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Man-made consciousness is set to upset the Machine Vision industry. Here are Qualitas, we've set out on this excursion almost 10 years back. With our aptitude in sending great over a 100 mechanical machine vision arrangements Vision Automation the whole way across the globe, we comprehend modern mechanization. We convey enormous 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 measures, while giving them unlimited oversight over framework support and execution simultaneously Robotic Solution

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

Man-made brainpower is set to upset the Machine Vision industry. Here are Qualitas, we've set out on this excursion almost 10 years prior. With our skill in sending admirably over a 100 modern machine vision organizations the whole way across the globe, we comprehend mechanical mechanization. We convey massive 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 measures, while giving them unlimited oversight over framework upkeep and execution simultaneously Qualitas Technologies was established in 2008 in Redmond, WA (USA), and later settled to Bangalore, India. 

What We Do 

                                                Parts Counting

Man-made consciousness is set to upset the Machine Vision industry. Here are Qualitas, we've set out on this excursion almost 10 years back. With our aptitude in sending great over a 100 mechanical machine vision arrangements Vision Automation the whole way across the globe, we comprehend modern mechanization.

MachineVision R&D Consultancy

Automated Inspection with the help of Machine Vision and AI Technology

Industries all around the globe compete hard to deliver products of the highest quality standards. To maintain or improve competitive inspection standards the methodology of quality control must be made efficient. Automation is an essential part of this improvement process as dependence on humans or manual processes would weaken the quality control process. A product’s inspection is of utmost importance in mass production industries and is mostly done through visual inspection. In order to achieve a 100% inspection, the industries spend a lot on the inspection. Often random sampling is used for inspection. Visual inspection is commonly done in the following ways i.e manually, semi-automated, or fully automated. The current trend of quality inspection adopted by most of the manufacturing companies is manual inspection.

In a production environment, there are two key activities that go into any quality inspection

Material Handling – transporting the products manufactured along the manufacturing supply chain to the next set of value-adding processes/tasks

Inspection – Which is the activity of visually inspecting the product/process for identifying defects.

Related Article: How to Improve Quality in Pharma Industry using Machine Vision

Manual Inspection

This is the least automated way of performing the inspection. The products or parts to be inspected are manually picked up and inspection is being done to identify various cosmetic defects like scratches, dents, burr, and other defects visible to the human eye. The performance of manual inspection is generally inadequate and inconsistent.

Why is Manual Inspection inefficient?

Endless routine jobs

Slow

Inaccurate

Ergonomic constraints

Also Read: Machine Vision is creating a new wave in the Automobile Industry

Semi-Automated Inspection

Semi-Automated Inspection utilizes automation for material handling and the manual operation here though is the inspection activity. Though there is an improvement from the manual inspection processes described above there still has to be constant coordination between the automation system and manual operator performing the inspection. If the speeds are high or if the human misses apart, defects can be missed and thus passed out to customers.

Related Article: How Can Food & Beverage Segment Benefit From Machine Vision Inspection

Fully Automated Inspection

In a fully automated system, the material handling as well as the inspection activity is automated. Many technology companies including Qualitas technologies develop cutting edge systems to automate the visual inspection process. Providing benefits like speed accuracy and traceability in the inspection process. Coupled with automated material handling, these systems can be fully autonomous reducing human dependence thus eliminating the errors that come with manual operations. Of late, AI technology has made significant strides in the computational aspects as well providing added advantage which was not available with earlier technologies.

Several practical reasons for automated inspection include:

Matching high-speed production with high-speed inspection

Higher accuracy

Freeing humans from their monotonous job

Lower expenditure on human labor

Performing inspection in unfavorable environments

No dependency on highly skilled human inspectors

Related Article: Integrating Machine Vision & AI with Toyota Production System

Machine Vision and AI Technology for Inspection

Machine vision focuses on image acquisition by various cameras with different resolutions. AI is used for image processing, where the software algorithms analyze the image to provide desirable results that match the preset inspection parameters. Machine vision and AI technology are increasing the speed and accuracy of the inspection process, thereby paving paths for new ways of improving quality aspects of the manufactured products.

Machine Vision R&D Consultancy

Automated Inspection with the help of Machine Vision and AI Technology

Industries all around the globe compete hard to deliver products of the highest quality standards. To maintain or improve competitive inspection standards the methodology of quality control must be made efficient. Automation is an essential part of this improvement process as dependence on humans or manual processes would weaken the quality control process.

Why work with a vision system integrator

Read complex characters with varying fonts and challenging backgrounds using cutting edge Artificial Intelligence technology

Qualitas Technologies has developed an Optical Character Recognition (OCR) solution based on cutting edge Artificial Intelligence (AI) technology.

