specification for three dimensional (3D) geometry of a part; generating 3D geometry of a model of a semi-finished structure in accordance with a computer simulation of deflections experienced by a workpiece as stock material is cut from the workpiece using the finishing toolpath specification; creating a semi-finishing toolpath specification for the semi-finished structure; and providing the semi-finishing toolpath specification for use in machining the part by cutting away a first portion of the stock material using the semi-finishing toolpath specification to form the semi-finished structure, followed by performing a finishing operation of the semi-finished structure by cutting away a second portion of the stock material to form the part.