Performance Grinding Solutions

Abstract

The present application relates to systems and methods for obtaining real-time abrasion data. An example computer-implemented method could include receiving, at a computing device, sensor data from one or more sensors. The one or more sensors are disposed in proximity to an abrasive product or a workpiece associated with the abrasive product. The one or more sensors are configured to collect abrasion operational data associated with an abrasive operation involving the abrasive product or the workpiece. The computer-implemented method could further include training, based on the sensor data, a machine learning system to determine product specific information of the abrasive product and/or workpiece specific information. The computer-implemented method could also include providing the trained machine learning system using the computing device.

We claim: 
     
         1 . A computer-implemented method, comprising:
 receiving, at a computing device, sensor data from one or more sensors, wherein the one or more sensors are disposed in proximity to an abrasive product or a workpiece associated with the abrasive product, wherein the one or more sensors are configured to collect abrasion operational data associated with an abrasive operation involving the abrasive product or the workpiece;   training, by way of the computing device and based on the sensor data, a machine learning system to determine product specific information of the abrasive product or workpiece specific information of the workpiece; and   providing, by way of the computing device, the trained machine learning system.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising tagging at least a portion of the sensor data to provide tagged sensor data, wherein the tagged sensor data comprises one or more tags, each tag identifying different product specific information for the abrasive product. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the one or more tags identify a pattern of the abrasion operation data from a duration of time prior to an abrasive product related event. 
     
     
         4 . The computer-implemented method of  claim 3 , wherein the pattern of the abrasion operation data comprises one or more stages, each stage associated with one or more sensor threshold values, wherein the one or more tags are associated with a stage based on the one or more sensor threshold values. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein training the machine learning system comprises training one or more machine learning models, wherein each model is trained with sensor data from abrasive products having unique identifiers from a shared identifier set. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the sensor data from one or more sensors is aggregated by a local computing device to provide aggregated sensor data, and wherein receiving the sensor data comprises receiving the aggregated sensor data from the local computing device. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the one or more sensors are disposed in a wearable device, wherein the sensor data from one or more sensors is aggregated by the wearable device to provide aggregated sensor data, and wherein receiving the sensor data comprises receiving the aggregated sensor data from the wearable device. 
     
     
         8 . The computer-implemented method of  claim 7 , wherein the sensor data comprises information indicative of a rotation per minute (RPM) value of the abrasive product. 
     
     
         9 . A computer-implemented method, comprising:
 receiving, at a computing device, sensor data from one or more sensors, wherein the one or more sensors are disposed in proximity to an abrasive product or a workpiece associated with the abrasive product, wherein the one or more sensors are configured to collect abrasion operational data associated with an abrasive operation involving the abrasive product or the workpiece, and wherein the computing device has access to a trained machine learning system configured to receive input sensor data and output product specific information of the abrasive product or workpiece specific information of the workpiece;   determining, by applying the trained machine learning system on the sensor data, product specific information of the abrasive product or workpiece specific information of the workpiece; and   providing, to one or more client devices, the product specific information or the workpiece specific information.   
     
     
         10 . The computer-implemented method of  claim 9 , wherein the one or more sensors are disposed in a wearable device, wherein the sensor data from one or more sensors is aggregated by the wearable device to provide aggregated sensor data, and wherein receiving the sensor data comprises receiving the aggregated sensor data from the wearable device. 
     
     
         11 . The computer-implemented method of  claim 10 , wherein the sensor data comprises information indicative of a rotation per minute (RPM) value of the abrasive product. 
     
     
         12 . The computer-implemented method of  claim 9 , wherein the one or more client devices comprises at least one of: a wearable device, a mobile device, a dashboard device, a web server, an analytics processing engine, or a third party server. 
     
     
         13 . The computer-implemented method of  claim 9 , wherein providing the product specific information comprises providing information associated with a new abrasive product or an updated abrasive product, wherein the information includes, at least in part, instructions for constructing the new abrasive product or the updated abrasive product. 
     
     
         14 . The computer-implemented method of  claim 9 , wherein providing the product specific information or the workpiece specific information comprises providing, to the one or more client devices, a notification. 
     
     
         15 . The computer-implemented method of  claim 9 , wherein providing the product specific information or the workpiece specific information comprises providing, to the one or more client devices, one or more product specific solutions for resolving issues with the abrasive product. 
     
     
         16 . The computer-implemented method of  claim 15 , wherein the one or more client devices are configured to:
 display, on a graphical user interface, the one or more product specific solutions,   receive, via the graphical user interface, a selection of one of the one or more product specific solutions;   determine training data for the trained machine learning system based on the selected product specific solution; and   transmit, to the computing device, the training data.   
     
     
         17 . The computer-implemented method of  claim 9 , wherein the abrasive product is a handheld abrasive product operated by a user. 
     
     
         18 . The computer-implemented method of  claim 17 , wherein the abrasive operational data comprises at least one of: vibration data associated with the handheld abrasive product or acceleration data associated with the handheld abrasive product. 
     
