#VisionAutomation

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HOW CAN FOOD & BEVERAGE SEGMENT BENEFIT FROM MACHINE VISION INSPECTION

Machine vision technology has been making critical advances in the food and beverage sector. For quite a while, this industry has been a client of machine vision gear. The business has been generally viewed as an early adopter of the most recent innovation. Today, an expanding requirement for effectiveness, quality, and administrative consistence is driving much more machine vision innovation selection in the space.

Read More: https://bit.ly/2TcEMB1

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Industrial Automation Companies in Bangalore

          We Specialize In Industrial Vision-Based Solutions

                                Using Artificial Intelligence

          Vision Automation and Robotic Solution

                      Automated Vision Inspection

         Industrial Automation Companies in Bangalore

Man-made brainpower is set to upset the Machine Vision industry. Here are Qualitas, we've set out on this excursion about 10 years prior. With our mastery in conveying great over a 100 mechanical machine vision organizations the whole way across the globe, we comprehend modern computerization. We convey gigantic incentive to use the intensity of Machine Vision and AI for our clients. Our clients likewise esteem key experiences that can be gotten from these frameworks so they can improve their assembling forms, while giving them full oversight over framework upkeep and execution simultaneously

Qualitas Technologies

was established in 2008 in Redmond, WA (USA), and later headquartered to Bangalore, India.

Cloud Vision System

The Qualitas EagleEye is the most recent item to be created by Qualitas Technologies. The EagleEye accompanies a completely adaptable and particular picture obtaining and handling unit. The picture obtaining unit, with its adaptable mounting arm, worked in camera, and adjustable brightening framework is completely prepared to catch the most clear picture for your particular necessity. With it's cloud based Deep Learning preparing module, you don't have to put resources into costly AI preparing equipment and you can understand the most testing applications at a small amount of the time and cost than current techniques and items.

Click here for more Details : https://bit.ly/3fMER8L

[machinevision]

Cloud Vision System company in Bangalore

Cloud Vision System company in Bangalore

Man-made reasoning is set to agitate the Machine Vision industry. Here are Qualitas, we've departed on this outing around 10 years sooner. With our ability in ignoring on extraordinarily a 100 present day machine vision affiliations the whole way over the globe, we comprehend mechanical computerization. We give enormous helper to use the intensity of Machine Vision and AI for our clients. Our clients in like way respect key bits of data that can be gotten from these frameworks so they can improve their gathering structures, while giving them unlimited oversight over structure upkeep and execution at the same time. Qualitas Technologies was developed in 2008 in Redmond, WA (USA), and later headquartered to Bangalore, India. 

Electronic reasoning is set to disturb the Machine Vision industry. Here are Qualitas, we've set out on this excursion practically 10 years sooner. With our ability in ignoring on remarkably a 100 present day machine vision designs the whole course over the globe, we acknowledge mechanical robotization. We give gigantic impulse to use the power of Machine Vision and AI for our clients. Our clients in like way respect key bits of data that can be gotten from these frameworks so they can improve their storing up structures, while giving them full oversight over framework support and execution simultaneously Qualitas Technologies was developed in 2008 in Redmond, WA (USA), and later headquartered to Bangalore, India. 

EagleEyeInspection System 

Completely incorporated (camera to cloud) vision framework for mechanical mechanization 

Cloud Vision System 

The Qualitas EagleEye is the most recent item to be created by Qualitas Technologies. The EagleEye accompanies a completely adjustable and secluded picture procurement and handling unit. The picture securing unit, with its adaptable mounting arm, worked in camera, and adjustable light framework is completely prepared to catch the most clear picture for your particular necessity. With it's cloud based Deep Learning preparing module, you don't have to put resources into costly AI preparing equipment and you can tackle the most testing applications at a small amount of the time and cost than current strategies and items.

Read more: https://bit.ly/3fMER8L

[machinevision]

Automated Vision Inspection Company in Bangalore

Automated Vision Inspection Company in Bangalore

Artificial Intelligence is set to disrupt the Machine Vision industry. Here are Qualitas, we’ve embarked on this journey nearly a decade ago. With our expertise in deploying well over a 100 industrial machine vision deployments all across the globe, we understand industrial automation. We deliver immense value to leverage the power of Machine Vision and AI for our customers. Our customers also value key insights that can be derived from these systems so they can improve their manufacturing processes, while giving them complete control over system maintenance and performance at the same timeQualitas Technologies was founded in 2008 in Redmond, WA (USA), and later headquartered to Bangalore, India.

