Emerging Machine Vision Technologies
V-ONE provides detailed production insights and image traceability across ViTrox's SMT inspection systems, enabling a strategic and data-driven approach to manufacturing—making it a powerful enabler for Smart Manufacturing operations.
Designed to solve the problem of "siloed" monitoring by providing one dashboard with complete visibility for the entire SMT line.
Customisable interface that visualises live production data, KPls, and alerts for informed, data-driven decision-making on the sh op floor.
Eliminates manual data hunting by letting users scan a barcode, instantly visualise the PCBA, and drill down to find individual component defects.
Provides closed-loop image traceability across our smart 3D inspection solutions.
Designed to optimise your manufacturing processes with advanced features.
All collected data is efficiently managed through a centralised server and system
Guides operators through defect verification and repair with visual data and inspection images for higher accuracy and efficiency.
Analyses machine data to predict and prevent potential failures, reducing unplanned downtime and maintenance costs.
Rea-time tracking of machine conditions, performance, and utilisation to ensure optimal operation and quick response to issues
In the high-stakes world of electronics manufacturing services (EMS) and semiconductor production, "Industry 4.0" is no longer a buzzword—it is the baseline for competitiveness. Manufacturers today face a perfect storm of challenges: shrinking components, labour shortages, expensive equipment downtime, and the overwhelming volume of data generated by modern lines.
To survive, factories must move from Reactive (fixing what is broken) to Predictive (fixing it before it breaks). This is where ViTrox V-ONE enters the equation. As a leading Manufacturing Intelligence Solution, V-ONE does more than just display data; it connects, visualises, analyses, and autonomously acts to create a Process Self-Healing Line. This comprehensive guide details every facet of the V-ONE ecosystem, demonstrating how it transforms disjointed SMT lines into a unified, intelligent smart factory.
1. The Core Philosophy: Connect, Visualize, Proact, Digital Twin V-ONE is built on four strategic pillars that define the journey to digital transformation:
2. Real-Time Visualization: The "War Room" for Manufacturing Managers cannot fix what they cannot see. V-ONE offers a suite of dynamic dashboards that serve as the "single source of truth" for the factory floor.
Line Insight: The Executive OverviewThe new Line Insight module provides total visibility across the entire factory. Instead of checking machines individually, operations managers can view a global dashboard displaying First Pass Yield (FPY), Throughput, and Machine Status for every line in a single pane of glass. It allows users to filter by Workcell or Line Name instantly, making it the perfect "morning coffee" dashboard for executive decision-making.
Machine Status Monitoring (OEE & MU)Maximising Return on Investment (ROI) requires keeping machines running. V-ONE tracks:
Production Output & KPI DashboardsCustomisable templates allow engineers to track FPPM (False Fail Parts Per Million) and DPPM (Defect Parts Per Million) trends. Users can drill down from a monthly yield chart directly into specific defect images, bridging the gap between high-level metrics and ground-level root causes.
3. Process Optimization: The "Self-Healing" BrainThe most powerful aspect of V-ONE is its ability to correlate data across the line to fix problems. This is achieved through three advanced modules.
A. ONE View: Intelligent "Digital Thread" TrackingA major pain point in SMT is the time wasted manually correlating data between machines.ONE View links the inspection results of every single component across the line.
B. Process Insight: From Reactive to PredictiveTraditional quality control relies on operators noticing trends—a slow and error-prone process. Process Insight automates this vigilance.
Intelligent Control Tower: Remote Force MultiplierThe Control Tower allows a single expert to manage quality across multiple lines without entering the production floor.
4. Asset Management: Zero Unplanned DowntimeIn high-volume manufacturing, unplanned downtime is the ultimate profit killer. V-ONE’s Asset Management suite, powered by IoT, protects your most expensive hardware.
AXI Predictive MaintenanceX-ray tubes are costly consumables. V-ONE monitors critical telemetry to predict failure before it happens:
IoT Sensor Integration (XLTM)For older machines or environmental monitoring, V-ONE integrates with XLTM IoT Sensors. These can be magnetically attached to machines to monitor ambient temperature, humidity, and vibration, feeding this physical data directly into the V-ONE dashboard for correlation with yield data.
