Manufacturing Intelligence | Trinesis Technologies

Amphenol FCI manufactures electrical connectors through a multi-step precision process. We deployed FluxAI across the three most critical manufacturing stages.

Manufacturing Process Flow

⚙

Raw Material

Metal strips, polymers

→

⚗

Stamping

800+ strokes/min

→

🏭

Moulding

Injection moulding

→

⚡

Welding

Ultrasonic joining

→

📦

Assembly

Final connectors

FluxAI Implementation at Critical Stages

⚗ Stamping

Vision AI captures every stamped component. Dimensional verification against spec. Die wear prediction 7 days ahead. Auto-stop on out-of-spec detection.

🏭 Moulding

Real-time parameter monitoring - pressure, temperature, cycle time. Shot-to-shot consistency tracking. Defect prediction before parts exit mould.

⚡ Welding

30 welding machines monitored. Energy, force, time parameters. Pull strength prediction without destructive testing. 100% inspection coverage.

Production Results

84%

OEE

from 62%

87%

Downtime Reduction

15.8% to 2.1%

<1%

Defect Rate

from 3.2%

7 days

Failure Prediction

advance warning

200 machines deployed in 14 weeks. Now expanding to all 4 Amphenol factories in India.

2

Smart Energy Infrastructure

Meter Data Management & AI Analytics for a leading Indian OEM.

Recently deployed a complete data platform for a major Indian smart meter manufacturer — demonstrating our expertise in energy systems and industrial data analytics.

📈 Meter Data Management (MDM)

VEE Engine: Validation, estimation, editing for meter data
Data Infrastructure: Long-term storage for millions of meters
HES Integration: Head-end system connectivity
RDSS Compliance: Full regulatory alignment
Billing Integration: Utility system connectivity

🤖 Appliance Load Disaggregation

AI-Powered: Identify individual appliances from aggregate consumption
No Additional Hardware: Uses existing smart meter data
Consumer Insights: "Your AC used 35% of your bill"
Detection Accuracy: 88-95% for major appliances
Market Differentiator: First in Indian market

FluxAI Analytics Layer

📈 Load Forecasting

Short, medium, and long-term demand prediction. Weather integration. 95%+ accuracy.

🔍 Anomaly Detection

Identify unusual consumption patterns. Theft detection with ML models. 3x fewer false positives.

👥 Consumer Segmentation

Behavioral clustering. Tariff optimization. DSM targeting. 15+ segment profiles.

3

VFD Manufacturing Intelligence

Test station analytics and quality prediction for FRENIC production.

VFD Production & Test Flow

📌

PCB Assembly

Control boards

→

🔨

IGBT Mount

Thermal interface

→

🌡

Thermal Test

Temperature rise

→

📈

Load Test

Performance

→

⚡

HiPot Test

Insulation

→

✔

Ship

Passed QC

Current Challenges

Thermal Test	Failures detected late in cycle
IGBT Mounting	Torque variance causes thermal issues
Vibration Patterns	Manual signature analysis
Root Cause	Hours to correlate failures to assembly
Retest Cycles	5-8% units need multiple test runs

FluxAI Solution

Failure Prediction	Flag issues BEFORE test fails
Assembly Correlation	Link failures to torque, TIM volume
Pattern Recognition	ML models on vibration/thermal data
Instant Root Cause	AI identifies cause in seconds
First-Pass Yield	Reduce retest to <2%

AI Prediction Example

Unit FRN-4521: Passed vibration and load tests. Thermal test marginal. FluxAI confidence: 82%. Recommendation: Check IGBT thermal pad before shipping.

The AI compares test signatures against historical field failures — flagging units that technically "pass" but match patterns of units that failed in the field.

4

PCBA Production Intelligence

SMT line analytics for in-house PCB assembly.

SMT Line Process

📋

Bare PCB

Board input

→

🖨

Paste Print

Solder paste

→

🔍

SPI

Paste inspection

→

🔨

Pick & Place

Components

→

🔥

Reflow

Soldering

→

📷

AOI

Final inspect

Pre-Reflow Defect Prediction

Traditional quality control catches defects at AOI — after the board has gone through the oven. By then, the defect is permanent. FluxAI predicts defects before reflow.

🖨 Paste Volume Analysis

SPI data → insufficient solder prediction. Flag boards before placement.

🔨 Placement Accuracy

Pick & place offset tracking. Bridging and tombstone risk scoring.

🌡 Environmental Factors

Humidity, temperature correlation. Paste viscosity trending.

What We Monitor

Solder Paste Volume	Per-pad measurement
Placement Offset	X/Y/theta per component
Reflow Profile	Zone temperatures, dwell times
First Pass Yield	Boards passing AOI first time
Defect Pareto	Type, location, frequency

AI Actions

Predict insufficient solder	Before reflow
Trigger calibration alerts	Before defects occur
Optimize reflow profile	Per board type
Identify process drift	Real-time SPC
Root cause correlation	Seconds, not hours

5

How We Work

Pune-based team. Factory-floor experience. Production-ready deployment.

1

Discovery

Factory visit. Machine audit. Data sources. Requirements.

2

Edge Deployment

FluxAI devices on machines. Data pipeline. Cloud setup.

3

Model Training

ML models trained on your production data. Validation.

4

Production Live

Dashboards. Alerts. Training. Go-live.

Trinesis Capabilities

Engineering Team	85+ engineers, Pune HQ
Domain Expertise	Electronics, power systems, energy
Platform	FluxAI — production-deployed
Edge Hardware	Proprietary FluxAI Edge devices
Live Reference	Amphenol FCI — 200 machines

Ready for Factory Visit

Chennai VFD facility or Pune UPS plant. Identify priority lines. Define scope. Deploy.

Next Steps

1

Factory Visit

1-day assessment at Chennai or Pune. Identify machines, data sources, priority lines.

2

Technical Proposal

Detailed scope, architecture, integration plan, timeline, investment.

3

Deployment

Phase 1 on priority lines. Results visible within 12 weeks.

VFD test station or SMT line as priority?

Chennai or Pune facility to start?