Common Challenges In Manufacturing
100% assurance in cognitive quality control not sampling
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In high volume production lines, visual inspection becomes the bottleneck process. Conflict between speed and QC
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Quality control usually done in batches – wasteful loss on batch reject, risk of batch level approvals.
Calibration of economic and quality levers
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In high volume production lines, visual inspection becomes the bottleneck process. Conflict between speed and QC
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Quality control usually done in batches – wasteful loss on batch reject, risk of batchlevel approvals.
Traditional machine vision has not worked
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Most existing machine vision solutions detect anomalies and not defects.
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They are not equipped to handle drift in manufacturing process, causing high cost of repurposing
Reliance on individuals - Expensive and shortage of expertise
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Unavailability of trained Subject Matter Experts (SME). Loss of community knowledge when SME quits
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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
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Manual inspection of basic defects that an eye can see (cognitive and measurable) – at a batch level/QC by sampling
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Lower accuracy levels, defect leakage and bottlenecks in production line
Evolved QC
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Using machine vision (cameras) to detect anomalies (anything that doesnt look like a perfect model)
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Only able to detect defects at a basic level.
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Image to image comparisons result in high false positives.
Next Gen QC
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AI powered Automated Cognitive Quality Control Solution
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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
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Fully automated, high accuracy, high speed and consistency.
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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.

Surface defect - object detection
Designed to look for specifically defined objects - can be trained and added to defect library

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

Assembly verification - classification
Designed to look for low number of defects in specific areas

Audio defects
Designed to look for variations in decibel levels and vibrations

Identification services
Designed to look for anomalies on printed material - labels, barcodes and QR Codes
Industries We Work With
Automotive
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Fine blanking and forging part - surface defects - dents, damages, scratches, cracks, rust/colour difference
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Assembly and casting parts - process/part missing, product sorting, counting in packaging and assemblies, Wet leak test
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Ground and machine parts - Counting in packages and assemblies
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Radiography - defects on Xray - porosity, shrinkage
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Print quality - colour, offsets, misprints, smudges. Content error
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Noise-based inspection (NVH) - Seatbelt, Car horn
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All Products - product sorting, dimension measurement

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

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Labels indicates data collected
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Captures “community knowledge”, which acts as benchmark
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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

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Month 1 closely reflecting trained data
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Month 2 – process drift identified! Major occurrences of defect changed orientation
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Visual summary of occurrence trend for each type of defect by location
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Critical input for Preventive Action and continuous improvement
Cutting Edge Analytics:
Recognizing And Clustering Defects

The Cluster Map Shows
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Pattern in defects will be identified and grouped as clusters
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Mapping of existing defects
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Simultaneous mapping of other possible defects/features not trained to AI model
Key Outcomes of the Analysis
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Helps prioritize focus areas
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Allows to identify clusters and reclassify them as OK or NG
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Allows for continuous upskilling for new defect identification and improving accuracy of the end product