Abstract: The step for performing machine learning includes acquiring shape data; acquiring geometric information for each of a plurality of machining faces; acquiring a tool path pattern selected for the machining faces from among a plurality of tool path patterns; and performing machine learning by using the geometric data for known workpieces and the tool path patterns wherein the input is the geometric information for the machining faces and the output is the tool path pattern for the machining faces. The step for generating a new tool path includes: acquiring shape data for the workpiece; acquiring geometric information for each of the plurality of machining faces of the workpiece to be machined; and generating a tool path pattern for each of the plurality of machining faces on the workpiece on the basis of the results of the machine learning using the geometric information of the workpiece to be machined.

Abstract: A method is provided with a step of converting shape data (51) into a plurality of voxels (55) for each of a plurality of known objects being processed, a step of setting, for each of the known objects being processed, a processing condition for a voxel (55) constituting a processing surface, a step of using the voxel (55) of the plurality of known objects being processed and the processing condition to perform machine learning in which an input is the voxel (55) and an output is a processing condition, a step of converting shape data (52) of a candidate object being processed into a plurality of voxels (56), a step of setting, on the basis of results of machine learning, a processing condition for a voxel (56) constituting a processing surface of the candidate object being processed, and a step of determining a processing condition for each processing surface of the candidate object being processed, a processing condition that is set for the largest number of voxels (56) in one processing surface being deter

Abstract: The step for performing machine learning includes acquiring shape data; acquiring geometric information for each of a plurality of machining faces; acquiring a tool path pattern selected for the machining faces from among a plurality of tool path patterns; and performing machine learning by using the geometric data for known workpieces and the tool path patterns wherein the input is the geometric information for the machining faces and the output is the tool path pattern for the machining faces. The step for generating a new tool path includes: acquiring shape data for the workpiece; acquiring geometric information for each of the plurality of machining faces of the workpiece to be machined; and generating a tool path pattern for each of the plurality of machining faces on the workpiece on the basis of the results of the machine learning using the geometric information of the workpiece to be machined.

Abstract: A fungal bed cultivation method of a hon-shimeji mushroom, in which the sprouting step and/or fruit body growing step is carried out under an environmental condition of high CO2 concentration, is provided. Examples of the environmental condition of high CO2 concentration include a CO2 concentration of 2,500 ppm or more in the sprouting step and a CO2 concentration of 5,000 ppm or more in the fruit body growing step. Since the formation ratio of a budlet in the fungal bed cultivation of a hon-shimeji mushroom is improved by embodiments of the present invention, stable production of a hon-shimeji mushroom by its large scale commercial cultivation becomes possible.