AI Solutions for Manufacturing & Industrial

Quality checking
Quality checking
In the manufacturing process there are several areas where we can use machine learning and computer vision technologies to check the quality of components and products and detect potential defects. Visual checking can be automated completely. We can use cameras and computer vision technologies to analyze the streaming video data to check the quality of PCB and other electronic components.
Predictive analytics
Depends on what sensor data is available we can use machine learning and deep learning technologies to do predictive analytics on equipment failure and recommending proactive maintenance for equipment. The predictive process could narrow down the part that is going to fail so it could be replaced or automatically ordered before the actual failure occurs, thus doing the healing process before the failure.
Predictive analytics
Supply chain optimization
Supply chain optimization
Optimization of supply chain operations has become a necessity, as organizations face with growing customer demands, unpredictable events, and increasing costs. On top of these ongoing challenges, complexities are increased by the growing number of tools required to manage everything from workforce schedules to resource allocation. To meet competitive demands, business decisions are needed quickly and accurately. We can use AI and Machine Learning technologies to streamline the supply chain operations.


Xen.AI can help the Manufacturing and Industrial companies to apply artificial intelligence, machine learning, deep learning and data science technologies in many areas to improve the efficiency and reduce the operating cost.

Customer Case Studies

 Machine Learning based Root Cause Analysis for Manufacturing

 

Challenges:

Client was having a problem where there was machine defects when some of the components were created with several machine defects and several hours of time was required to sort through the chad failures manually. Image shown below are some a problematic chad manifestations as those defects. 

Solution: 

We used computer vision, SVM and deep learning with CNN networks to help with checking failures without the intervention of human elements by detecting dimensions of product that was not meeting specifications. We worked with the client to determine failure rate and classified kinds of failure to determine machine failure or tool failure and dimensionality change. Images above show a QC passes sample and some defective samples with chad failure.

 

We can help build innovative solutions and applications using Artificial Intelligence technologies.