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Spot Welding Defect Prediction - Siemens | MindSphere · MindSphere Partner Use Case Americas...

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Siemens www.siemens.com/mindsphere MindSphere Partner Use Case Americas Europe Asia- Pacific +1 314 264 8499 +44 (0) 1276 413200 +852 2230 3333 Spot Welding Defect Prediction Benefits Predict spot welding defects in real time using machine process data Achieve spot welding success ratios of better than 95% first time through Allow line operators to quickly address defects in process, minimizing later inspection time and rework Raise customer satisfaction by reducing product defects that reach customers Features Artificial intelligence applied to real time machine process data. Monitored pro- cess parameters locate potential failure points. Output data is actionable and easy to understand. Line operators can quickly take action. Reporting data available to plant man- agement, R&D, Quality operations. Integration with PLM, ERP, MES systems Predict defects in spot welding using machine data and AI Summary MindSphere enables defect prediction in spot welding techniques commonly used by automotive manufacturers. Utilizing advanced analytics to examine process data from your lines, defect prediction and analysis is improved to help your line operators quickly address defects within the production cycle. Many automotive companies leverage spot welding in manufacturing processes due to their speed and relatively low cost. Due to the high number of welds taking place, it is difficult to manually inspect and detect flaws across all of the spot welds being made. Predictive modeling, real time AI This solution use case, provided by Tech Mahindra on the MindSphere platform, enables rapid response to challenges with spot welding based on machine data that would otherwise not be easily noticed by an operator. Actionable alerts and instructions are provided to the operator to address po- tential flaws while the part is in the most cost-effective place to address the defect. This detection process reduces rework costs to the manufacturer and improves product quality by helping detect potential flaws be- fore they leave the factory. Spot welding is a very economical approach to automotive manufacturing, providing potential defects can be caught early. With a 95% first time through success ratio, hidden rework costs are minimized and quality is improved. MindSphere is the cloud-based, open IoT operating system from Siemens that connects real things to the digital world, and enables powerful industry applications and digital services to drive business success. MindSphere’s open Platform as a Service (PaaS) enables a rich partner ecosystem to develop and deliver new applications. © 2018 Siemens AG. Siemens, the Siemens logo, MindSphere, MindAccess, MindConnect and MindServices are trademarks or registered trademarks of Siemens AG. All other trademarks, registered trademarks or service marks belong to their respec- tive holders. 73990-A1 9/18 A
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Page 1: Spot Welding Defect Prediction - Siemens | MindSphere · MindSphere Partner Use Case Americas Europe Asia- Pacific +1 314 264 8499 +44 (0) 1276 413200 +852 2230 3333 Spot Welding

Siemens www.siemens.com/mindsphere

MindSphere Partner Use Case

AmericasEuropeAsia- Pacific

+1 314 264 8499+44 (0) 1276 413200+852 2230 3333

Spot Welding

Defect Prediction Benefits

• Predict spot welding defects in real time using machine process data

• Achieve spot welding success ratios of better than 95% first time through

• Allow line operators to quickly address defects in process, minimizing later inspection time and rework

• Raise customer satisfaction by reducing product defects that reach customers

Features

• Artificial intelligence applied to real time machine process data. Monitored pro-cess parameters locate potential failure points.

• Output data is actionable and easy to understand. Line operators can quickly take action.

• Reporting data available to plant man-agement, R&D, Quality operations.

• Integration with PLM, ERP, MES systems

Predict defects in spot welding using machine data and AI

Summary MindSphere enables defect prediction in spot welding techniques commonly used by automotive manufacturers. Utilizing advanced analytics to examine process data from your lines, defect prediction and analysis is improved to help your line operators quickly address defects within the production cycle. Many automotive companies leverage spot welding in manufacturing processes due to their speed and relatively low cost. Due to the high number of welds taking place, it is difficult to manually inspect and detect flaws across all of the spot welds being made.

Predictive modeling, real time AI

This solution use case, provided by Tech Mahindra on the MindSphere platform, enables rapid response to challenges with spot welding based on machine data that would otherwise not be easily noticed by an operator. Actionable alerts and instructions are provided to the operator to address po-tential flaws while the part is in the most cost-effective place to address the defect.

This detection process reduces rework costs to the manufacturer and improves product quality by helping detect potential flaws be-fore they leave the factory.

Spot welding is a very economical approach to automotive manufacturing, providing potential defects can be caught early. With a 95% first time through success ratio, hidden rework costs are minimized and quality is improved.

MindSphere is the cloud-based, open IoT operating system from Siemens that connects real things to the digital world, and enables powerful industry applications and digital services to drive business success. MindSphere’s open Platform as a Service (PaaS) enables a rich partner ecosystem to develop and deliver new applications.

© 2018 Siemens AG. Siemens, the Siemens logo, MindSphere, MindAccess, MindConnect and MindServices are trademarks or registered trademarks of Siemens AG. All other trademarks, registered trademarks or service marks belong to their respec-tive holders. 73990-A1 9/18 A

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