Machine Learning in Automotive

Artificial intelligence is being implemented in the Automotive Industry, from the factory floor to post-production and analysis. AI helps to improve production workflows, maintenance and diagnostics processes, and automobile servicing.

COMPUTER VISION - Image Verification & Quality Control

Automobile Parts Quality

Manual inspection of Automobile parts, manufactured and from third party suppliers, is a cumbersome and expensive task. Even a slight variance in material quality or integrity can affect the assembly process of the vehicle. AI techniques that automatically detect defects in the early stages can help reduce material waste, repair, and rework costs. Automated inspections, assisted by Computer Vision, use multiple scanners and cameras for inspecting the manufacturing and assembly lines. This ensures that only the highest quality of Automobile parts move onto the next production line process.

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NATURAL LANGUAGE PROCESSING - Named Entity Recognition

OEM Manuals Querying

Original Equipment Manufacturers (OEMs) create detailed product manuals for each automobile model type in their fleet. These are usually thousands of pages long with information about vehicle specifications, diagnostic procedures, and troubleshooting. Using a Natural Language Processing platform, this unstructured data can automatically be tagged for these relevant details, categorized, and stored onto a database. A repair technician can quickly search through the manuals digitally, using phrases as a search query and receive an extract of the required information. This saves processing time to examine these manuals and speeds up the diagnostic and repair processes.

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NATURAL LANGUAGE PROCESSING - Named Entity Recognition

Repair Order Diagnostics

In the automotive industry, the invoices for third-party repair orders are usually created by multiple repairmen working on the same unit. Processing these unstructured documents for diagnostic purposes can be difficult due to a lack of language structure or grammatical errors. Natural Language Processing is able to process this data much more efficiently than human technicians. AI is taught to extract information like repair type, most common types of repairs, frequently failing models, and can give technicians insight into the failures that happen to specific vehicle models. This makes the diagnostic process much more efficient and simplified, saving technicians hours in viewing these documents manually.

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