Vision Inspection System Manufacturers

Our SolutionsCombining best in class industry standards and technologies to integrate it to a perfect solution that fits your needs.BearingInspecWeb InspectionParts CountingRobotic GuidanceOptical Character RecognitionSurface Anamoloy InspectionInventory TrackingAssembly Verification

Click here to know more

: https://bit.ly/3fMER8L

[surfaceinspection]

Man-made reasoning is set to upset the Machine Vision industry. Here are Qualitas, we've set out on this excursion almost 10 years back. With our skill in sending admirably over a 100 modern machine vision arrangements the whole way across the globe, we comprehend mechanical computerization. We convey massive incentive to use the intensity of Machine Vision and AI for our clients. Our clients additionally esteem key bits of knowledge that can be gotten from these frameworks so they can improve their assembling measures, while giving them unlimited oversight over framework support and execution simultaneously Vision Automation and Robotic Solution

[machinevision]

Learning Approaches

Machine Learning, an exciting branch of Artificial Intelligence, is all around us in this modern world. Machine Learning brings out the power of data in a new way, like Facebook suggesting the stories in your feed and Amazon recommending your products. Machine Learning enables computer systems to learn and improve from experience continuously as it is primarily developed with the ability to access data and perform tasks automatically through predictions and detections.

Machine Learning is one of the core sub-areas of Artificial Intelligence. ML applications learn from experience and data like humans without explicit programming for the same. When exposed to newer and newer data, these algorithms learn, evolve, transform, and develop all by themselves. In other words, with Machine Learning, computers find insightful information without the need to tell them where to find it. Instead, they achieve this by leveraging algorithms that learn from continuous data in a process that is iterative.

Related Article: ARTIFICIAL INTELLIGENCE (AI) VS MACHINE LEARNING (ML) VS DEEP LEARNING (DL)

There are only a few main learning styles or learning models that a machine learning algorithm can have:

1. SUPERVISED LEARNING

In supervised learning, input data is known as the training data. It has a known label or result such as spam/not-spam or a stock price at a time. A model is developed through training in which it is required to make predictions and is corrected when those predictions are inaccurate. The training process is reiterated until the model achieves the desired level of accuracy on the training data.

Example problems are classification and regression. Logistic Regression and the Back Propagation Neural Network are some popular examples of supervised learning algorithms.  

2. UNSUPERVISED LEARNING

In unsupervised learning, input data is not labeled and does not have a known result. A model is developed by deducing structures evident in the input data. This is generally done to extract general rules and may be carried out through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems in unsupervised learning are clustering, dimensionality reduction, and association rule learning. The Apriori algorithm and K-Means algorithms are some of the popular unsupervised learning algorithms.

3. CLUSTERING

Clustering algorithms are used to describe the class of problem and the class of methods. Clustering methods are usually organized by the modeling approaches namely centroid-based and hierarchal. All methods are concerned with using the evident patterns in the data to best organize the data into groups of maximum similarity.

The most popular clustering algorithms are:

k-Means

k-Medians

Expectation Maximisation (EM)

Hierarchical Clustering

Also Read: Differences Between Machine Learning and Rule-Based Systems

4. ASSOCIATION

Association rule learning algorithms are used to extract rules that best explain observed relationships between variables in a dataset. These rules can prove to be useful in discovering important and commercially useful associations in large multi-dimensional datasets that can be exploited by an organization to increase profitability.

The most popular association rule learning algorithms are:

Apriori algorithm

Eclat algorithm

5. SEMI-SUPERVISED LEARNING

In semi-supervised learning, input data is a mixture of labeled and unlabelled features. There is a desired prediction problem. However, the model must learn the structures to organize the data as well as make predictions accurately.

Example problems include classification and regression. Most semi-supervised algorithms are extensions to other flexible methods that make assumptions about how to model the unlabelled data.

6. GAN (GENERATIVE ADVERSARIAL NETWORKS)

The focus for GAN (Generative Adversarial Networks) is to generate data from scratch, mostly images but other domains including music have been done. However, the scope of application is far bigger than this. In reinforcement learning, it helps a robot to learn much faster.