OCR using Machine Vision is commonly used to read characters printed on packaging labels, currency, credit cards, automotive parts like chassis, etc. Basically used for the purpose of identification, verification, tracing and tracking of character codes and data. Machine Vision OCR technology can also be used for ensuring the correctness of labels that are printed, this is especially important in pharmaceutical manufacturing.

Benefits:The main differentiators over traditional OCR technologies is that:

Easy to train using a point and click interface

It can read any style of character even if images differ in characteristics from the trained ones.

Much higher accuracies, comparable to human read rates, but with much faster processing time

Multiple fonts can be easily supported

Our solution uses Deep Learning for character recognition

Our solutions are running successfully across a variety of different industries and applications. Some images are shown as an illustrative example below.

The below example is where we’re using the latest deep learning technology to read complex and difficult to read characters. Deep Learning has been successful in achieving over 98% read rates, with over 97% accuracy in reading errors.

Why work with a vision system integrator

Read complex characters with varying fonts and challenging backgrounds using cutting edge Artificial Intelligence technology

Qualitas Technologies has developed an Optical Character Recognition (OCR) solution based on cutting edge Artificial Intelligence (AI) technology.

OCR using Machine Vision is commonly used to read characters printed on packaging labels, currency, credit cards, automotive parts like chassis, etc. Basically used for the purpose of identification, verification, tracing and tracking of character codes and data. Machine Vision OCR technology can also be used for ensuring the correctness of labels that are printed, this is especially important in pharmaceutical manufacturing.

Machine Vision System

Machine vision is a worldview and an umbrella term that envelops all modern and non-mechanical applications in which a mix of equipment and programming gives operational direction to gadgets in activities and working dependent on the catch and preparing of pictures. Despite the fact that modern PC vision utilizes huge numbers of similar calculations and approaches as scholastic and military utilizations of PC vision, the imperatives are normally altogether different. Industrial vision systems are needed to be more vigorous, more dependable, and stable contrasted with a scholarly and examination situated vision framework. They ordinarily likewise cost significantly less than those utilized in military applications. Hence, modern machine vision systems are generally minimal effort, of adequate precision, are profoundly dependable and hearty, and have high mechanical and temperature dependability. 

Inside the previous not many years, Machine Vision has increased gigantic prevalence in unique ventures, for example, retail and assembling. These businesses are utilizing the innovation to upgrade their client experience, streamline the use of assets, and accomplish better quality affirmation. Despite the fact that machine vision is seeing developing applications close by the advances in innovation, there are a couple of significant applications where machine vision has demonstrated amazingly important. 

Also Read, 7 APPLICATIONS OF MACHINE VISION

Machine vision frameworks incorporate the whole framework that both recognizes imperfections and eliminates them from the creation line. The accompanying advances make up the machine vision measure stream: PRE-PROCESS AUTOMATION 

In this underlying advance, the framework designs itself for the picture securing measure. A machine vision framework in an assembling plant may be reviewing different articles for the duration of the day. These articles may differ in size, shape, direction, and so forth. The machine vision framework needs to design itself to catch the most ideal picture for the best outcomes. The framework may need to change its working separation or even zoom in if necessary. This whole cycle is computerized by the product and is one of the most significant strides in any machine vision measure. The first and most significant advance of a whole machine vision framework is picture obtaining. Picture procurement is the activity of recovering a picture from a source, ordinarily equipment frameworks like cameras, sensors, and so on. It is the first and the most significant advance in the work process arrangement in light of the fact that, without a picture, no genuine handling is conceivable. In the picture obtaining measure, approaching light vitality from an item is changed over into an electrical sign by the blend of sensors that are delicate to the specific kind of vitality. 

These frameworks cooperate as one to furnish your picture preparing calculation with the most exact portrayal of the item. The objective of the whole picture procurement measure is to make a picture that is usable by the machine vision innovation. The imaging framework's quality is to a great extent answerable for the accomplishment of your machine vision framework. In a machine vision framework, the cameras are liable for taking the light data from a scene and changing over it into computerized data for example pixels utilizing CMOS or CCD sensors. The sensor is the establishment of any machine vision framework. Many key determinations of the framework compare to the camera's picture sensor. These key viewpoints incorporate goal, the complete number of lines and segments of pixels the sensor obliges. 

Related Article: IMAGE ACQUISITION COMPONENTS Picture handling is the algorithmic cycle for extricating valuable data from an advanced picture and may happen remotely in a committed PC, or inside in an independent vision framework. Preparing is performed by programming and comprises of a few stages. Initial, a picture is recovered from the camera. Now and again, some minor pre-preparing might be needed to streamline the picture and guarantee that all the essential highlights are featured. Next, the product finds the particular highlights, runs estimations, and analyzes these to the details settled upon before. At long last, the picture preparing calculation settles on a choice and conveys the outcomes. While numerous physical segments of a machine vision framework, (for example, lighting) offer similar particulars, the vision framework calculations are what separate different frameworks and should head the rundown of key segments to assess when contrasting answers for your necessities. Contingent upon the framework and region of utilization, the product arranges the boundaries of the camera, settles on the pass-bomb choice, and speaks with the manufacturing plant floor for post-measure computerization. 

