Skip to Main Content

Sales:

texto

Spot and Seam Welding Inspection for Automotive and Electronics

​​Automotive and electronics manufacturers face constant pressure to detect weld defects like porosity, undercut, lack of fusion, and alignment issues, while keeping cycle times down and complying with safety rules. Traditional inspection methods struggle to differentiate acceptable weld features and flaws, leading to higher scrap rates, increased costs, and downtime. Cognex vision systems use AI to find and classify weld features, improving quality and throughput while reducing false positives.​ 

Recommended Products

Cognex L38 and L38-500 vision systems

In-Sight L38 Series

AI-powered 3D vision system delivering fast deployment, ease of use, and highly reliable results for advanced inspection, measurement, and guidance applications.

View Product
VisionPro Deep Learning software interface

VisionPro Deep Learning

AI-powered PC image inspection software for fast, robust quality control.

View Product

​​Weld quality is vital for safety and reliability​ 

​​Weld integrity directly impacts product safety, electrical connectivity, and structural reliability in multiple industries. The automotive industry relies on welds for structural integrity and safety. Heavy equipment manufacturers face similar quality demands where weld failures can compromise safety and trigger expensive warranty claims.

Electric vehicles (EVs) need defect-free welds to maintain electrical connections and ensure that containment to prevent dangerous electrolyte leaks. Consumer electronics depend on spot weld quality for voice coil connections in smartphone speakers, where under-welding creates weak contacts that may fail prematurely, while over-welding can shorten component life.  

​Across all these industries, the challenge extends beyond simple presence detection - manufacturers must identify porosity, undercut, lack of fusion, cracks, burns, blowholes, and surface contamination while distinguishing between cosmetic variations and performance-affecting defects. These applications require in-line inspection capabilities that keep cycle times short while ensuring comprehensive defect detection, making reliable, automated weld inspection essential.​ 

​​Traditional inspection tools often miss subtle welding defects​ 

Spot welds and welding seams can vary quite a bit in appearance, with non-uniform shape, position, color, reflectivity, texture, and surface markings on perfectly good welds. Mistaking an acceptable weld for a defective one leads to false positives, also called overkills, that increase scrap rates and costs, and adding manual inspection to look for false positives limits the advantages of automating in the first place.

Other welding inspection challenges include:

  • Complex surface textures and reflective backgrounds that complicate imaging
  • Wide range of defect types from missing welds to porosity that appear unpredictably
  • Excessive false positives that send good parts to manual inspection, disrupting line efficiency

Traditional, rule-based machine vision can’t adapt to variations in welds and variable defects. Manual inspection can't keep pace with automated production speeds and often misidentifies weld defects. The combination of appearance variation, complex backgrounds, and diverse defect types creates an inspection environment where conventional methods consistently underperform, leading to missed defects or production bottlenecks from excessive rejections. 

EV Deep Learning Cap Welding Cylinder

Subtle defects, flaw variability, and reflective surfaces make it hard to separate acceptable welds from defective ones, necessitating AI-powered machine vision systems.


Cognex 3D vision system inspecting the weld on an EV battery pack

​3D machine vision systems use embedded AI and speckle-free lasers to inspect welds.​


AI-powered machine vision makes weld inspection routine 

​​Cognex machine vision systems use powerful AI technology to distinguish between acceptable weld variations and genuine quality defects. Since Cognex AI learns by analyzing images of flawed and adequate welds, it can easily adapt to new defect types and acceptable variations.

​The classification tool categorizes specific defect types, including poor shape, holes, cracks, burns, and surface contamination, enabling upstream process control that minimizes defects over time.  

​For complex applications like piston seam weld inspection, the system handles overlapping seams that are desirable safety features while identifying problematic missing, overpowered, or underpowered welds. Cognex machine vision excels in challenging environments with dark image areas, textured backgrounds, and variable lighting conditions that confuse traditional systems.  

​A range of cameras can be used to image the weld for analysis. Although a 3D camera may be required to measure weld volume, a 2D camera can supply the images for all other defect detection and ensure a proper positioning of the spot welder before the process starts.

​AI-powered solutions dramatically reduce false positives compared to rule-based approaches, keeping production lines running efficiently while maintaining thorough quality control across automotive, electronics, and heavy equipment applications.  

​Cognex keeps your weld inspection running at top form by giving you the resources and support you need to expand your skills, add new products or capabilities, and troubleshoot any problems quickly. ​ 

In-Sight 3D vision systems detect spot weld defects like holes, recesses, and over-fill

Subtle spot-weld defects, like holes, recesses, and over-welds, often require 3D machine vision solutions.


Using Deep Learning In Spot Welding Applications | English

Using Deep Learning in Spot Welding Applications

Download