Also Read: MACHINE VISION PROCESS FLOW

7. REINFORCEMENT LEARNING

Reinforcement learning is a specialized subfield of machine learning, which is known as approximate dynamic programming or neuro-dynamic programming. Learning concerned with agents who take some sort of action in an environment to maximize the cumulative reward. In reinforcement learning the environment is represented in Markov Decision Process (MDP). The learning algorithm tries to target the large MDPs where the model is infeasible by not assuming predetermined knowledge of a mathematical model.

Reinforcement learning can be understood with a simple example of a child in a living room. A child sees a fireplace and tries to approach that fire. It is warm, it is positive, the child feels good (positive reward +1). The child understands that fire is a positive thing. However, when the child tries to touch that fire, it burns the child’s hand (negative reward -1). The child just understood that fire is a good thing at a sufficient distance, but not too close. That is how reinforcement learning learns from some set of actions.

Read More: https://bit.ly/33Ucm3Q

[machinevision]

Machine Learning, an exciting branch of Artificial Intelligence, is all around us in this modern world. Machine Learning brings out the power of data in a new way, like Facebook suggesting the stories in your feed and Amazon recommending your products. Machine Learning enables computer systems to learn and improve from experience continuously as it is primarily developed with the ability to access data and perform tasks automatically through predictions and detections.

Read More: https://bit.ly/33Ucm3Q

[machinevision]

Machine Vision – Augment not replace Humans

What is Machine Vision (MV) ?

Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision is a term encompassing a large number of technologies, software and hardware products, integrated systems, actions, methods, and expertise.

Example 1

Piston Ring Counting – This machine is used to count piston rings and can count different models of rings ranging from a minimum thickness of 0.25mm.

(Picture credits – Qualitas Technologies)

Problems that are most likely to occur – To pack the stack of rings, the counting of the piston rings has to be done. And it is a tedious and time-consuming process. Also, the accuracy for lesser thickness can go down due to human errors.

Example 2

Gear teeth counting machine – This machine is used to count the number of teeth available on the machine gears and classify the gears based on the number

(Picture credits – Qualitas Technologies)

Problems that are most likely to occur – Counting the teeth of gears is highly essential because of it’s vital role in generating the required torque, but the diameter of the gears and patterns of the teeth varied over a wide range based on shape, teeth height, thickness etc and counting it is a challenging task.

Why Machine Vision?

While human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, machine vision systems use cameras and image processing software to perform similar inspections.  Machine Vision inspection plays an important role in achieving 100% quality control in manufacturing, reducing costs and ensuring a high level of customer satisfaction. Machine vision system inspection consists of narrowly defined tasks such as counting objects on a conveyor, reading serial numbers, and searching for surface defects. Manufacturers often prefer machine vision systems for visual inspections that require high speed, high magnification, around-the-clock operation, and/or repeatability of measurements.

Few other advantages of using Machine vision –

Accuracy – Today’s machine vision systems have a high degree of accuracy that can be achieved. With advances in learning as well as artificial intelligence you could actually build machines that can surpass human accuracy.

Reliability – This is another major advantage of Machine vision. Humans aren’t really designed for repetitive tasks. We are creative in nature. If you put a factory worker in assembly line and ask him to do the same thing over and over again for like 12 hours, he cannot be relied upon for giving accurate results. This won’t happen with Machine vision.

Inspection of the “invisible” – The human sight is limited to what’s in the visible spectrum. And that’s typically 400 to 700 nanometers. But with advanced multi spectral, hyper spectral imaging systems you could actually go beyond these ranges, see things which are not visible with the naked eye. Common applications of multi spectral imaging could be in food processing, health care, and pharmaceutical or even the military.                                                                                                                                                                                                                                      

Can it really replace humans?

Machine vision systems have made huge leaps in innovation in the past decade or two alone.  They’re used in everything from traffic and security cameras to food inspection and medical imaging – even the checkout counter at the grocery store uses a vision system!

When we look at each sub-component (ex: camera and Software), there’s no doubt that machines outperform humans.

Cameras

There are much faster cameras, they can reliably and with much higher precision capture images just not comparable to the human eye. HS and MS cameras can image scenes which are outside the visible spectral range.

Difference between human eye and cameraANGLE OF VIEW

With cameras, this is determined by the focal length of the lens (along with the sensor size of the camera). For example, a telephoto lens has a longer focal length than a standard portrait lens, and thus encompasses a narrower angle of view.                                                                                                        Unfortunately our eyes aren’t as straightforward. Although the human eye has a focal length of approximately 22 mm, this is misleading because

(i) the back of our eyes are curved,

(ii) the periphery of our visual field contains progressively less detail than the center, and

(iii) the scene we perceive is the combined result of both eyes.

