Prior to the rolling of a flat rolling material (2) on a rolling line that includes a number of roll stands (1), a control system (3) receives actual variables (I) and target variables (Z) of the material (2). The control system (3) determines desired values (S*) for the roll stands (1), based on the actual (I) and target variables (Z) in combination with a model (10) of the rolling line, such that expected variables (E1) of the material (2) after its rolling are aligned as far as possible with the target variables (Z) and transfers the desired values (S*) to the roll stands (1) such that the material (2) is rolled according to the desired values (S*). The target variables (Z) comprise at least one freely selectable, discrete characteristic variable (K1 to K5, K2′ to K4′, K2″ to K4″) defining the contour (K) of the flat rolling material (2).

A parameter indicating a surface property of an object surface is plotted on the horizontal axis, a normal direction change rate of the shape of the object surface is plotted on the vertical axis, the minimum normal direction change rate visible to a person is associated with the parameter indicating the surface property of the object surface to create a visible area map, the relationship between the parameter indicating the surface property of a machining surface of a workpiece, and the maximum value of the normal direction change rate of the shape of the machining surface of the workpiece is displayed on the visible area map, and the object surface is evaluated.

An estimation apparatus includes: an acquisition section that acquires a measurement result of a measurement unit that measures an object to be an estimation target of a contact sense in a contactless manner; a determination section that makes a determination as to an aspect of the object or a measurement condition of the object on a basis of the measurement result of the measurement unit; a selection section that selects, on a basis of a result of the determination, an estimation scheme to be used for estimation of the contact sense of the object from among a plurality of estimation schemes; and an estimation section that estimates the contact sense of the object using the selected estimation scheme.

The invention relates to a method for operating a machine, in particular a grinding machine, comprising the steps of: detecting a workpiece to be machined and setting various setting values of the machine, performing machining of the workpiece, detecting a first actual value and a second actual value during or after performance of the machining, wherein the first actual value is assigned a higher prioritization than the second actual value, comparing the first actual value with a first set value range and the second actual value with a second set value range, and changing the setting values of the machine such that the actual values meet the assigned target value range according to the prioritization.

This calculation device for predicting the surface roughness of a processed product from a physical quantity includes: a measurement data acquisition unit which acquires measurement data of surface roughness measured by a surface roughness measuring device; a physical quantity acquisition unit which acquires a physical quantity indicating a factor that causes surface roughness; a first amplitude spectrum conversion unit which converts the measurement data into a first amplitude spectrum; a second amplitude spectrum conversion unit which converts the physical quantity into a second amplitude spectrum; and a coefficient calculation unit which calculates a coefficient on the basis of a specific frequency, the second amplitude spectrum, and the first amplitude spectrum.

A surface evaluation system that includes one or more vision systems that generate target surface data during evaluation of a surface, the one or more vision systems comprising two or more of: at least one light, a camera, a structured light camera, a laser scanner and a 3D scanner.

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

A rolling line for rolling a flat rolling material (2) includes a number of roll stands (1). Prior to the rolling, a control system (3) receives actual variables (I) of the material (2) before the rolling and target variables (Z) after the rolling. The control system (3) determines desired control variables (S*) for the roll stands (1), based on the actual (I) and target variables (Z), in combination with a description (B) of the rolling line, using a model (10) of the rolling line. The control system (3) determines the desired values (S*) such that expected variables (E1) for the material (2) after its rolling are aligned as far as possible with the target variables (Z). The control system (3) transfers the desired values (S*) to the roll stands (1) such that the material (2) is rolled according to the transferred desired values (S*). The target variables (Z) comprise at least one freely selectable, discrete characteristic variable (K1 to K5, K2′ to K4′, K2″ to K4″) defining the contour (K) of the flat rolling material (2).

In the present invention: a plurality of meshes are defined on an object surface; the luminance of the object surface when the object surface is viewed from a viewpoint position is calculated for each of the plurality of meshes on the basis of data concerning a processed shape, a surface roughness curve, the viewpoint position, the direction, angle distribution, and intensity of incident light onto the object surface, and a reflectance and scattering characteristics for each wavelength on the object surface; the shape of the object surface is displayed on the basis of the luminances; and data concerning at least the object shape or the surface roughness curve is corrected so as to obtain a desired appearance of the object surface.