5. Traceability & Compliance: Audit-Ready AnytimeFor Automotive, Medical, and Aerospace sectors, traceability is non-negotiable. V-ONE provides Full Board Traceability 2.0.
6. Operational Efficiency: Streamlining the WorkflowV-ONE also digitises the manual workflows that slow down production.
7. The Connectivity Ecosystem: Open & ScalableA smart factory is only as good as its connections. V-ONE is an Open Platform designed to work with your existing equipment.
ViTrox V-ONE is not just software; it is a strategy. By integrating AI-driven Process Insight, Predictive Maintenance, and Full Traceability, it solves the "blind spots" in manufacturing. It empowers factories to operate with fewer staff, resolve defects faster, and protect expensive assets, ultimately delivering the Self-Healing Line promise.
Customer Testimonials Inquire
Complete Line Visibility to Instantly Pinpoint Bottleneck
Deep-Dive Product and Defect Analysis
Comprehensive OEE and Utilisation Intelligence
Highly Customisable and Prioritised
Real-Time Dashboard
Yield or Process Optimisation
Machine Productivity & Performance
Improved Quality Control
Comprehensive Full Board Visualisation
Detailed Component-Level Drill Down
Enhanced Data Security
Productivity Improvement and Bottleneck Analysis
V-ONE is well equipped with great data analytics tool and can be used for every small and medium-sized enterprise (SME) ....
watch the testimonial video
ViTrox’s Industry 4.0 Smart Solution, V-ONE designed with many innovative features which play an essential role in supporting the real-time production process monitoring at Penang Automation Cluster Sdn. Bhd. (PAC) during the journey of digital transformation with the commitment to uplift the local precision metal fabrication ecosystem and drive the creation of a supply chain ecosystem to support Local Large Companies (LLCs) and Multinational Corporations (MNCs).
Since early 2020, ViTrox has been collaborating with PAC to implement V-ONE to strengthen PAC’s production process. With the machine connectivity and visualization through V-ONE, it enables PAC to connect the SME's legacy machines within PAC premises. Besides, V-ONE provides value-added data analytics and artificial intelligence proactive actions to strengthen the PAC real-time production process monitoring.
Recently, ViTrox is honoured to have three guests from PAC sharing their unique V-ONE experience with us. The three honoured guests are Mr Hng Chuan Keat, Operation Manager, Mr Terrence Tan, Production and Marketing Manager, and Mr Liong Hock Chuan, Machining and Tooling Manager.
“I would say V-ONE is well equipped with great data analytics tool and can be used for every small and medium-sized enterprise (SME). The most convenient feature that I like is our legacy machine’s data source can be easily retrieved from the tower light sensor kit, and digitally auto-converted and transferred to the V-ONE cloud database. V-ONE could form new strategies to enhance our manufacturing performance through detailed data analysis,” quoted by Mr Terrence Tan, Production and Marketing Manager of PAC.
“With real-time updates and customizable integration, V-ONE provides further visibility and insight across the entire production line. We can easily monitor the performance side by side with the machine or monitor from the control room remotely. Although this is just a start-up journey, we believe V-ONE could guide our production, save up to 30% of operating cost and reduce the process time cycle," said Mr Liong Hock Chuan, Machining and Tooling Manager of PAC.
"I am glad that with V-ONE's Alert Plan Development for predictive and preventive maintenance, our production engineers and technicians easily visualize the machine's threshold limit - Machine Utilization (MU) in percentage, while the auto alert receiver will be triggered if there is abnormal activity going on with the machines. Soon, we hope to connect all of our newest machines and cover the entire PAC principle activities, linking from CNC machine to Precision Tooling, Sheet Metal Fabrication, Metal Finishing, Module Assembly, and OQA, enabling IoT connectivity between each machine and be ready for Industry 4.0."
We highly encourage all to take a moment to watch a PAC testimonial video to get more insights from the three PAC managers through this link.
To discover more about Industry 4.0 Smart Solutions V-ONE and how to step closer to Industry 4.0 success? We highly recommend you to contact our sales expert by sending your enquiries to cc-vone@vitrox.com.
