QUALITAS TECHNOLOGIES

Introduction The term “bearing” is derived from the combination of the two words “to bear”, reflecting this component’s ability to bear loads.  A bearing is a common machine element used to regulate relative motion and reduce the friction between moving parts. Metal-on-metal contact results in a large amount of friction, and therefore, by allowing two surfaces to roll over one another, bearings reduce the amount of friction produced and guard the metal’s quality.The boom in the automotive sector has provided an impetus to the bearing manufacturing industry, presenting us with a vast range of options. For instance, a roller bearing is one type that contains rolling elements like balls or rollers in circular races, making them effective at bearing radial loads. Another kind of bearing called the tapered bearings has rings and rollers tapered in the shape of truncated cones allowing them to support radial and axial loads simultaneously. Ultimately, the aptness of a kind of bearing differs with varying application demands.

Related Article– Quality Inspection of Bearing Balls

Types of Bearing Defects Since the significance of bearing is naturally justified, bearing manufacturing has always been the subject of continuous improvements and extensive research. However, despite the careful design, bearing manufacturing and testing, bearing defects or even bearing failure is probable. The principal causes of bearing defects and premature bearing failure are overloading, improper lubrication, contamination, and improper handling and installation.

Let’s go through some of the common types of bearing defects.Pitting & Contaminants Pitting is the result of foreign materials like metal particles and dirt entering into the bearing. Improperly cleaned housings are a primary source of these particles. Even light pitting can result in bearing failure, and thus, the lightly damaged components must be addressed evaluated with caution.Wear Wear is commonly observed on the rolling surfaces of cylindrical rollers and races. Such damage is majorly the effect of lubrication failure and might also occur due to the slippage of rolling elements towards bearing rings.Corrosion Corrosion is the consequence of inadequate protection against atmospheric moisture. The sites of rust formation can also gradually become the initial sites of flaking, resulting in reduced bearing durability. Vibrations of loose components cause frictional corrosion. The rubbing ultimately results in surface oxidation and results in early wear and fatigue.Grooving Hard contaminants can often enter the bearing assembly, get wedged in the cage and cut grooves in the rollers. This bearing defect is known as grooving. The damage that occurs due to this bearing defect is irreversible and might lead to premature bearing failure.Cage damage The roller bearing cage is under little stress in normal operating conditions. The problem begins with poor lubrication. If the lubrication provided is inadequate, cage wear starts occurring on the surfaces which are in contact with the rolling elements.Bearing failures increase downtime, drive up the operational costs, shorten the service life, lower the plant’s operational efficiency, and in the worst of cases, can injure your employees. Unprecedented bearing failures might also force companies to incur the costs associated with replacing or repairing the bearing and adjacent components, which might have also sustained damage. To minimize such occurrences, deliberate planning and timely maintenance are necessary.

Related Article– Surface Inspection Of Bearing Cages- Use Case

How is Visual Bearing Inspection Performed? Needless to say, bearings are components of pivotal significance in a large number of industries. It has come on the shoulders of bearing manufacturing companies to provide quality components to the expanding bearing markets. To ensure compliance with the set standards of bearings and discard any defective bearings, a proper bearing inspection procedure is crucial. Some of the features that you should look out for are mentioned below:Cracks, that might occur due to heat treatment, grinding, stress, forging, etc. Pitting, oxidized surfaces, and rust Mechanical scars, like abrasion, scratches, hit marks, etc. Material that might be peeling off Welding quality or bearing cage riveting Diligent inspection of the features mentioned above will help you eliminate the majority of defective bearings.

Automating Bearing Inspection with Vision Systems

The small size and the nature of defects make manual bearing inspection quite tedious when a large volume of components is to be inspected. The manual inspection also means more workforce, lower productivity and more errors. These issues negatively impact the bearing inspection procedure’s effectiveness, and thus the need for automation arises.Let’s consider the inspection of taper rollers. Bearing rollers are the most critical components in a bearing and determine the bearing’s life, performance, and stability. We have developed an automated roller inspection system that is capable of inspecting around 200 parts per minute. Since the rollers go through various stages, defects can come up at any stage of the bearing manufacturing process. The taper roller inspection system can detect a multitude of defects such as outer diameter scratches and cracks, rust, spiral marks, crack on the chamfer, and several others.