RESOLUTION & DETAIL  

Most current digital cameras have 5-20 megapixels, which is often cited as falling far short of our own visual system. This is based on the fact that at 20/20 vision, the human eye is able to resolve the equivalent of a 52 megapixel camera (assuming a 60° angle of view).

However, such calculations are misleading. Only our central vision is 20/20, so we never actually resolve that much detail in a single glance. Away from the center, our visual ability decreases dramatically, such that by just 20° off-center our eyes resolve only one-tenth as much detail. At the periphery, we only detect large-scale contrast and minimal color.

Software

This is highly consistent for repetitive tasks and don’t fall prey to fatigue or boredom issues, etc. They are also consistent in decision making.For example, give 1000 images to a human at different days or times , the results will vary due to various factors and there is no consistency here. But the software will always give consistent results.Deep Learning is gaining much popularity due to its supremacy in terms of accuracy when trained with huge amounts of data.                                              Practically, Deep Learning is a subset of Machine Learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.For example,Language recognitionDeep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.Image caption generationAnother impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.However, When It Comes To The System As A Whole, The Human Capability Is Still Largely Superior.

Multi-tasking – Humans can work on multiple responsibilities unlike machine vision where in the time required to teach system on each and everything is considerably high.

Decision making – Humans have the ability to make decisions from their past experience. But, even the most advanced robots can hardly compete with a 6 years old kid.

Augment Not Replace!

AI over the next few years only automates tasks, within broader processes, that are currently handled exclusively by humans. Organizations will divide many of their critical processes into a series of smaller tasks and see where they can benefit the most from automation and which tasks need to remain with humans. The goal here won’t be to displace people but to use AI to augment existing processes.

Machine Vision Is Reactive In Nature. It Only Tells You When Something Is Wrong Or Has A Defect.

For example,Finding the defects on the surface of gun parts. As this is a special case of analyzing the surface defects due to the visibility of defects only under UV light, the image acquisition was done using a color camera and UV light in the factory condition. The defects were clearly visible and trained accordingly.

(Picture credits – Qualitas Technologies)

Machine Vision can be used to segregate sure defects and unsure defects. Only unsure defects can be re-verified by humans.

One such example is, usage of Machine vision in defect detection.

Machine vision is used to detect surface defects on the UBS line (Under-body sealant) which is hard to inspect continuously by a human. Hence, AI based Machine vision is used here to do the task effectively and when a defect is identified, human inter-vision is needed re-verify the detected defect and fix it. This way humans and Machine vision technology join hands which results in augmentation.

Manual quality control to sample the output of machine vision systems identify gaps and errors.

An ideal example for this would be,

Online reading of QR code and characters on Blisters which was soporific in nature and most importantly less accurate.

(Picture credits – Qualitas Technologies)

Read More:https://bit.ly/304BIeu

[machinevision]

Vision Automation and Robotic Solution

ARTIFICIAL INTELLIGENCE (AI) VS MACHINE LEARNING (ML) VS DEEP LEARNING (DL)

We Specialize In Industrial Vision-Based Solutions

Using Artificial Intelligence

Vision Automation and Robotic Solution

Automated Vision Inspection

Artificial Intelligence is set to disrupt the Machine Vision industry. Here are Qualitas, we’ve embarked on this journey nearly a decade ago. With our expertise in deploying well over a 100 industrial machine vision deployments all across the globe, we understand industrial automation. We deliver immense value to leverage the power of Machine Vision and AI for our customers. Our customers also value key insights that can be derived from these systems so they can improve their manufacturing processes, while giving them complete control over system maintenance and performance at the same time

Qualitas Technologies was founded in 2008 in Redmond, WA (USA), and later headquartered to Bangalore, India.

EagleEyeInspection System

Fully integrated (camera to cloud) vision system for industrial automation

Cloud Vision System

The Qualitas EagleEye is the latest product to be developed by Qualitas Technologies. The EagleEye comes with a fully customizable and modular image acquisition and processing unit. The image acquisition unit, with its flexible mounting arm, built in camera, and customizable illumination system is fully equipped to capture the clearest image for your specific requirement.

With it’s cloud based Deep Learning training module, you don’t need to invest in expensive AI training hardware and you can solve the most challenging applications at a fraction of the time and cost than current methods and products.