For more information about V-ONE, feel free to visit V-ONE official website at https://www.v-one.my/.
Automatic Triggers for Front Process Good or Bad Images
Real-time Defect Detection
Comprehensive Defect Root Cause Assumption Analysis
Side-by-side Cross Reference Analysis
Centralised Remote Control
Remote Tuning (V-Tune Sync) without Halting the Production Line
Easy Traceability of Component Images
Eliminate Human Manual Sticker Indication
Accurately Pinpoint Defects Location
Increase Rework Efficiency
Allows Feedback & Comments on Rework Results
Optimal Usage
Maintenance Efficiency
Production Downtime Reduction
Lower Operational Cost
Eliminate Production Bottlenecks
Increased Productivity
Optimise Machine Usage
Remote Monitoring
1. The Problem: In traditional SMT manufacturing, inspection data is highly siloed. When a defect—such as a Head-in-Pillow (HiP) or a BGA short—is detected at the end of the line by an X-ray machine, the inspection records and images are scattered across different systems. Engineers must manually trace the barcode and walk between machines to piece together the history, which can take up to 2 hours just to diagnose a single defect.
2. The Science: The solution lies in creating a unified "Digital Thread" using ONE View. By centralising data from Solder Paste Inspection (SPI), Automated Optical Inspection (AOI), and Advanced X-ray Inspection (AXI), systems utilise spatial mapping to correlate images automatically. Advanced features like Pin-Pad Joint Synchronisation allow an engineer to click on a specific pin in an X-ray image and instantly view the exact corresponding solder paste deposit on the SPI image.
3. The Spec: Cross-platform synchronisation reduces root-cause troubleshooting time from ~2 hours down to minutes. By centralising this data, the system instantly matches 100% of board barcodes across SPI, AOI, and AXI platforms.
1. The Problem: One of the most dangerous risks in high-volume manufacturing is "Consecutive Defects Unattended". If a machine parameter drifts—such as a clogged stencil—it can cause a string of consecutive defects. Operators might miss this subtle trend, resulting in expensive rework or unrecoverable scrap before human intervention occurs.
2. The Science: To shift from reactive to predictive quality, factories must deploy AI-Assisted Defect Analysis. Machine learning algorithms continuously aggregate and analyse real-time inspection data to detect specific anomaly patterns. When the AI identifies a trend, it triggers an auto-ticketing workflow. The AI acts as a digital process doctor, interpreting the data to suggest the precise actionable root cause.
3. The Spec: Automated systems can trigger alerts based on specific logic thresholds—such as detecting 3 consecutive true-call defects. The AI models can identify critical drifts, such as a 40% spike in solder bridge defects over a 2-hour window, triggering a direct recommendation to "check stencil cleaning".
1. The Problem: Advanced X-ray Inspection (AXI) machines rely on high-value, consumable hardware—specifically X-ray tubes—that naturally degrade over time. Without proactive visibility, these components fail unexpectedly, halting the entire production line and causing severe financial leaks.
2. The Science: Unexpected failures are mitigated through IoT Telemetry and Predictive Maintenance algorithms (such as SVM-Based or LSTM-Based Anomaly Detectors). IoT sensors continuously track critical hardware parameters like the X-ray tube's cathode level, power consumption, ambient temperature, and fan speeds. AI models analyse this telemetry to flag outliers and forecast the asset's remaining lifespan, allowing for scheduled maintenance.
3. The Spec: Predictive monitoring tracks exact hardware variables in real-time, logging data such as an X-ray tube drawing 16W at 243.4V, or tracking over 6,160 hours of total power-on time for cathode life.
1. The Problem: The industry is facing a severe human resources constraint. Machine fine-tuning is typically a manual task where engineers walk the line to adjust parameters. This "fire-fighting" approach wastes valuable engineering hours on low-impact tasks.
2. The Science: Factories address labour constraints by utilising an Intelligent Control Tower equipped with Smart Impact Calculation. AI analyses false call trends across the factory and calculates the exact production impact of each defect, generating prioritised task cards. Through V-Tune Sync, engineers can adjust algorithms remotely without stopping the production line.
