Auto parts are complex and may present a multitude of defects.
Deep learning solutions can help.
“It provides a collaborative approach, not a Blackbox application. It helps us to understand technology, apply, and expand it across our manufacturing footprint by ourselves.” — Denso
Automobile parts of all types benefit from visual inspection and machine vision technologies — from a printed circuit board up to the vehicle’s door.
- Electric Vehicle Battery InspectionMachine vision deployment in electric vehicle (EV) battery inspection has grown in recent years because of the rise in EV popularity. Manufacturers have used 2D and 3D machine vision technologies to inspect battery components and cells, but there are several different types of batteries and a considerable amount of variability when it comes EV battery assemblies.
- Spot Welding InspectionSpot welding involves joining two or more metal pieces together by applying pressure and heat from an electric current to the weld area, which creates slight variations in each weld. Oftentimes, machine vision cameras capture these acceptable variations as shadows or reflections, which machine vision software reads as a defect. Deep learning software can help differentiate between actual defects and variations in the image.
- Crack Inspection of Automotive PartsSmall cracks in automotive parts such as camshafts, brake discs, or brake pads can potentially lead to failures that can be costly and damage customer relationships. Early identification of such cracks is critical for automotive manufacturers.
- Radiator (HVAC System) InspectionAutomotive parts such as radiators can feature complex patterns, making visual inspection challenging, oftentimes producing high false-positive rates.
- Part Assembly InspectionIn automated automotive manufacturing processes, companies must ensure that the correct parts get installed in the right location. Consider the ramifications of riveting panels or structural components together in the wrong spots. Machine vision systems leveraging deep learning software can help identify the correct parts while also spotting potential defects, saving the company time and money on costly rework.
- Leak DetectionOnce an automotive assembly process has completed, the manufacturer must ensure that no leaks are present. With the help of deep learning–based visual inspection, companies can detect leaks and avoid product escapes.
- Final Assembly VerificationDeep learning–based visual inspection systems can verify proper assembly of the correct rim, wheel, and tire type for the right vehicle model, for example.
- Seat Thread InspectionVisual inspection systems utilizing deep learning software can ensure proper interior automotive part installation and allow the vehicle to move along to the next part of the assembly process.
- Painting and Surface DefectsOften performed by manual inspectors, surface defect detection helps prevent vehicles from leaving the factory floor with imperfections, which is critical for the company’s reputation.
LandingLens Benefits for Automotive Manufacturing
LandingLens, an industry-first data-centric artificial intelligence (AI) visual inspection platform, helps improve inspection accuracy and reduce false positives. The end-to-end platform standardizes deep learning solutions that reduce development time and scale projects easily to multiple facilities across the globe.
Maintain Quality, Boost Efficiency
Automotive manufacturing involves many part types of varying shapes and sizes. Defects in these parts can lead to problems for manufacturers and customers, so identifying defects early in the process is a top priority. Certain complex inspection tasks present problems for rules-based machine vision solutions, but deep learning software can help. LandingLens software lets automotive manufacturers maintain product quality by deploying their own deep learning models and optimizing inspection accuracy without any impact on production.
A Complete Inspection System
Automotive manufacturers can deploy LandingLens to augment existing automated inspection systems. LandingLens can augment a machine vision system by providing an added layer of security. For example, if a rules-based system rejects a part, LandingLens can reevaluate the rejection to distinguish an actual defect from an acceptable variation. It ensures that acceptable parts move to the next assembly step, maintaining an efficient production flow while catching defects early in the process.