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Common Challenges In Manufacturing

100% assurance in cognitive quality control not sampling

  • In high volume production lines, visual inspection becomes the bottleneck process. Conflict between speed and QC

  • Quality control usually done in batches – wasteful loss on batch reject, risk of batch level approvals.

Calibration of economic and quality levers

  • In high volume production lines, visual inspection becomes the bottleneck process. Conflict between speed and QC

  • Quality control usually done in batches – wasteful loss on batch reject, risk of batchlevel approvals.

Traditional machine vision has not worked

  • Most existing machine vision solutions detect anomalies and not defects.

  • They are not equipped to handle drift in manufacturing process, causing high cost of repurposing

Reliance on individuals - Expensive and shortage of expertise

  • Unavailability of trained Subject Matter Experts (SME). Loss of community knowledge when SME quits

  • Manual Inspection is highly subjective. Results in inconsistent output. Human accuracy rarely exceeds 85%, usually due to employee fatigue. Leads to excess false positives, wasteful rejection

Quality Control In Manufacturing

Traditional QC

  • Manual inspection of basic defects that an eye can see (cognitive and measurable) – at   a batch level/QC by sampling

  • Lower accuracy levels, defect leakage and bottlenecks in production line

Evolved QC

  • Using machine vision (cameras) to detect anomalies (anything that doesnt look like a perfect model)

  • Only able to detect defects at a basic level.

  • Image to image comparisons result in high false positives.

Next Gen QC

  • AI powered Automated Cognitive Quality Control Solution

  • Using AI to understand defects and its types and make intelligent decisions that ensure high quality products – just like a good subject matter expert would

  • Fully automated, high accuracy, high speed and consistency.

  • Integrated with Industry 4.0 platforms to enable and effective intervention  for better quality

Our Solutions

Vertical solutions for Incoming, In-process(Individual/Mulit-line), 

and final Inspection leveraging.

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Surface defect - object detection

​Designed to look for specifically defined objects -  can be trained and added to defect library

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Anomaly detection

​Anomaly Detection aims to identify anomalies - Anything that doesn't look like the original is a defect​

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Assembly verification - classification

Designed to look for low number of defects in specific areas​

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Print/Object character verification
(OCR/OCV)

Designed to look for Text detection and text recognition

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Identification services

Designed to look for anomalies on printed material - labels, barcodes and QR Codes

Industries We Work With

Automotive

  • Fine blanking and forging part - surface defects - dents, damages, scratches, cracks, rust/colour difference

  • Assembly and casting parts - process/part missing, product sorting, counting in packaging and assemblies, Wet leak test

  • Ground and machine parts - Counting in packages and assemblies

  • Radiography - defects on Xray - porosity, shrinkage

  • Print quality - colour, offsets, misprints, smudges. Content error

  • Noise-based inspection (NVH) - Seatbelt, Car horn

  • All Products - product sorting, dimension measurement

Engine

Cutting Edge Analytics:
Identifying Process Drifts

Step 1. Training Data to AI

QC Baseline (50 Samples)

Step 2. AI in Production

Raw Image with AI Inference

  • Labels indicates data collected

  • Captures “community knowledge”, which acts as benchmark

  • Matches shop floor information with reality

Step 3. Outcome From Heatmap Production Trend

Month 1 in Production: 514 Instances

Month 2 in Production: 429 Instances

  • Month 1 closely reflecting trained data 

  • Month 2 – process drift identified! Major occurrences of defect changed orientation

  • Visual summary of occurrence trend for each type of defect by location

  • Critical input for Preventive Action and continuous improvement

Cutting Edge Analytics:
Recognizing And Clustering Defects

The Cluster Map Shows

  • Pattern in defects will be identified and grouped as clusters

  • Mapping of existing defects

  • Simultaneous mapping of other possible defects/features not trained to AI model

Key Outcomes of the Analysis

  • Helps prioritize focus areas

  • Allows to identify clusters and reclassify them as OK or NG

  • Allows for continuous upskilling for new defect identification and improving accuracy of the end product

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