3. The Spec: By mathematically calculating defect impact, the system identifies the most critical tasks—for example, alerting the engineer that tuning a specific component will resolve a 51,000 PPM (Parts Per Million) false call rate. A single engineer can effectively manage quality across multiple machines remotely.
1. The Problem: Different inspection machines often use different sequence setups and component ID naming conventions (e.g., one machine labels a component "1:C04" while another uses "1_C04"). This discrepancy breaks the digital thread, making cross-platform correlation impossible.
2. The Science: Seamless traceability is achieved through ONE View Components Matching 2.0 algorithms. Instead of forcing engineers to reprogramme every machine, the software uses the Board Barcode as the primary anchor and applies symbolic normalisation to the Reference Designators (Ref Des) to standardize variations in the background.
3. The Spec: These algorithms normalise variations seamlessly, guaranteeing a 100% barcode match across SPI, AOI, and AXI systems without requiring engineers to modify the original programme panel arrangement.
1. The Problem: Implementing factory-wide intelligence platforms often involves complex backend configurations and deploying massive server images, which leads to human error during manual setup and excessive storage requirements.
2. The Science: Next-generation smart manufacturing platforms utilise Automated Installers and advanced containerisation. These systems run automatic System Prerequisites Scans. By optimising the database structure and image processing servers, the software footprint is drastically compressed, enabling "command-free" Linux deployments.
3. The Spec: Automated deployment reduces the required OVA (Open Virtual Appliance) file size down to just 13 GB (a 35% or 7 GB reduction). The automated installer handles 95% of configuration settings, eliminating the need for engineers to manually execute up to 50 pages of Work Instruction (WI) setup procedures.
1. The Problem: In traditional rework stations, operators rely on manual paper sticky notes or CADCAM software to locate defects on a board. This manual indication is slow and highly prone to human error, often leading to repairs on the wrong component.
2. The Science: Factories eliminate this by deploying a Smart Rework Station. The software replaces physical stickers with an interactive GUI that displays a Golden Board CAD view alongside actual 2D/3D defect images. When an operator scans a board, the system accurately pinpoints the exact defect location and automatically logs the repair feedback.
3. The Spec: This digitises 100% of defect mapping, replacing manual stickers. The system strictly logs specific customisable repair actions (e.g., Solder Retouched, Replaced Component, Realigned Component) to ensure perfect traceability.
1. The Problem: In Automotive, Aerospace, and Medical sectors, manufacturers must prove to auditors that every single critical component was inspected. Relying on isolated machine reports makes it difficult to guarantee there are no blind spots between SPI, AOI, and AXI.
2. The Science: The solution is a centralised Test Coverage Report. This plugin aggregates inspection data from the entire line to map exactly which technologies tested which parts. It proves that solder volume was checked at SPI, placement and polarity at Pre-AOI, and hidden joints at AXI.
3. The Spec: The system provides a visual mapped coverage report across 3 distinct inspection gates (SPI, AOI, AXI) and offers automated Test Coverage Optimisation Suggestions to close any detected inspection gaps.
1. The Problem: Sometimes, an AOI operator mistakenly buys off (passes) a defect, or the AOI algorithm misses it. When this defective board reaches the AXI (X-ray) machine, the defect is finally caught, but the upstream AOI machine is never corrected, allowing the error to repeat.
2. The Science: Factories use Escapee Feedback loops. By linking the VDSPC databases of the AOI and AXI, the system cross-checks results. When AXI finds a defect that AOI passed, the system flags it as an "Escapee". This closed-loop communication ensures the AOI programmer is alerted to tighten the specific algorithm threshold.
3. The Spec: Escapee Feedback 1.0 automatically generates tickets and email notifications directly to the dashboard the moment an AXI failure correlates with an AOI pass, enabling immediate retraining of the vision model.
1. The Problem: Traditionally, verifying inspection results requires two separate software tools and stations—one for optical (AOI) review and another for X-ray (AXI) review. This duplicates labour and forces operators to juggle multiple interfaces.
