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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.
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Automated Quality Control
AI VS ML VS DL
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are terms that are more often than not, used interchangeably to describe software that behaves intelligently. However, they are disparate and it is useful to understand the key distinctions among them.
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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.
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Machine Vision is creating a new wave in the Automobile Industry
Vision Automation and Robotic Solution
MACHINE VISION IS CREATING A NEW WAVE IN THE AUTOMOBILE 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.
Similarly, Machine Vision has gained massive popularity in dynamic industries such as retail and manufacturing within the past few years. 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.
However, you may ask, how exactly is machine vision contributing to the automobile industry? What is its relevance when it comes to large streamlined manufacturing lines?
Related Article: Machine Vision- Changing The Way of Quality Inspection In Automotive Industries
THE AUTOMOBILE INDUSTRY IS AN EARLY ADOPTER
Together with the supplier industry, the automobile industry is considered an “early adopter” of machine vision technology. The automotive industry has always been at the forefront of promoting automation in production processes — from the production line and robot-supported manufacturing to today’s fourth industrial revolution, dubbed Industry 4.0. The automotive industry has historically been at the forefront of considerable innovation. Similar things can be said about the machine vision industry, making it the ideal partner for meeting the demand for new technologies.
Due to the very high-quality standards, the automotive sector places a series of special demands on their service providers. For example, solutions must be very robust, reliable, and powerful. Furthermore, enormous price pressures in the automotive and supplier industries demand competitively priced products that offer maximum value. Solutions need to be suited to reduce production costs over the long term. Other vital aspects include long-term availability of the machine vision products, including qualified support, as well as comprehensive knowledge of disparate technologies.
By merging the automobile production and machine vision more closely, carmakers can save money, speed up automated production processes, and make them more efficient. Standards can also be defined and established that make it easier to integrate different components.
Related Article: Machine Vision- Changing The Way of Quality Inspection In Automotive Industries
HOW VISION APPLICATIONS WORK
Mostly, vision applications in the automotive industry comprise of machine guidance or quality inspections. In quality control inspections, the vision system determines whether parts or subassemblies are acceptable or defective. The system then directs motion control equipment to reject or accept them. Machine guidance applications use vision systems to improve accuracy as well as the speed of assembly robots and automated material handling equipment to make the entire process significantly more efficient. Although they can vary in a number of ways, the applications are usually in one of several general categories.
Dimensional Gaging
With their precise recognition capabilities and easy programmability, the new generation of machine vision systems excels at ensuring that those measurements are correct. Dimensional gaging applications by machine vision often involves a variety of odd lines, angles, arcs, diameters, and tolerances. Almost without exception, the systems can measure them much more quickly, and with far greater reliability and accuracy than would be possible with even the most sophisticated manual methods.
Assembly Verification
Once again, here is an area in which the new generation of machine vision is proving its worth. Users easily “train” vision systems to look for detailed patterns and shapes that match templates for correctly made subassemblies. The systems accomplish those inspections better than virtually any other quality control method—manual or automated.
Flaw Detection
This has gradually turned out to be a primary mission for many machine vision systems on automotive industry production lines. These vision systems use powerful pattern recognition capabilities to find missing material, chips, scratches, dents, misplaced markings, and a wide variety of other flaws. In addition to ensuring the quality of finished parts and products, they also enable manufacturers to reduce costs by eliminating defective pieces before wasting additional material and production time on them.
To achieve high levels of precision and accuracy, the most effective machine vision applications typically use software-based vision tools that perform sub-pixel level, image analysis. These tools work with the system’s image processing capabilities to perform specific types of recognition and analysis, effectively streamlining the application development cycle.
Over the years machine vision systems have become much simpler to use. However, the applications themselves can still be extremely complex. In some cases, the best way to ensure success is to rely on the experience and expertise of the vision system vendor or a qualified system integrator.
Also Read: Using Artificial Intelligence (AI) for Visual Part Identification
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7 Common Applications of Machine Vision in Manufacturing
Vision Automation and Robotic Solution
Universally,Machine Vision has been a famous device for robotized visual examination in Manufacturing. In India, all the more as of late there has been a colossal increment in appropriation inferable from expanded framework reconciliation mastery and comprehension of the innovation. With the ongoing progression in Artificial Intelligence and expanded calculation power combined with propels in calculation improvement, Machine Vision has taken on another symbol as Deep Learning-based assessment frameworks. These frameworks are anything but difficult to prepare and educate and diminish the incorporation unpredictability with regards to "instructing" the machines (otherwise known as Machine Learning) on what to search for.
Nonetheless, it's essential to seeing practically, how this innovation can be applied in assembling. There are a wide range of utilization gatherings. It's imperative to comprehend the sort of utilization your necessity falls under to choose which sort of framework structure and innovation you have to put resources into. Frequently time there will be a need to have one (or considerably more) practical necessities dependent on your application need. . Recorded beneath are the essential classifications.
