A weld production knowledge system for processing welding data collected from one of a plurality of welding systems, the weld production knowledge system comprising a communication interface communicatively coupled with a plurality of welding systems situated at one or more physical locations. The communication interface may be configured to receive, from one of said plurality of welding systems, welding data associated with a weld. The weld production knowledge system may comprise an analytics computing platform operatively coupled with the communication interface and a weld data store. The weld data store employs a dataset comprising (1) welding process data associated with said one or more physical locations, and/or (2) weld quality data associated with said one or more physical locations. The analytics computing platform may employ a weld production knowledge machine learning algorithm to analyze the welding data vis-à-vis the weld data store to identify a defect in said weld.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A welding system comprising: a first processing circuitry to process a first welding input from a first data source to define first welding data, wherein the first data source is associated with a weld, weldment, or weld process; a second processing circuitry to process a second welding input from a second data source to define second welding data, wherein the second data source is associated with the weld, weldment, or weld process; and a communication network communicatively coupled with the first processing circuitry, the second first processing circuitry, and a remotely situated analytics computing platform, wherein the communication network communicates the first welding data and the second welding data to said remotely situated analytics computing platform via a communication network, wherein the remotely situated analytics computing platform is facilitated via a cloud computing framework that employs a distributed and scalable file system running on commodity cluster hardware, wherein the remotely situated analytics computing platform associates the first welding data with the second welding data based at least in part on tag data to define a welding data set, wherein the remotely situated analytics computing platform generates or updates a large scale dataset based at least in part on said welding data set, the large scale dataset comprising weld operational, production and productivity data, weld quality data, weldment quality data, welding process data, and welding machine parameters data collected from a plurality of heterogeneous data sources, and wherein the remotely situated analytics computing platform employs a production knowledge machine learning algorithm to analyze the welding data set vis-à-vis the large scale dataset to predict a characteristic of the weld, weldment, or weld process. 2. The welding system of claim 1, wherein the first data source and the second data source each include: a sensor; a non-transitory data storage device; an operator interface; a database inside or outside welding equipment; or a combination thereof. 3. The welding system of claim 2, wherein the first data source is associated with a first physical location and the second data source is associated with a second physical location that is different from said first physical location. 4. The welding system of claim 2, wherein the first data source and the second data source are associated with the same physical location. 5. The welding system of claim 2, wherein the first data source and the second data source are heterogeneous data sources. 6. The welding system of claim 1, wherein the remotely situated analytics computing platform cleans or formats the first welding data and the second welding data into a standardized or structured form. 7. The welding system of claim 1, wherein the production knowledge machine learning algorithm is agnostic to welding operation type, weldment type, or welding application type. 8. The welding system of claim 1, wherein the remotely situated analytics computing platform is further configured to generate a control signal based at least in part on the predicted characteristic that is transmitted back to a weld cell for weld process control. 9. The welding system of claim 1, wherein said cloud computing framework is a Platform as a Service (PaaS) or an Infrastructure as a Service (IaaS). 10. The welding system of claim 1, wherein said cloud computing framework employs MapReduce parallel processing. 11. The welding system of claim 1, wherein said cloud computing framework is a platform for managing heterogeneous distributed data center infrastructures. 12. The welding system of claim 1, wherein said production knowledge machine learning algorithm is a scalable machine learning algorithm. 13. The welding system of claim 1, wherein said production knowledge machine learning algorithm is an unsupervised learning algorithm that employs at least one technique selected from a group consisting of: (1) k-means; (2) principal component analysis; (3) hierarchical clustering; (4) self-organizing maps; (5) fuzzy k-means; (6) Dirichlet distribution; (7) independent component analysis; (8) expectation-maximization; (9) mean-shift; and (10) competitive layer neural network. 14. The welding system of claim 1, wherein said production knowledge machine learning algorithm is a supervised learning algorithm that employs at least one technique selected from a group consisting of: (1) linear regression; (2) logistic regression; (3) adaptive logistic regression; (4) artificial neural networks; (5) support vector machines; (6) Naïve Bayesian classifiers; (7) decision trees; (8) random forests; (9) recurrent neural network; (10) nonlinear autoregressive; (11) radial basis; and (12) learning vector quantization algorithms. 15. The welding system of claim 1, wherein the predicted characteristic is employed to facilitate a function selected from a group consisting of: (1) machine learning; (2) predictive modeling or analytics; (3) automation of fault detection and diagnostics; (4) automation of process control; (5) automation of maintenance; (6) automation of quality control; (7) welding personnel training; (8) warranty assessment; (9) weldment design optimization; (10) weld equipment design optimization; (11) welding consumables design optimization; and (12) production workflow optimization. 