2. The Science: This is resolved through a Centralised Second Buyoff within a unified New Platform VVTS (Visual Verification Tool Solution). A single software solution aggregates both optical 2D/3D images and X-ray slices into one centralised interface, allowing one operator to efficiently review and update buyoff results for the entire line.
3. The Spec: By replacing existing fragmented tools, factories consolidate their review process from 2 software tools down to 1 single platform, significantly reducing the number of verification operators needed.
1. The Problem: AXI machines rely on pneumatic components (outer barriers, inner barriers, clampers). If the facility's air pressure drops unexpectedly, these mechanical parts can fail to actuate properly, potentially causing physical damage to the boards or the machine.
2. The Science: Protection is achieved by integrating IoT Sensors (XLTM). External sensors monitor the incoming air pressure in real-time, sending data to a V-ONE Mars gateway. The system establishes a baseline and utilises rules to trigger immediate action if the pressure drops dangerously low.
3. The Spec: The system continuously monitors pressure levels against a set safety threshold (e.g., 4 Bar minimum). If pressure drops below this limit, it automatically triggers a warning and can execute an automated machine stop to ensure safe operation.
1. The Problem: High-resolution 3D images and inspection data from SPI, AOI, and AXI consume massive amounts of server space. Without proper management, active databases become bloated and slow, or older data is deleted, causing compliance failures during customer audits.
2. The Science: Factories utilise a Long-Term Data Backup Solution. Rather than keeping all data on the active server, the software automatically packs and shifts historical file, CAD, image, and result data to an external Storage Database. When historical data is needed for an audit, users can selectively restore specific timeframes back to the active viewer.
3. The Spec: This architecture successfully manages an estimated 20 TB of storage by archiving files based on strict [year][month][date] time configurations, freeing up active database memory without permanently losing data.
1. The Problem: Calculating Overall Equipment Effectiveness (OEE) manually is inaccurate because micro-stoppages are rarely recorded. Management struggles to differentiate whether a machine is idle because it is waiting for a board (Starvation) or blocked by downstream traffic.
2. The Science: Machine Status Monitoring connects directly to the machine's internal controller to extract precise Machine Utilisation (MU) states. The system categorises every second of operation into productive time or specific unproductive states (e.g., Upstream Idling, Downstream Block, Software Off).
3. The Spec: The system calculates real-time OEE by tracking actual metrics down to the second—such as tracking an 80-second idle period (0.10%) or calculating a precise 90.72% Overall OEE based on Availability, Performance, and Yield Quality.
1. The Problem: Suboptimal solder paste printing—such as paste drying, clogging, or uneven application—frequently leads to massive downstream reflow defects like tombstoning. By the time these defects are caught post-reflow, the printer has already ruined dozens of boards.
2. The Science: Through Process Insight 2.0, the system leverages 3rd Party Printer Adaptors to correlate SPI measurement failures (like insufficient paste) directly with Printer data. When a trend is detected, the system can recommend physical checks (like humidity control) or trigger "Auto Healing" machine-to-machine commands.
3. The Spec: By analysing specific failure modes (e.g., excessive volume, uneven application), the software prescribes exact corrective actions, such as triggering an automatic regular stencil cleaning cycle at the printer before tombstoning can occur.
1. The Problem: Over time, the optical cameras inside AOI and AXI machines experience slight physical or lighting degradation. If the camera scaling or resolution drifts, the machine will start failing good boards or passing bad ones, destroying measurement accuracy.
2. The Science: This is prevented via Vision Module Hardware Monitoring. The software continuously tracks the Camera Resolution Trend and Scaling Data. By mapping this calibration data onto historical charts, engineers can spot deviations in the optical system before the drift impacts production.
3. The Spec: The system tracks extreme microscopic tolerances, validating that camera resolution remains stable (e.g., precisely 14.99 µm/pixel) and ensuring the Camera Skew Angle does not exceed strict limits like 0 ± 0.003 rad.
1. The Problem: In a Surface Mount Technology (SMT) line, different inspection machines—such as Solder Paste Inspection (SPI), Pre-Reflow AOI, Post-Reflow AOI, and Advanced X-ray Inspection (AXI)—often use different sequence setups and component ID naming conventions. For example, one machine might label a component as "1:C04" while another labels it "1_C04". This discrepancy breaks the digital thread, making it highly difficult for engineers to correlate defects across platforms automatically.