POST-PROCESS AUTOMATION 

After the picture preparing has been done, the calculation needs to convey the pass or bomb results to the system answerable for following up on the isolation cycle. In light of the choice of the calculation, the framework isolates the things naturally. The framework at that point designs the vision framework for the following article in the assembling line. The entirety of this is important for the post-measure mechanization.

Also Read, 3 Reasons for choosing Machine Vision in Manufacturing

Machine Vision SystemMachine vision is a worldview and an umbrella term that envelops all modern and non-mechanical applications in which a mix of equipment and programming gives operational direction to gadgets in activities and working dependent on the catch and preparing of pictures. Despite the fact that modern PC vision utilizes huge numbers of similar calculations and approaches as scholastic and military utilizations of PC vision, the imperatives are normally altogether different.

Within the past few years, Machine Vision has gained massive popularity in dynamic industries such as retail and manufacturing. These industries are leveraging the technology to enhance their customer experience, optimize the usage of resources, and achieve better quality assurance.  By now, we all are aware of the benefits Industry 4.0 promises to deliver. Industry 4.0 is an umbrella term that refers to the numerous developments happening in the industrial value chain process. These changes are primarily powered by emerging technologies, especially the cloud, offering a better way to organize and manage all standard processes within the manufacturing industry.

In the United States, Japan, South Korea, Germany, and other European countries, the automotive industry plays a significant part in the overall manufacturing sector. In 2019 alone, almost 92 million motor vehicles were produced worldwide. The automobile industry is highly automated for mass production, with strict quality requirements and a high degree of cost sensitivity. Large manufacturers place a premium on having a close and trust-based collaborative relationship with their suppliers and technology providers who support this high degree of automation. Machine vision is a crucial part of this highly automated sector.

Within the automotive industry, quality check and assurance is one of the areas where machine vision can prove to be the most helpful.

Related Article: Machine Vision is creating a new wave in the Automobile Industry

HUMAN MISTAKES HAPPEN

A prime example of this capacity for machine vision to overcome human limitations is quality control, where mistakes can lead to defective products, rushed orders, and even worse, damaged reputations. Humans have been traditionally tasked with quality control because it requires judgment. Is the paint job on this unit without any defects? Is this particular part with or without any glaring defects that can prove to be dangerous for the user? A human can easily make that determination when presented with an isolated case.

However, an interesting thing happens when a human views not just a single unit but hundreds of them, let alone thousands of units streaming along a high-speed assemble line. After repeatedly seeing an image, that image ends up being imprinted on the brain. So when an inspector sees a number of parts at the proper quality level and then sees a unit that’s defective, the inspector’s eyes send that signal to the brain — but the brain may instead use the imprinted image of a flawless piece and not register a problem.

This is where machine vision can make the job much easier. Classification can achieve accurate and consistent results for the aforementioned problem. Classification involves predicting which class or category an item belongs to. Some classifiers output binary classifications like yes/no. Some are multi-class, that categorize items into one of several possible categories. Classification is a very common use case of deep learning—classification algorithms are used to solve problem categorization, image recognition, and image-based classification in the industrial manufacturing environment. In classification problems, the input is usually an image of the item that needs to be classified. The algorithm processes the entire image and classifies it accurately based on its previous training.

USEFUL APPLICATIONS IN QUALITY CHECK

The key examples of image processing systems in the automotive industry include:

1. Engine Character Recognition

In this application, machine vision is used to capture an image of the part numbers marked on engines and read them with the OCR tool. The part numbers can be read accurately without being affected by marking quality. This prevents the mixing of different types of engines. This application eliminates the tedious task of manual checking.

2. Autoparts Classification

Since the manufacturing process includes a large range of items, the classification of produced parts according to different automobile models can prove to be a tedious task. To solve this problem, classification algorithms can be deployed to recognize different model types and segregate the same without any human intervention.

3. Sticker Classification

Classification of stickers and manual separation of the stickers based on the variants has been traditionally done manually. The process is usually time-consuming, requires a lot of labor, and possesses a large possibility of error. Using OCR tools and machine vision a high level of accuracy and efficiency can be achieved.

4. Solder Inspection

Solder inspection has traditionally been difficult with 2D cameras. 3D cameras can measure height, so solder can be inspected accurately with machine vision algorithms. Using the height extraction function  the3D height images can be converted into gray-scale images (mm → shade) to generate a cross-sectional image at a specified height. Using the cross-sectional area and shape of the image helps in achieving stable fillet inspection.

