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ReliVision is hardware and image-modality agnostic and applicable across different use cases,
enabling quality automation and smart manufacturing without writing a single line of code.
Detection
accuracy
24/7/365
shop floor availability
Same day
remote support
Process drift
prevention
Digitizing quality assurance and visual inspections with ReliVision
Quality control in server assembly across server types and components with unprecedented detection accuracy.
Hardware: Relimetrics Robotic MVS.
Type of Inspection: Discrete.
Read more: LINK to whitepaper.
Inspection of cosmetics defects (e.g. dents, scratches, bumps) on objects moving on a conveyor belt.
Hardware: Relimetrics Static MVS.
Type of Inspection: Discrete.
Read more: LINK to whitepaper.
Inspections of flat surfaces (e.g. ceramic tiles, electroplated copper, silicone adhesive) with advanced line scanning technology.
Hardware: Relimetrics Scanning MVS.
Type of Inspection: Continuous.
Inspections of panel alignment, thickness, and surface cavities and correlating this with machine and process data to increase process efficiency.
Hardware: Static Conventional Cameras.
Type of Inspection: Continuous.
Inspections of cosmetic anomalies (e.g. wrinkles, dirt stains, burn trim) on car seats per OEM thresholds.
Hardware: Relimetrics Robotic MVS.
Type of Inspection: Discrete.
Cosmetic inspection of vehicle surface while it passes through the light tunnel.
Hardware: Static Conventional Cameras.
Type of Inspection: Discrete.
Digitizing inspection of surface cosmetic defects on manifolds, ducts, cylinder head covers and correlating this information with machine and process data to increase manufacturing efficiency and decrease costs.
Hardware: 3D scanner.
Type of Inspection: Discrete.
Sorting and handling of returned articles to have full traceability of quality before making decisions on article repair, reuse, and customer billing.
Hardware: iPhone.
Type of Inspection: Lot Based.
Inspection of automotive modules and checking assembly errors before the assembled parts are shipped to the OEM.
Hardware: Static Conventional Cameras.
Type of Inspection: Discrete.
Digitizing x-ray image analysis to detect prohibited objects in security applications, avoiding human errors with high detection accuracy.
Hardware: Industrial X-ray Machine.
Type of Inspection: NDT.
Digitizing reading of anomalies on industrial PAUT NDT data, boosting detection accuracy and reducing inspection time.
Hardware: Industrial PAUT Machine.
Type of Inspection: NDT.
Digitizing identification of different work objects (e.g., fasteners) and their health diagnostics (e.g., sealant squeeze-out).
Hardware: Conventional camera on a robotic arm.
Type of Inspection: Discrete.
ReliVision uses machine learning to achieve a high level of flexibility and scalability for computer vision systems.
As part of its industry 4.0 transformation, our customer is implementing Relimetrics' AI accelerated machine vision technology at Foxconn to automate quality inspection of server assembly.
Our customer's portfolio of server products is highly complex and customized.
The assembly of different server types must be inspected in-line near real time. This problem is well suited for Relimetrics' AI-based machine vision software. For more details visit our insight.tech article.
Relimetrics Machine Vision System, aka "Static MVS", can be used across industries for cosmetic anomaly inspections as the products move on a conveyor belt. The Static MVS has modular design and offers customers the ability to also integrate a line scanner for surface inspections in addition to static cameras.
Lockheed Martin has worked together with Relimetrics to automate aircraft manufacturing and assembly processes with Relimetrics' AI-based machine vision enabled robotics technology.
ReliVision is improving manufacturing efficiency by making work-object identification and inspections easier, smarter and unprecedentedly more accurate.
Our customer is a global Tier I supplier of car seats. Meeting OEM standards for aesthetic quality requires a thorough quality inspection before seats are shipped to be installed in vehicles. Improving quality audit standards and creating full traceability of quality are key goals of its digitization strategy.
At e.GO Mobile, Relimetrics implemented a very intuitive, flexible and efficient solution to capture product quality issues with Relimetrics‘ AI-based machine vision software, ReliVision, in partnership with HPE and PTC.
Our customer is a global Tier I automotive supplier of modular and assembled automotive parts. To keep OEM customers satisfied and prevent warranty claims, our customer is focusing on digitizing and improving product quality assurance.
Our customer provides automotive OEMs with high performance components with plastic parts, such as manifolds, ducts and cylinder head covers. As an early adopter of Industry 4.0 technology, it set out on a robust digitization journey in an effort to increase manufacturing efficiency and decrease costs.
Our customer is a leading global supplier of formwork and scaffolding products in the construction industry. It is digitizing sorting and handling of returned articles in its rental businesses to have full traceability of quality before making decisions on article repair, reuse, and customer billing.