2. The Science: Seamless traceability is achieved through intelligent Component Matching algorithms. Instead of forcing engineers to reprogramme every machine to share a rigid naming structure, advanced smart manufacturing systems use the Board Barcode as the primary anchor. The software then applies symbolic normalisation to the Reference Designators (Ref Des), standardising the variations in the background. This maps the data seamlessly without requiring engineers to modify the original programme panel arrangement.
3. The Spec: Advanced cross-platform algorithms, such as ONE View Components Matching 2.0, normalise variations (e.g., treating "1:C04", "1_C04", and "1-C04" as identical). This enables automated, 100% barcode matching across SPI, AOI, and AXI systems, ensuring the digital thread remains intact regardless of differing machine setups.
1. The Problem: Advanced X-Ray Inspection (AXI) machines rely on sensitive, high-value components like X-Ray tubes, Y-axis motors, and high-resolution cameras. Incoming power anomalies, temperature spikes inside the tube chamber, or drops in pneumatic air pressure can lead to degraded image quality or unexpected, expensive hardware failure.
2. The Science: Factories mitigate this risk by integrating smart Industrial IoT (IIoT) sensors directly into the equipment environment. These sensors continuously measure 3-phase incoming power, ambient chamber temperatures, and air regulator pressures. Machine learning anomaly detectors (such as SVM-based or LSTM-based algorithms) analyse this telemetry in real-time. If the algorithms detect a deviation from normal operating parameters—such as a temperature rise in the X-Ray tube surface or Y-axis motor—the system automatically triggers a warning or adjusts cooling mechanisms (like fan speeds) to stabilise the hardware before damage occurs.
3. The Spec: Comprehensive IoT monitoring tracks real-time data across 14 distinct camera conditions, X-ray tube cathode levels, and usage hours. It acts dynamically on specific thresholds, such as warning the user when pneumatic air pressure drops below a minimum threshold, or triggering an automated machine stop if the pressure drops too low to ensure safe operation.
1. The Problem: In high-speed manufacturing, process parameters can drift subtly. Issues like solder paste drying out or stencil clogging can suddenly increase the defect rate. Traditional workflows require operators to manually filter data, analyse the trends, and guess the root cause. This manual process often leads to "Consecutive Defects Unattended," where hundreds of defective boards are produced before the true issue is isolated and resolved.
2. The Science: To shift from reactive guessing to proactive resolution, modern factories deploy AI-Assisted Defect Analysis. By continuously aggregating data across the entire line, AI models monitor for cycle time abnormalities, machine behaviour deviations, and spikes in true or false calls. Instead of simply flagging that a board has failed, the AI analyses the broader defect pattern to deduce the root cause, providing a confident, actionable diagnosis directly to the engineer.
3. The Spec: AI analytics evaluate aggregate data to catch yield loss before it explodes. For example, the system can automatically detect that "Solder bridge defects spiked 40% on Line 2 in the last 2 hours" and subsequently issue a direct recommendation to "Check stencil cleaning," effectively bypassing hours of manual investigation.
1. The Problem: Implementing factory-wide intelligence platforms often involves complex backend configurations, extensive manual setup procedures, and deploying massive server images. This heavy IT footprint traditionally leads to human error during setup, extended deployment times, and excessive server storage requirements.
2. The Science: Next-generation smart manufacturing platforms utilise automated installers and advanced containerisation to streamline deployment. These systems run automatic System Prerequisites Scans before installation begins. By optimising the database structure and image processing servers, the software footprint is drastically compressed. This enables "command-free" Linux deployments and automatic self-recovery mechanisms (like auto-restarts) if backend services experience downtime.
3. The Spec: Automated deployment technologies optimise server loads, reducing the required OVA (Open Virtual Appliance) file size down to just 13 GB (a 35% or 7 GB reduction in size). Furthermore, the automated installer handles 95% of configuration settings autonomously, eliminating the need for engineers to manually execute up to 50 pages of complex Work Instruction (WI) setup procedures.
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