     
         19 . The computer-implemented method of  claim 17 , wherein providing the product specific information or the workpiece specific information comprises providing a notification to a graphical interface of a wearable device worn by the user. 
     
     
         20 . The computer-implemented method of  claim 17 , wherein the product specific information comprises at least one of: time spent performing tasks assigned to the user, idle time of the user, or productive time of the user. 
     
     
         21 . The computer-implemented method of  claim 17 , wherein the product specific information comprises at least one of: a working angle of the user with respect to the handheld abrasive product, a working angle of the handheld abrasive product with respect to the workpiece, a grip tightness of the user on the handheld abrasive product, or an applied pressure of the user on the handheld abrasive product. 
     
     
         22 . The computer-implemented method of  claim 17 , wherein the product specific information comprises an end of life estimate of the handheld abrasive product, wherein the end of life estimate comprises an estimated amount of time the user can safely use the handheld abrasive product. 
     
     
         23 . A computer-implemented method of  claim 9 , wherein the abrasive product is an automated abrasive product operated by a controller. 
     
     
         24 . The computer-implemented method of  claim 23 , wherein the one or more sensors comprises a spark-invariant sensor configured to collect operational speeds of the automated abrasive product. 
     
     
         25 . The computer-implemented method of  claim 23 , wherein providing the product specific information comprises providing a determination that one or more abrasive articles of the automated abrasive product are damaged or malfunctioning. 
     
     
         26 . The computer-implemented method of  claim 25 , wherein upon providing the determination, the computing device is further configured to:
 identify, by a product database, one or more replacement abrasive articles for the automated abrasive product; and   responsive to identifying the one or more replacement abrasive articles, place a request for the one or more replacement articles or a refurbishment treatment.   
     
     
         27 . The computer-implemented method of  claim 23 , wherein providing the product specific information comprises transmitting at least one control instruction to the controller of the automated abrasive product, wherein the at least one control instruction comprises at least one of: adjust a rotational speed of the automated abrasive product, provide a notification to the automated abrasive product, turn on the automated abrasive product, or turn off the automated abrasive product. 
     
     
         28 . The computer-implemented method of  claim 9 , wherein determining the product specific information of the abrasive product or workpiece specific information of the workpiece comprises determining one or more of: a predicted future condition of the abrasive product or a predicted future condition of the workpiece. 
     
     
         29 . The computer-implemented method of  claim 28 , wherein providing the product specific information or the workpiece specific information comprises providing at least one of the predicted future condition of the abrasive product or the predicted future condition of the workpiece. 
     
     
         30 . The computer-implemented method of  claim 9 , wherein determining the product specific information of the abrasive product or workpiece specific information of the workpiece comprises determining a prescriptive action. 
     
     
         31 . The computer-implemented method of  claim 30 , wherein providing the product specific information or the workpiece specific information comprises providing the prescriptive action, wherein the prescriptive action comprises at least one of: adjusting an operational parameter of an abrasive machine, performing a maintenance operation, redressing the abrasive product, or replacing the abrasive product. 
     
     
         32 . A computing system, comprising:
 a trained machine learning system configured to receive input sensor data and output, based on the input sensor data, product specific information or workpiece specific information; and   a computing device configured to:
 receive sensor data from one or more sensors, wherein the one or more sensors are disposed in proximity to an abrasive product or a workpiece associated with the abrasive product, wherein the one or more sensors are configured to collect abrasion operational data associated with an abrasive operation involving the abrasive product or the workpiece; 
 determine, by applying the trained machine learning system on the sensor data, product specific information of the abrasive product or workpiece specific information of the workpiece; and 
 provide, to one or more client devices, the product specific information or the workpiece specific information. 
   
     
     
         33 . The computing system of  claim 32 , further comprising:
 a display configured to provide a graphical user interface with a search interface, wherein the search interface comprises a plurality of user-selectable criteria, wherein the user-selectable criteria comprise at least one of: a location menu, a device menu, a date range, or a workpiece menu, wherein the graphical user interface is configured to:   receive user-selected search criteria from the user-selectable criteria;   determine, based on the user-selected search criteria, one or more metrics; and   display, via the graphical user interface, the one or more metrics.   
     
     
         34 . The computing system of  claim 33 , wherein the one or more metrics comprise at least one of: a grinding time metric, an optimal grinding metric, a vibration metric, a depth of cut, a current trace, a tool identifier, or a part count. 
     
     
         35 . The computing system of  claim 33 , wherein the graphical user interface is further configured to receive a desired metric selected from a metric menu, wherein displaying the one or more metrics is based on the desired metric. 
     
     
         36 . The computing system of  claim 33 , wherein the display is further configured to provide, via the graphical user interface, a cycle comparison interface, wherein the cycle comparison interface is configured to display at least a portion of the sensor data in an overlapping arrangement of a plurality of periodic time series. 
     
     
         37 . The computing system of  claim 32 , wherein the computing device is further configured to:
 compare a plurality of periodic time series of at least a portion of the sensor data, wherein determining the product specific information of the abrasive product or workpiece specific information of the workpiece, is based, at least in part, on the comparison.