What We Do

Qualitas enables companies to automate visual processes in manufacturing. We don’t believe that humans can be replaced, but they need to be augmented with technology to realize the full benefit of automation.

Our expertise comes from more than a decade of experience in manufacturing and machine vision, starting from providing the best image acquisition design all the way to Deep Learning and AI software using the latest technology platforms and algorithms.

Read More:https://bit.ly/3fMER8L

[machinevision]

ARTIFICIAL INTELLIGENCE (AI) VS MACHINE LEARNING (ML) VS DEEP LEARNING (DL)

We Specialize In Industrial Vision-Based Solutions

Using Artificial Intelligence

Vision Automation and Robotic Solution

Automated Vision Inspection

Artificial Intelligence is set to disrupt the Machine Vision industry. Here are Qualitas, we’ve embarked on this journey nearly a decade ago. With our expertise in deploying well over a 100 industrial machine vision deployments all across the globe, we understand industrial automation.

Read More:https://bit.ly/3fMER8L

[machinevision]

Integrating Machine Vision & AI with Toyota Production System

Industrial Automation Solutions using Machine

Introduction to Toyota Production System

Toyota production system (TPS) is a lean manufacturing system, created by Taiichi Ohno. It focuses on the absolute elimination of waste, cost reduction, and producing high-quality products. TPS is implemented in industries for the following reasons:

It helps in monitoring quantity control to reduce costs by eliminating waste.

It enhances process and product quality.

Elements of Toyota production system

Just in time is a technique of supplying exactly the right quantity, at exactly the right time and at the exact location.

Jidoka is about building quality into the process. It uses tactics like poka-yoke, 5 whys, kaizens, and continuous improvement processes to improve quality. Machine vision and artificial intelligence solutions add value to jidoka tactics.

Machine Vision And Artificial Intelligence

Machine vision is the technology and methods used to provide image-based automatic inspection for industries. It uses a camera or multiple cameras to inspect and analyze objects automatically, usually in an industrial or production environment.

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Artificial intelligence supports the machine vision to make the right decision in the inspection.

Related Article: MACHINE VISION PROCESS FLOW

Using Jidoka tactics to integrate with machine vision and artificial intelligence

Here are the jidoka tactics that are poka-yoke, 5 whys, and Kaizens which is integrated with machine vision and artificial intelligence.

‘Poka-Yoke’s’ purpose is to achieve error proofing of processing activity and thereby make the process more robust. It is used in the inspection process to achieve a 100% inspection.  Machine vision and artificial intelligence can be used to ease the process of inspection as it is a non-contact technology and easy to integrate.  The speed being faster than manual labor is an added advantage. For example, identification of surface defects on doors by machine vision will reduce the outflow of defective parts to the next process, contributing to improved quality in the process within the industry. An average human would take about a minute to inspect, whereas a machine vision and artificial intelligence solution would inspect in just a few milliseconds, providing a high degree of accuracy.

‘5 Whys’ is an iterative interrogative technique used to explore the cause-effect relationships underlying a particular problem. The 5 whys technique is used to identify and get a clear understanding of the underlying problem. This enables us to provide customized solutions using machine vision and artificial intelligence to the clients that are accurate and consistent.

‘Kaizen’ is a Japanese word involving two concepts, KAI which means change and ZEN means better. Kaizen is the concept of improving a process with small continuous steps.  Kaizen is process-oriented. Improvisation of the process has to be done for the attainment of excellent results. It conglomerates innovation and continuous ongoing efforts in order to achieve continuous improvement and maintaining standards. Machine vision and artificial intelligence can be used as an innovative as well as practical solution to help industries continuously improve and maintain their standards by automating their inspection methodology and reduce their defect outflow.  This can be done by a four-step process, first is capturing images with the help of a specified camera. Second is solution development using annotation and deep learning algorithms. Next is the deployment of a solution for real-time inspection. Last is monitoring and fine-tune inspection accuracy. For example, ring counting is done with the help of a camera capturing rings using specialized lighting and our robust vision system validates the count and the equivalent result is shown in the display.  If the machine vision solution was not used, the ring counting inspection by humans would be inaccurate due to the small rings and thereby contributing to increasing the defects. Incorrect count of the rings would not be the right fit for the next part and would end up as scrap. Machine Vision and artificial intelligence solution benefit ring counting by improving its accuracy and at a much faster speed than humans.

Also Read: 3 Reasons for choosing Machine Vision in Manufacturing

Read More: https://bit.ly/35FW5C3