16. The welding system of claim 1, wherein the large scale dataset further comprises welding equipment maintenance data, weld geometry data, weld quality data, and welding operation productivity data. 17. The welding system of claim 2, wherein the second welding data includes marking data received via the operator interface, wherein the marking data indicates whether said weld, weldment, or weld process is subject to one or more quality classifications or fault classifications. 18. The welding system of claim 1, wherein the communication network receives said first welding data and said second welding data in batches or in live streaming format. 20. A production knowledge system for processing information collected from welding equipment, the production knowledge system comprising: a communication network communicatively coupled with welding equipment situated at one or more physical locations, wherein the communication network is configured to receive, from said welding equipment, first welding data representing a first welding input from a first data source and second welding data representing a second welding input from a second data source; and an analytics computing platform remotely situated from the welding equipment and operatively coupled with the communication network, wherein the remotely situated analytics computing platform is facilitated via a cloud computing framework that employs a distributed and scalable file system running on commodity cluster hardware, wherein the remotely situated analytics computing platform associates the first welding data with the second welding data based at least in part on tag data to define a welding data set, wherein the analytics computing platform generates or updates a large scale dataset based at least in part on said welding data set, the large scale dataset comprising weld operational, production and productivity data, weld quality data, weldment quality data, welding process data, and welding machine parameters data collected from a plurality of heterogeneous data sources, and wherein the remotely situated analytics computing platform employs a production knowledge machine learning algorithm to analyze the welding data set vis-à-vis the large scale dataset to predict a characteristic of the welding equipment or the weld, weldment, or weld process. 21. The production knowledge system of claim 20, wherein the first data source and the second data source each include: a sensor; a non-transitory data storage device; an operator interface; a database; or a combination thereof. 22. The production knowledge system of claim 21, wherein the first data source is associated with a first physical location and the second data source is associated with a second physical location that is different from said first physical location. 23. The production knowledge system of claim 21, wherein the first data source and the second data source are associated with the same physical location. 24. The production knowledge system of claim 21, wherein the first data source and the second data source are heterogeneous data sources. 25. The production knowledge system of claim 20, wherein the analytics computing platform generates a control signal based at least in part on the predicted characteristic. 26. The production knowledge system of claim 21, wherein said cloud computing framework employs MapReduce parallel processing. 27. The production knowledge system of claim 21, wherein said cloud computing framework is a platform for managing heterogeneous distributed data center infrastructures. 28. The production knowledge system of claim 21, wherein said production knowledge machine learning algorithm is a scalable machine learning algorithm. 29. The production knowledge system of claim 21, wherein said production knowledge machine learning algorithm is an unsupervised learning algorithm that employs at least one technique selected from a group consisting of: (1) k-means; (2) principal component analysis; (3) hierarchical clustering; (4) self-organizing maps; (5) fuzzy k-means; (6) Dirichlet distribution; (7) independent component analysis; (8) expectation-maximization; (9) mean-shift; and (10) competitive layer neural network. 30. The production knowledge system of claim 20, wherein said production knowledge machine learning algorithm is a supervised learning algorithm that employs at least one technique selected from a group consisting of: (1) linear regression; (2) logistic regression; (3) adaptive logistic regression; (4) artificial neural networks; (5) support vector machines; (6) Naïve Bayesian classifiers; (7) decision trees; (8) random forests; (9) recurrent neural network; (10) nonlinear autoregressive; (11) radial basis; and (12) learning vector quantization algorithms. 31. The production knowledge system of claim 20, wherein the predicted characteristic is employed to facilitate a function selected from a group consisting of: (1) machine learning; (2) predictive modeling or analytics; (3) automation of fault detection and diagnostics; (4) automation of process control; (5) automation of maintenance; (6) automation of quality control; (7) welding personnel training; (8) warranty assessment; (9) weldment design optimization; (10) weld equipment design optimization; (11) welding consumables design optimization; and (12) production workflow optimization. 32. The production knowledge system of claim 20, wherein the large scale dataset further comprises welding equipment maintenance data, weld geometry data, weld quality data, and welding operation productivity data. 33. A production knowledge system for processing welding data set, the production knowledge system comprising: a communication network communicatively coupled with welding equipment situated at two or more physical locations, wherein the communication network is configured to receive, from said welding equipment, welding data set associated with at least one weld, wherein the welding data set is generated based at least in part on an output signal from one or more sensors, or from one or more databases, and wherein said one or more sensors are situated to capture one or more attributes of a weld or welding process; and processing circuitry that is situated remotely from at least one of said two or more physical locations, wherein the processing circuitry is operatively coupled with the communication network and a weld data store, wherein the weld data store employs a dataset, said dataset comprising weld manufacturing data collected from a plurality of physical locations, and wherein the processing circuitry employs a scalable machine learning algorithm to analyze the welding data set vis-à-vis the weld manufacturing data to predict a characteristic of said at least one weld or weldment, or to predict a characteristic of said welding equipment.