Lattonedil partnered with Relimetrics to develop a solution for digitizing the inspection process of polymer panels. By using ReliVision, Lattonedil can now inspect various quality parameters, such as panel alignment, thickness, and surface anomalies. This advanced inspection capability allows them to detect defects that may have gone unnoticed and take corrective actions promptly. The integration of machine, process, and quality data also enables Lattonedil to gain new insights and adopt a proactive approach to quality assurance.
Select an application
As part of its industry 4.0 transformation, our customer is implementing Relimetrics' AI accelerated machine vision technology at Foxconn to automate quality inspection of server assembly.
Our customer's portfolio of server products is highly complex and customized. The assembly of different server types must be inspected in-line near real time. This problem is well suited for Relimetrics' AI-based machine vision software.
For more details visit our insight.tech article.
Increases detection accuracy
99.9%+
Detection accuracy
Reduces assembly audit time
70%
30-40 seconds per assembly audit
Reduces final testing and rework
80%
Boosts performance
4.2σ
Guaranteed
ROI Impact
$500,000
Savings per line per year
"Relimetrics solution is embedded within our assembly line and enables immediate feedback to the operators and line managers to address any occurring issues right away. The fact that Reli-QA has no fatigue-induced errors and handles bespoke tolerances drove a 2% point increase in the initial pass rate. This is significant as it moved the performance from 2.1σ to 4.2σ."
Relimetrics Machine Vision System, aka "Static MVS", can be used across industries for cosmetic anomaly inspections as the products move on a conveyor belt. The Static MVS has modular design and offers customers the ability to also integrate a line scanner for surface inspections in addition to static cameras.
With increased customization experienced today in the electronics industry, neither existing QA staff nor QA solutions are able to keep up with the high production variability on the shop floor.
Detection of defects is not sufficiently accurate leading to high rework & scrap costs, downtime and customer complaints.
Furthermore, manual QA intervention is required to weed out false detections [1-10% across different use cases] by existing QA automation solutions today and manually reconfigure algorithms to inspect new product configurations.
Relimetrics' Static MVS provides customers with a modular QA system they can quickly deploy on their shop floors to perform highly reliable inspections in high production variability manufacturing environments with guaranteed detection accuracy.
In this example, the Static MVS is scanning Cisco routers and inspecting a variety of defects including scratches, surface anomalies, label and decal misalignment as well as missing screws. The system is also capable of perfoming OCR, read barcode and serial numbers to identify differrent work objects.
With incorporation of AI, the Static MVS performs QA to a greater degree of accuracy than humans performing often tedious work and quickly adapt to new circumstances and tolerance levels.
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Lockheed Martin is working together with Relimetrics to automate aircraft manufacturing and assembly processes with Relimetrics' AI-based machine vision enabled robotics technology. ReliVision is improving manufacturing efficiency by making work-object identification and inspections easier, smarter and unprecedentedly more accurate.
Hunter Markussen
Application Engineer - Manufacturing Technology
Lockheed Martin Aeronautics
"Relimetrics is working with Lockheed Martin Aeronautics to develop AI-based machine vision enabled robotics for automating aircraft manufacturing and assembly processes. This innovation in automatic work-object identification allows the system to manage greater degrees of complexity than what is capable with traditional machine vision. Like Lockheed Martin’s other investments in advanced production technologies, the goal is speeding production and improving aircraft quality.”
Our customer is a global Tier I supplier of car seats. Meeting OEM standards for aesthetic quality requires a thorough quality inspection before seats are shipped to be installed in vehicles. Improving quality audit standards and creating full traceability of quality are key goals of its digitization strategy.
Today, quality inspections of car seats are done manually by operators, whose inspection is prone to error due to fatigue. Wrinkles and other car seat defects that are undetected or detected late lead to higher production costs due to rework and recalls.
There is high cost associated with each, and with increasing demand for quality, it is imperative for our customer to create full traceability of quality in its assembly to reduce costs and improve quality.
Relimetrics offers an industrial grade, shop floor ready system consisting of both hardware and software to digitize the quality of car seats.
Data acquired by the Relimetrics system are processed at the edge, providing evaluation of quality issues, such as wrinkles or other defects.
10s
Inspection time
for a whole seat
>99.9%
Probability of detection
20
# of defect
types detected
At e.GO Mobile, Relimetrics implemented a very intuitive, flexible and efficient solution to capture product quality issues with Relimetrics‘ AI-based machine vision software, ReliVision, in partnership with HPE and PTC.
Quality inspections are becoming increasingly more complex in the automotive industry, given today’s accelerated production schedules and the proliferation of options and extras.
