A monitoring method and a monitoring system for a machine tool to machine a workpiece are provided. The monitoring method includes the following steps. First, a vibration signal of a spindle of the machine tool is detected. Next, a vibration feature value of the vibration signal is obtained. Whether the vibration feature value exceeds a threshold condition is determined, wherein the threshold condition is determined by a training model based on a predetermined surface quality of the workpiece. When the vibration feature value exceeds the threshold condition, a machining parameter of the machine tool is adjusted.

Techniques are described for receiving manufacturing data and events over real time and non-real time interfaces and associating the data with one another. In one example, real time data is received, the real time data associated with a counter value assigned by a precision counter. The received real time data is stored in a storage buffer, and non-real time data is received, where the non-real time data associated with a counter value assigned by the precision counter. In response to receiving the non-real time data, the buffer is searched for real time data having a matching counter value and, in response to identifying stored real time data associated with a matching counter value, the non-real time data is associated with the real time data based on the match. Data packages are generated including related real time and non-real time data associated with matching counter values.

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 determined as a processing condition for said processing surface.

The invention described herein generally pertains to a system and method for collecting one or more welding parameters in real time during creation of one or more welds using a welding sequence. The one or more welding parameters can be associated with a particular welding sequence. Moreover, based on the one or more welding parameters collected, a modeled welding parameter can be generated to increase quality, efficiency, and the like. A collection component collects real time welding parameter data from which a quality manager component creates a modeled welding parameter. The modeled welding parameter can be employed for the welding sequence to monitor or track the welding parameter during a subsequent weld.

The invention described herein generally pertains to a system and method for collecting one or more welding parameters in real time during creation of one or more welds using a welding sequence. The one or more welding parameters can be associated with a particular welding sequence. Moreover, based on the one or more welding parameters collected, a modeled welding parameter can be generated to increase quality, efficiency, and the like. A collection component collects real time welding parameter data from which a quality manager component creates a modeled welding parameter. The modeled welding parameter can be employed for the welding sequence to monitor or track the welding parameter during a subsequent weld.

Techniques are described for viewing a plurality of related presentation areas linked to a set of machine-related data. In one example, a machine-related data set associated with a manufacturing process session for manufacturing a particular workpiece is presented, the data set representing a common data set presented in a plurality of presentation areas, each associated with a separate view on the machine-related data set. In one of the presentation areas, a selection of a particular group of data points is identified and that presentation area is updated. Reference values associated with the common data set are identified based on the selected group of data points. For each of the other presentation areas, (1) a particular set of data included in the particular other presentation area corresponding to the identified reference values is identified and (2) the corresponding presentation area is updated based on that identified data set.

Techniques are described for receiving manufacturing data and events over real time and non-real time interfaces and associating the data with one another. In one example, real time data is received, the real time data associated with a counter value assigned by a precision counter. The received real time data is stored in a storage buffer, and non-real time data is received, where the non-real time data associated with a counter value assigned by the precision counter. In response to receiving the non-real time data, the buffer is searched for real time data having a matching counter value and, in response to identifying stored real time data associated with a matching counter value, the non-real time data is associated with the real time data based on the match. Data packages are generated including related real time and non-real time data associated with matching counter values.

Techniques are described for receiving manufacturing data and events over real time and non-real time interfaces and associating the data with one another. In one example, real time data is received, the real time data associated with a counter value assigned by a precision counter. The received real time data is stored in a storage buffer, and non-real time data is received, where the non-real time data associated with a counter value assigned by the precision counter. In response to receiving the non-real time data, the buffer is searched for real time data having a matching counter value and, in response to identifying stored real time data associated with a matching counter value, the non-real time data is associated with the real time data based on the match. Data packages are generated including related real time and non-real time data associated with matching counter values.

Techniques for comparing two or more sessions of a manufacturing process are described. In one example, a particular metric associated with execution of a manufacturing process is identified, the particular metric evaluated in a plurality of manufacturing process sessions for at least one manufacturing process. A particular session particular session from the plurality of manufacturing process sessions is selected as a baseline session, wherein at least a portion of the remaining plurality of manufacturing process sessions are to be compared to the baseline session. In a primary portion of a presentation area, a visualization of the values of the identified metric associated with the particular session are presented. In a secondary portion of the presentation area, visualizations of the values of the identified metric associated with at least a portion of the other manufacturing process sessions from the plurality of sessions is presented.

Techniques are described for receiving visualizing two-dimensional (2D) metric data in connection with a three-dimensional (3D) visualization data of a manufacturing process. In one example, a 3D visualization of machine-related data from a process session for manufacturing a particular workpiece is presented, the manufacturing process performed by a machine operating in 3D, the machine-related data associated with a path taken by a tool or end-effector associated with the machine and/or the machine itself during the manufacturing process session. At least one 2D data set is provided representing a metric associated with manufacturing process session. A particular set is selected for presentation within the presented 3D visualization of the machine-related data. A connection between values of the 2D metric set and the machine-related data of the 3D visualization is determined and, based on the connection, the selected 2D metric set is incorporated into the 3D visualization of the machine-related data.