1.Item Detection
Here the goal is to find or distinguish whether an object of intrigue is available or missing in a given picture. A model is whether the egg plate or ice plate is available in a cooler. The vision framework basically recognizes the part by temperance of a "brilliant picture" or a "design" that has been pre-prepared which it uses to contrast it and the continuous pictures from the camera.
2.Estimation
Estimation applications as the name proposes includes an item measurements to be precisely decided. This is finished by finding certain focuses on a picture and estimating mathematical measurements (separation, sweep, width, profundity and so on) from this picture. Instances of such applications are estimating the internal distance across of a motor chamber bore. Another model is estimating the fluid fill level inside a container. Estimation should be possible utilizing either 2d or 3d cameras.
3.Blemish Detection
Blemish recognition applications distinguish anomalies, for example, surface imperfections, gouges and scratches on an item surface. Defect recognition applications should be painstakingly externalized so as to guarantee "satisfactory" blemishes can be recognized unsuitable imperfections. The utilization of Artificial based machine vision
is unmistakably appropriate for these applications as the framework is shown dependent on models instead of "rules".
4.Print Defect Identification
ID of printing oddities like off base shading conceals or where parts of the print is absent or imperfect is the target of print deformity ID. In these applications a brilliant or ace picture is prepared to the framework so as to recognize any deviations from this ace.
5.Recognizable proof
Recognizable proof utilizing Machine Vision includes distinguishing a section or item so as to follow this part over the assembling or calculated cycle or to confirm that the correct part is being delivered. ID should be possible either by reading characters (OCR) or standardized identifications.
6.Finding
Finding objects is a typical utilization of machine vision for applications, for example, mechanical direction. Here the machine vision framework's goal is to find the directions/position of an object of intrigue. This data can be utilized to get the object or play out whatever other cycle that is subject to this area. This kind of machine vision application, requires the kid part of intrigue should be instructed to the machine vision framework, identify this part during creation.
7.Checking
Considering the name recommends is the utilization of machine vision to check objects of intrigue (say pharma vials in a canister or cylinder rings in a stack)
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How to Improve Quality in Pharma Industry using Machine Vision
HOW TO IMPROVE QUALITY IN PHARMA INDUSTRY USING MACHINE VISION
Quality has always been the top concern in pharmaceutical manufacturing applications. Stringent FDA standards mean high levels of liability for errors in production. Machine vision plays a major role in delivering consistently high-quality products in the pharmaceutical industry, but machine vision can also deliver productivity gains, within the confines of strict quality demands.
The pharmaceutical industry was one of the earliest adopters of machine vision, as the incentives to guarantee product integrity and safety have always been foremost in this industry. Machine vision technology has advanced significantly over the years since its initial entry into the industrial sector. It is now suited for very specific applications in the pharmaceutical industry and creates productivity in a number of ways.
Manufacturing, shipping, and data processing are the most common use cases for machine vision in the pharmaceutical space. Machine vision has automated many different applications. Typically, machine vision technology is used for critical processes such as:
QUALITY CONTROL FOR PRODUCT AND PACKAGING
One of the most prominent applications for machine vision software in pharmaceuticals is in the quality control and packaging departments. The possibilities for these range from the quantity and condition detection to the inspection of packaging and included items such as printed instructions and dose applicators. Companies can utilize machine learning on the production and packaging lines to ensure each of their production units meet their organizational quality requirements and other compliances.
The business problems within pharmaceutical quality control and packaging inspection that machine vision addresses most often are as follows:
Counting the number of pills each bottle is filled with
Inspecting each pharmaceutical pill for accurate dimensions and any damage
Inspecting packaging for quality control or damage
Quality control of secondary items such as the instruction leaflet
Label validation for product information and barcodes
Quality control can also be extended to a product’s packaging when used to detect the presence of childproof caps, plastic wrappers over bottle caps, the correct box, or any other packaging element. A camera at the end of the production line could feed a machine learning model footage of each unit being packaged, and it would scan the video for each item being attached properly.
Also Read: Understanding Cost of Quality while investing in Quality Control Automation
SHIPMENT TRACKING AND PRODUCT TRACEABILITY
In the pharmaceuticals domain, it is imperative that drugs and all other products be trackable on their way to stores and distribution centers for maximum accountability and compliance. The products must also be traceable from the patient’s possession back to the manufacturer in case of any defects or unforeseen side effects.
Pharmaceutical companies are responsible for providing all the necessary tracking and manufacturer information with all of their products. Machine vision solutions can help consolidate all this information and deliver it across channels during many steps of the shipping process.
Companies can utilize machine vision software to ensure their tracking information is both accurate and readily available across the channels that would need to access it. This could be in the case of an emergency or a simple customer service inquiry. The possible departments that could benefit greatly by using machine vision technology for this purpose are as follows:
Factories and each of their various production lines
Distribution Centers
Warehouses
A machine learning model could also be trained to detect information from barcodes for retail inventory and serialization codes to prevent counterfeiting. Serialization codes could be related to the date of manufacturing as well as the factory or production line where the product was manufactured. This could help pharmaceutical companies to determine where and when a given product unit was made if they were ever asked.