Related Article– Automated Bearing Inspection- Needle Bearings- Use Case

What is a Deep Learning neural network?

Deep Learning is a subset of artificial intelligence. Just like we have a neural network in our brain, deep learning uses an artificial neural network to use raw input data, model and process multiple kinds of relationships, and transform to outputs parallelly. It involves complex mathematical computations and analysis over hundreds of layers between inputs and outputs using the forward path of data, the backward path of data, and iterating through the data multiple times. For example, while implementing it for a quality process, thousands of images are given as input. These images are processed picture element by picture element to build a learning algorithm, develop the rules and achieve the desired output.

Also, Read DEEP LEARNING AND ITS PROBLEM TYPES

What are the characteristics of Deep Learning?

Here are some key characteristics of Deep Learning –

It consumes a tremendous amount of raw input data.

It processes the input data through multiple layers of nonlinear to calculate a target output. It is computation-heavy.

We do not always know the conclusions deep learning algorithms arrive at and have to be careful in implementing them.

It requires specialized hardware such as Graphical Processing Units (GPUs) for optimized learning.

It is costly to implement deep learning algorithms due to the sophisticated hardware and the amount of storage required. Moreover, highly trained human resources are needed to develop and execute deep learning algorithms.

Deep learning algorithms take longer to train but are easier to train than debug. Training involves some amount of coding and feeding the correct input data. The algorithm then develops the logic by learning. Therefore it is difficult to debug it manually or track errors. We can improve the model by fine-tuning data or adding additional data. Since we live in the age of data, it is easier to improve the model using data or build a new model rather than debug the model.

What is the difference between Deep learning algorithms and rule-based algorithms?

Deep learning algorithms involve complex mathematical computations over hundreds of layers between the input and output data. Rule-based algorithms follow a structured path of logic. In deep learning, you provide input data and hypo parameters and apply logic decisions. You choose operations to be done and the rate at which the algorithm should learn. The deep learning algorithm then independently works its magic. With changes in data input, it fine-tunes its analysis.

Therefore it can be applied to complex business scenarios such as autonomous driving or pattern recognition. On the other hand, in rule-based programming, you code every detail and every step. When there are changes, you have to code explicitly to incorporate new features. It has specific pre-programmed blocks. Hence, there are limitations to what rule-based programming can achieve. It is not easy to bypass deep learning algorithms, and you cannot implement error tracking well in deep learning as the logic built is quite complex and covers a wide variety of scenarios. Rule-based programming is easy to debug and test.

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

Why should we use deep learning?

Business scenarios are becoming increasingly complex with more variables, personalization, regulations, and a high level of interconnectedness. Traditional algorithms are not capable of handling current business scenarios. Hardware has improved tremendously, and we have increased computing power in our hands. Data collection, storage, and processing in huge volumes is a possibility now. The combination of all these factors allows deep learning to provide better performance with lesser effort.

How has Qualitas used deep learning in its business processes?

Qualitas has applied rule-based algorithms and deep learning algorithms in our business processes. We have used it widely in processing images for quality control and assurance. We have used deep learning for packaging inspections, part counting, part identification, text identification, etc. Gas cylinder inspection is a process where we have automated quality inspection through deep learning and vision technology.

Qualitas uses 2D image processing and deep learning OCR here. We check the weight, expiry date, and filling pressure to be used to refill up the cylinder. We do this by capturing images of the relevant information on the cylinder using a camera with a wide-angle lens mounted on top. It is aided by deep learning technology to read characters with top-notch precision and high speed.

Deep learning is a powerful and fast-advancing technology. When it is leveraged in the right manner and used in the appropriate business scenarios, it can prove to have a significant impact on quality control and assurance.

