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 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
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.
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 Generation 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 doesnt look like the original is a defect
Assembly verification - classification
Designed to look for low no of defects in specific areas
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Audio defect
​Designed to look for variations in decible 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 -
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
General Manufacturing
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Packaging and printing - colour, offsets, misprints, smudges. Content error
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Textiles - holes, gaps, stitch defects, colour differences, etc.
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Machine tools - parts missing, Wrong sequence in assembly
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Airconditioning - validating ducts/tubes and final packaging
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Solar panels - surface defects - dents, damages, scratches, cracks,
colour difference -
Electricals - presence/absence
Pharma
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Vials and cartridges - surface defects on containers, foreign particle presence, bubble detection
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Tablets and capsules - surface defects, foreign particles
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Needles - surface defects, dimension measurement
Electronics
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Printed circuit boards - presence/absence
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Assemblies (capacitor/resistor) - validation, presence/absence
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Soldering - Wrong/improper soldering
Food
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Packaging - colour, offsets, misprints, smudges. Content error
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damages - crushed, broken, discoloured food products
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assembly - parts missing, Wrong sequence in assembly, counting
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Product inspection - presence/absence
Warehouse
& Logistics
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Palletizating Count Verification
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Inbound/Outbound package verification
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Product Counting
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Inbound/Outbound Invoice Validation
In-machine Realtime Inspection
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Authentication process (automated) - surface defects, dimension measurement
Parts missine, colour, offsets, misprints, smudges. Content error. Wrong sequence in assembly, Wrong /improper soldering
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