Related Article: 7 Common Applications of Machine Vision in Manufacturing
DIGITIZATION
Another major possibility for machine vision in pharmaceuticals is the automation business processes such as document digitization and data entry. A machine learning model for OCR trained in clinical and pharmaceutical forms could discern and extract key data points from those forms and deliver them to a database. This can prove to be tremendously useful for companies with a large number of physical forms containing data that they want to use for future projects.
Machine vision could help digitize the following documents and more:
Clinical Trial Documents
Clinical data or medical records from patient reports
Lab Notebooks
Patient reports may contain useful information about a patient’s response to a drug and the severity of any side effects. This is especially important if a new side effect is discovered or if the patient has an allergy to the drug. With proper digitization both the business and the customers can benefit as the company can factor in every past patient reaction and use that awareness to drive patient health and satisfaction.
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PARAMETERS FOR LENS SELECTION
PARAMETERS FOR LENS SELECTION
The first step of any machine vision system is image acquisition. Image acquisition is the action of retrieving an image from a source, usually hardware systems like cameras, sensors, etc. It is the first and the most important step in the workflow sequence because, without an image, no actual processing is possible. In a machine vision system, the cameras are responsible for taking the light information from a scene and converting it into digital information i.e. pixels using CMOS or CCD sensors. The sensor is the foundation of any machine vision system. Many key specifications of the system correspond to the camera’s image sensor.
However, the light from the source has to be focused adequately by a lens on the sensor for it to capture the image with maximum clarity. The lens should provide appropriate working distance, image resolution, and magnification for your system.
Following are some of the important parameters to consider when selecting an appropriate lens for your system:
1. WORKING DISTANCE
Working Distance dictates the space in which the optical system of a machine vision system must work. Working distances generally get longer when objects are large, if they move through large distances, or if they need to be distanced from the camera for safety and environmental purposes. For example, for an object that resides in an ultra-high vacuum (UHV) chamber, the camera will have to be mounted outside the chamber and capture images through a window, as most cameras are incompatible with UHV. In such a case, the minimum working distance is the distance from the window to the object in the chamber.
Considering your working distance is important because, as the working distance changes, so does the image distance. This, in turn, increases or decreases the magnification.
Related Article: The Ultimate Guide to Machine Vision Camera Selection
The Field of View is a measurement of distance and is defined as the viewable area of the object under inspection. In machine vision applications, the lens focal length and the sensor size sets up a fixed relationship between the working distance and the field of view. It is the area of the inspection captured on the camera’s imager and that fills the camera sensor. The size of the field of view and the size of the camera’s imager directly affect the image resolution, which is one of the most important factors determining the accuracy of the system.
2. CAMERA SENSOR SIZE
The size of a camera sensor’s active area is crucial in determining the system’s field of view (FOV). Given a fixed primary magnification that is usually determined by the imaging lens, larger sensors yield greater FOVs. The standard sensor sizes are ¼”, 1/3″, ½”, 1/1.8″, 2/3″, 1″ and 1.2″, with larger available. The nomenclature of these standards dates back to the Vidicon vacuum tubes used for television broadcast imagers, so it is important to note that the actual dimensions of the sensors differ.
One issue that can potentially arise in an imaging application is the inability of an imaging lens to support certain sensor sizes. If the sensor is too large for the lens, the resulting image may appear to fade away and degrade towards the edges because of a phenomenon known as vignetting. This effect is also referred to as the tunnel effect since the edges of the field become dark. Smaller sensor sizes usually do not yield this vignetting issue.
Also Read, CAMERA FUNDAMENTALS IN MACHINE VISION
3. RESOLUTION
A high-resolution image can only be captured if a high-resolution lens is utilized. The lens also needs to be capable of resolving the pixel size accurately. The resolution of a lens is given in line pairs per millimeter and specifies how many lines on a millimeter appear as separate from one another. The more line pairs that appear as differentiated, the better is the resolution of the lens. Having a high-resolution lens can be beneficial as higher resolutions preserve details in the picture more accurately that would be completely lost in low-resolution images.
4. DEPTH OF FIELD
A prerequisite for a robust and accurate inspection is a sharp image in most of the cases. On the z-axis, only a small area within certain limits provides a sharp image: The depth of field is the scene space in depth, away from camera and optics, which appears sufficiently sharp in the video image generated by the camera. As an object is placed closer or farther than the set focus distance of a lens, the object blurs and both resolution and contrast suffer consequently. For machine vision applications, it is important that all features to be inspected are located within this depth of field area for maximum clarity and accuracy.
Related Article: IMAGE ACQUISITION COMPONENTS
Choosing the appropriate lens needed for a machine vision system requires an expert understanding of the application requirements and technology capabilities.
The lens should provide appropriate working distance, image resolution, and magnification for your system.Following are some of the important.
