Weld spot analytics

Abstract

An adaptive welding system including a plurality of welding machines, an edge computing system configured to gather weld data from a first welding machine from the plurality of welding machines, and a weld analytics system configured to build an optimized operational model for the first welding machine based at least upon the weld data. The weld analytics system is further configured to transmit the optimized operational model to the first welding machine as a firmware image.

What is claimed is: 
     
         1 . An adaptive welding system comprising:
 a first welding machine;   an edge computing system configured to gather weld data from the first welding machine; and   a weld analytics system configured to build an optimized operational model for the first welding machine based at least upon the weld data,   wherein the weld analytics system is further configured to transmit the optimized operational model to the first welding machine as a firmware image.   
     
     
         2 . The adaptive welding system of  claim 1 , wherein the edge computing system is configured to produce a process stability factor (PSF) value based on the weld data, the PSF value indicating a quality of a welding process that produces a particular weld. 
     
     
         3 . The adaptive welding system of  claim 1 , wherein the edge computing system is configured to produce a weld energy delivered (WEDF) value based on the weld data, the WEDF value indicating a weld quality based on at least one of weld strength, current delivered to create the weld, weld shape, or weld depth. 
     
     
         4 . The adaptive welding system of  claim 1 , wherein building the optimized operational model includes building the optimized operational model based on the WEDF value and the PSF value. 
     
     
         5 . The adaptive welding system of  claim 1 , further comprising a user terminal, wherein the user terminal is configured to
 accept user input; and,   adjust, by machine learning software, the operational model based at least upon user input.   
     
     
         6 . The adaptive welding system of  claim 1 , wherein the transmitting of the firmware image to the first welding machine includes transmitting the firmware image to an edge computing system for to the first welding machine. 
     
     
         7 . The adaptive welding system of  claim 1 ,
 wherein building the optimized operational model further comprises analyzing weld data from a second welding machine, and,   
       wherein the first welding machine is positioned in a first welding plant, and the second welding machine is positioned in a second welding plant remote from the first welding plant. 
     
     
         8 . An adaptive welding system comprising:
 a first welding machine;   a weld analytics system configured to receive weld data from the first welding machine and to optimize an operational model for the first welding machine based at least upon weld data received from other welding machines via a cloud computing service,   wherein the weld analytics system is further configured to transmit the optimized operational model to the first welding machine as a firmware image.   
     
     
         9 . The adaptive welding system of  claim 8 , further including an edge computing system configured to gather the weld data from the first welding machine,
 wherein the edge computing system is configured to produce a process stability factor (PSF) value based on the weld data, the PSF value indicating a quality of a welding process that produces a particular weld.   
     
     
         10 . The adaptive welding system of  claim 8 , further including an edge computing system configured to gather the weld data from the first welding machine,
 wherein the edge computing system is configured to produce a weld energy delivered (WEDF) value based on the weld data, the WEDF value indicating a weld quality based on at least one of weld strength, current delivered to create the weld, weld shape, or weld depth.   
     
     
         11 . The adaptive welding system of  claim 8 , wherein building the optimized operational model includes building the optimized operational model based on the WEDF value and the PSF value. 
     
     
         12 . The adaptive welding system of  claim 8 , further comprising a user terminal, wherein the user terminal is configured to accept user input; and,
 adjust, by machine learning software, the operational model based at least upon user input.   
     
     
         13 . The adaptive welding system of  claim 8 , wherein the transmitting of the firmware image to the first welding machine includes transmitting the firmware image to an edge computing system for to the first welding machine. 
     
     
         14 . The adaptive welding system of  claim 8 ,
 wherein building the optimized operational model further comprises analyzing weld data from a second welding machine, and,   wherein the first welding machine is positioned in a first welding plant, and the second welding machine is positioned in a second welding plant remote from the first welding plant.   
     
     
         15 . A method of tracking weld quality for a group of sequential welds comprising:
 receiving, via a weld analytics system, weld data from a first welding machine,   building, via the weld analytics system, an optimized operational model for the first welding machine based at least upon the weld data, and   transmitting, via the weld analytics system, the optimized operational model to the first welding machine as a firmware image.   
     
     
         16 . The method of  claim 15 , further comprising
 gathering, via an edge computing system, the weld data from the first welding machine; and,   producing, via the edge computing system, a process stability factor (PSF) value based on the weld data, the PSF value indicating a quality of a welding process that produces a particular weld.   
     
     
         17 . The method of  claim 15 , further comprising
 gathering, via an edge computing system, the weld data from the first welding machine; and,   producing, via the edge computing system, a weld energy delivered (WEDF) value based on the weld data, the WEDF value indicating a weld quality based on at least one of weld strength, current delivered to create the weld, weld shape, or weld depth.   
     
     
         18 . The method of  claim 15 , wherein building the optimized operational model includes building the optimized operational model based on the WEDF value and the PSF value. 
     
     
         19 . The method of  claim 15 , further comprising
 accepting, via a user terminal, user input; and,   adjusting, via machine learning software, the operational model based at least upon the user input.   
     
     
         20 . The method of  claim 15 , wherein the transmitting of the firmware image to the first welding machine includes transmitting the firmware image to an edge computing system for to the first welding machine.