Also Read: Integrating Machine Vision & AI with Toyota Production System

Within the past few years, Machine Vision has gained massive popularity in dynamic industries such as retail and manufacturing. These industries are leveraging the technology to enhance their customer experience, optimize the usage of resources, and achieve better quality assurance.  By now, we all are aware of the benefits Industry 4.0 promises to deliver.

Machine vision technology has been making significant advances in the food and beverage sector. For a long time, this industry has been a user of machine vision equipment. The industry has been widely regarded as an early adopter of the latest technology. Today, an increasing need for efficiency, quality, and regulatory compliance is driving even more machine vision technology adoption in the domain.

Machine vision has been playing an integral role in the ever-evolving and heavily regulated food and beverage industry, helping introduce efficiency, quality, and safety. Machine vision has an indispensable effect inside the assembling procedure for food and drink agencies worldwide. System imaginative and prescient makes packages faster at the same time as improving well-being and best.

Related Article: How Can Food & Beverage Segment Benefit From Machine Vision Inspection

CHALLENGES FOR MACHINE VISION IN THE INDUSTRY

The food and drinks industry provides novel problems for gadget imaginative and prescient systems manufacturers to provide the unwavering excellent, repeatability, and talent required for the programs wished in the inspection. A full-size quantity of these difficulties rotates around a fundamental clash in the enterprise: The truth of low-aspect duties and the requirement for huge amounts of security and quality.

Sustenance and refreshment producers are looked with progressively stringent necessities with regards to things, for example, sullying, recognizably, security, etc. Conveying device imaginative and prescient innovation into sustenance and drink assessment strategies can set apart time and coins at the same time as truly restricting those dangers.

There is zero resilience for errors in the food industry, mainly with reference to security and satisfaction. Genuinely, issues emerge whilst someone with a nut-allergic reaction takes a nibble of walnut dessert that is falsely named vanilla. To add to these issues, ordinary varieties in items are arbitrary, and the criteria to decide deformities are regularly emotional and undefined, presenting significant hindrances in system structure and arrangement.

Traditionally inspection processes have heavily relied on people to check for safety, quality, and consistency. This is expensive, subjective, and prone to large human error. This is where machine vision systems allow food producers to ensure high levels of safety and consistency among its products, while also keeping costs down.

MAJOR WAYS MACHINE VISION HAS IMPACTED THE INDUSTRY

Some of the ways that machine vision has positively influenced the food and beverage industry are:

1. Aluminium Tray Inspection

Aluminum trays are used for various things in the food industry. What they all have in common is that they should all be leak-proof with the correct contours. Due to the high cycle speeds and large production volumes, manual checks for aluminum, trays cannot be carried out on a random sample basis. An imaging system can be used in such cases to automate the production and inspection process.

The trays verified by the image processing station as defect-free can further be automatically stacked, shrink-wrapped, and packaged. This means that the only tasks that may need to be performed manually on the line are placing the aluminum coil at the entrance to the press, providing the packaging materials, and taking away the pallets with the stacked, shrink-wrapped trays.

2. Inspection and Cutting of Sausages

In order to cut down on both time and cost a machine vision system can be used that is able to divide numerous pairs of sausages in a very short amount of time. The sausages can be taken from the smoking or cooking trolley and transferred to rack onto the machine’s conveyor belt. The sausages can then be fixed in place and spread out so that the machines can inspect them and later separate them.

3. Inspection of Baked Goods

Image processing systems for bakery products can be used to inspect the geometry of each item. Therefore, the image processing system can measure the length, width and height of each baked good in order to detect any geometrical, dimensional or breakage errors. Additionally, machine vision systems can easily be configured to determine whether features like browning, topping, or roundness of the loaves, or bread rolls meet quality requirements.

4. Food Safety Compliance

Safety has always been a massive concern in the food processing business. Even the smallest contamination in the food can have massive consequences. Following the regulations is necessary. Factories can implement AI-based cameras to detect whether an employee is wearing a proper costume or not. AI-enabled cameras can help restaurant managers to keep a watch on the restaurant workers as to whether or not they are wearing proper food protection gears as per food safety regulations. It helps them to detect any indiscipline in real-time.

Also Read: Pizza topping inspection using Deep Learning

Machine vision technology has been making significant advances in the food and beverage sector. For a long time, this industry has been a user of machine vision equipment. The industry has been widely regarded as an early adopter of the latest technology. Today, an increasing need for efficiency, quality, and regulatory compliance is driving even more machine vision technology adoption in the domain.

Machine vision has been playing an integral role in the ever-evolving and heavily regulated food and beverage industry, helping introduce efficiency, quality, and safety. Machine vision has an indispensable effect inside the assembling procedure for food and drink agencies worldwide. System imaginative and prescient makes packages faster at the same time as improving well-being and best.

Related Article: How Can Food & Beverage Segment Benefit From Machine Vision Inspection