For an automotive OEM like e.GO, it is critical to adopt a quality digitization solution that is able to adapt to high production variability on the shop floor with high detection accuracy, and that does not rely on proprietary hardware enabling any industrial grade camera whether smart or conventional to be leveraged.
At e.GO, Relimetrics implemented a very intuitive and efficient solution to capture end-of-line quality issues using Relimetrics‘ AI-based machine vision software (ReliVision) in partnership with HPE and PTC.
ReliVision leverages existing industrial grade camera hardware, providing a flexible and scalable image inspection solution for anyone without any coding expertise to perform AI based machine vision and quickly deploy trained models in line for real time inspection on the shop floor and scale across inspection sites.
10-30%
Reduction
in downtime
25%
Reduction
in scrap & rework
€5.3m
Reduction
in scrap & rework
Our customer is a global Tier I automotive supplier of modular and assembled automotive parts. To keep OEM customers satisfied and prevent warranty claims, our customer is focusing on digitizing and improving product quality assurance.
Customer has already invested in embedded computer vision systems to digitize quality audit of drive shaft modules in its production line.
The return on investment has not been realized due to the deployed systems failing to deliver the required probability of detection (POD) and generating high percentage of false alarms.
As the module directly impacts vehicle safety, false detection is a significant liability.
Relimetrics augmented the existing camera hardware previously deployed at the customer, boosting the POD with its machine learning approach at the edge.
This solution enhanced and unlocked much more value from the previous investment.
It also fully integrated with the factory Manufacturing Execution System (MES), allowing our customer to use a single interface while using Relimetrics.
<.5s
Near real-time
inspection
>99.9%
Increased existing PoD from
97% to 99.9%
Full
traceability
Improved handling
of quality issues
Our customer provides automotive OEMs with high performance components with plastic parts, such as manifolds, ducts and cylinder head covers. As an early adopter of Industry 4.0 technology, it set out on a robust digitization journey in an effort to increase manufacturing efficiency and decrease costs.
Customer uses blow molding process to manufacture automotive filter components. Produced parts have to meet stringent customer quality demands and standards to avoid costly recalls.
Today, quality inspections are done manually by operators. Defective parts are either recycled or discarded. Recycling can lead to contamination, and discarding is costly and wasteful.
Process control is the main challenge. Currently, blow molding technicians adjust machine parameters to optimize production efficiency within quality specifications. Yet, quality drifts may go unnoticed for days.
With increasing quality demands, customer sought to make automated quality audit and process control an integral part of the production process.
Relimetrics deployed a fully-integrated system that digitized visual inspections of manufactured components. The system:
■ Inspects the dimensions and surface of every manufactured part and assures quality is within specifications
■ Correlates digitized quality data with machine and process data to optimize blow molding process
■ Identifies quality drifts in real-time and provides recommendations on how to adjust process parameters for optimized production, increasing productivity and throughput of a production line
50%
Cost decrease related
to quality audit
>99.9%
Probability of detection
Closes
the loop
Real-time process
control feedback
Our customer is a leading global supplier of formwork and scaffolding products in the construction industry. It is digitizing sorting and handling of returned articles in its rental businesses to have full traceability of quality before making decisions on article repair, reuse, and customer billing.
Our customer relies on manual sorting and inspection of articles returned to its rental sites and lacks ways to objectively assess damages. Damages caused by the renters are often overlooked, and renters are not charged for the incurred damage. Increasing number of articles in our customer's portfolio increases labor time and complexity, leading to mistakes in sorting and counting. This is amplified further with differences in contract terms and conditions with the renters.
Relimetrics provides a flexible and scalable image inspection solution, which can be managed directly by customers, for object recognition, counting and damage assessment. Interface modules enable connection to MES, ERP systems, enabling a closed loop inventory management process to be delivered to any production or rental facility. Relimetrics solution is hardware agnostic and can leverage existing hardware resources, allowing our customers to extract more value from their investment.
Full traceability
of defects
More accurate way to
determine root causes
>99.9%
Probability of detection
Significant
time reduction
Significant reduction
in inspection time
Covestro is a leading global supplier of innovative polymers used in nearly every part of modern life across industries and products. It is helping its own customers to innovate and create better products by designing better performing materials and modernizing manufacturing.
Strict quality demands in the construction industry puts high pressure on panel suppliers to deliver quality assured products.
Visual inspections are slow, subjective and often prone to errors, making defects undetectable until panels show signs of tear and wear while in use. Extraction of defective panels is costly, and expose the panel suppliers to fines and other liabilities.
Identifying the root cause of defects poses an additional challenge due to lack of understanding of correlation between machine, process and quality parameters.
Relimetrics solution digitizes quality inspection of polymer panels using ReliVision and provides a full overview of quality before the products leave the shop floor.
The system can be used to inspect a variety of quality parameters in the manufactured panels, including panel alignment, thickness, and surface anomalies, such as cavities and voids.
The deployed system also integrates with the shop floor MES, and offers the customer additional value metrics to improve its manufacturing and quickly detect quality drifts.
For more details, visit our Insight.tech article.
1s
Inspection time
for a whole panel
>99.9%
Probability of detection
Full
traceability
More accurate way to
determine root cause of defects
Relimetrics' Non-Destructive Testing (NDT) Module, RELI-NDT, digitizes visual inspections of defects in phased array ultrasonic testing (PAUT) as well as x-ray and infrared thermography inspections. Customers can define their specific requirements and train algorithms with well defined deep learning recipes without writing a single line of code.
There is a plethora of possibilities for things to go wrong in logistics operations and supply chains across industries. Take, for example, the wind energy industry. According to National Renewable Energy Laboratory, blade failure is one of the most common failure events in wind turbines, resulting in costly repairs and lost revenue.
The blades of a wind turbine must be able to withstand high winds in the field. As a result, the inspection of each blade, before getting installed on the field is a delicate process that requires the utmost precision. This inspection produces an enormous amount of data that needs to be evaluated to detect defects.
Today, the inspection of PAUT data is carried out by visual inspection, which is a time consuming and labor-intensive process prone to error due to fatigue.
Relimetrics’ AI accelerated NDT module, RELI-NDT, is designed to overcome the drawbacks of human visual inspection in PAUT, x-ray and infrared thermography inspections.
With RELI-NDT, customers can rapidly implement well defined AI algorithms to digitize visual inspection of PAUT data to inspect blade defects and their size. RELI-NDT can be configured to meet the specific requirements of ultrasonic inspection of any customer. A good example is renewable energy leader Siemens Gamesa, which engaged with Relimetrics to inspect its wind turbine blades with RELI-NDT.
Depending on the inspection requirements, RELI-NDT can perform inspection of adhesive joints on a single wind-turbine blade in 45 min. This is a major reduction in inspection time considering that the manual inspection consumes an average of 7 hours to complete a full blade inspection and requires 100% of the attention of the inspector during the shift.
Designed for high production variability manufacturing environments with a wide variety of configurations, RELI-NDT takes inspections of NDT data to a new level, providing a greater degree of accuracy than humans performing the tedious work.
RELI-NDT outperforms conventional inspection products relying on rule-based algorithms with its proprietary AI stack. Inspection algorithm scan be re-trained quickly to adapt to new circumstances and tolerance levels.
RELI-NDT provides well defined deep learning recipes, enabling anyone to train deep learning models without any AI or coding expertise.
RELI-NDT is also rapidly scalable within a manufacturer’s ecosystem: trained models and configurations can be managed with a centralized data management interface and shared across plants in the cloud.
Relimetrics’ Non-Destructive Testing (NDT) Module, RELI-NDT, digitizes visual inspections of defects in X-ray inspections as well as infrared thermography and phased array ultrasonic testing (PAUT). Customers can define their specific requirements and train algorithms with well defined deep learning recipes without writing a single line of code.
Radiographic testing is one of the most commonly used NDT methods to ensure reliability and safety of industrial products. However, today, the inspection of RT images is still mostly carried out by visual inspections as X-ray images often have poor contrast and are noisy, making it difficult to visualize anomalies.
Conventional radiographic testing based on human visual inspection is time consuming, labor intensive and prone to errors due to reading fatigue. This makes the reliable detection of defects one of the most challenging tasks in NDT. To overcome the drawbacks of human visual inspection, our customer seeks to digitize the inspection of X-ray images by leveraging advances in computer vision and AI.
With ReliVision, customers can rapidly implement well defined AI algorithms to digitize visual inspections of defects in X-ray data. ReliVision can be configured to meet the specific requirements of X-ray inspection of any customer. A good example is Asia’s biggest energy company Eneos, which engaged with Relimetrics to inspect anomalies in X-ray images with ReliVision.
Relimetrics provides a very intuitive and efficient solution to detect shades on X-ray images with a high detection probability under 1 second per image.
Using ReliVision, the defect inspection of X-ray data is automated, assessing the internal structure of manufactured components quality before they leave the shop floor.
Relimetrics solution serves as a standardized quality inspection automation platform that can be used across plants and ingest images from a variety of sources.
>95%
Probability of Detection
1sec
Inspection Time per Image
>67%
Positive Predictive Value
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