Introduction The very notion of machines being able to “see” things and accomplish mind-numbing tasks with tremendous accuracy seems to be the stuff of a sci-fi movie. But vision systems have now converted this notion into a reality. You may have come across IT buzzwords like “machine vision” and “computer vision”. You may be wondering what is computer vision, and is it not the same as machine vision? While these terms are frequently used interchangeably, these are actually two different kinds of technologies. Let’s now dive directly into what is computer vision technology and machine vision technology. Computer vision is composed of a system with high, PC-based processing capabilities for analyzing the visuals that it collects. It can identify, predict and observe trends and also study a lot of variables at once. Computer vision has found significant traction in multiple industries that have to analyze mountains of data being churned out every day. Machine vision can be thought of as a simpler and more robust form of computer vision. It is built to analyze images and take simple, automated decisions accordingly. It often only requires PLC-based processing. Machine vision systems work well in production and practical applications. Computer vision and machine vision are both inherent image processing systems. So, they both have similar components like a camera, a frame grabber, lighting systems, and necessary software to manage the data. While the involved processes may appear the same superficially, these vision systems have considerable differences in terms of capabilities and processing techniques. Also, Read Different Types of Vision Systems

Computer Vision vs Machine Vision Objective Computer vision technology is dedicated to comprehending images maximally after acquiring, processing, and analyzing them. Their primary aim is to extract all the possible, meaningful insights from the object or scene under observation. Machine vision, on the other hand, focuses only on the most critical parts relative to its application to take simple, automated decisions.Capability Computer vision systems are equipped with much more powerful processors than machine vision systems. However, these PC-based vision systems are less robust in industrial environments, and often, industrial applications don’t require such advanced capabilities. Now, this is the place where machine vision fits in perfectly.Application Machine vision systems are often designed to cater to specific applications. While computer vision technology often delivers value to the Sciences and Big Data applications, machine vision technology is more concentrated in the engineering domain.Also, Read 3 Uncommon Applications of Machine VisionBenefits and Limitations Since their inception, both computer vision and machine vision have amassed massive popularity due to their amazing range of benefits. Some prominent benefits are enumerated below:Considerable reduction in operational and maintenance costs. Stringent quality control and reduced scrap rate. Tasks are accomplished faster and with formidable accuracy. Minimal human involvement ensures uncontaminated products. Best alternative to labor working in hazardous environments. It spares the employees considerable time to indulge in creative thinking and tackle the more critical issues. However, as valuable as these benefits are, there are also a few limitations to these vision systems, which are:Since the vision systems involve the use of AI, ML, and other advanced technologies, companies have to hire a team of professionals with suitable technical expertise. A dedicated team is needed to regularly monitor and evaluate the performance of the vision systems to avoid any unanticipated breakdowns. Applications of Computer Vision and Machine Vision Broadly speaking, both computer vision and machine vision are extremely valuable technologies and have heaps of industrial use cases. Some prominent industries where these technologies are gaining traction are:Computer Vision Applications Medical Healthcare heavily relies on imaging, extraction of critical facts, and recognition of trends from images. Also, the visuals generated by medical imaging are frequently not entirely clear. Thus, computer vision, a form of AI, can aid the medical staff in making an accurate and timely diagnosis. For instance, computer vision is being used extensively to detect pneumonia from the X-ray reports in the COVID pandemic.Retail Computer vision applications such as security, spillage detection, theft control, and video analytics are helping retailers augment the customers’ shopping experience. By sifting through the viewed products and purchases made, the system can offer more personalized recommendations to each shopper and instill loyalty.Banking So, what is computer vision doing in the banking sector? Computer vision is being used these days extensively to identify counterfeit currency at customer touch-points. Also, using computer vision, washed cheques, or fake cheques can also be spotted easily.Also, Read Understanding Camera Sensors for Machine Vision Applications

Machine Vision Applications Pharmaceutical Industry Product integrity is the paramount responsibility of the pharma industry. Machine vision systems can be utilized for counting, proper packaging, and diligent inspection. These systems can identify defective and substandard products in real-time and minimize the turnaround time.Agriculture Machine vision systems can help farmers identify and fulfill the nutritional needs of different crop varieties. It will also constantly monitor livestock growth and alert the farmers regarding weeds and crop diseases. Smart automation of such arduous tasks will boost the bottom-line yield and throughput.F&B(Food and Beverage) Industry The FMCG industries demand strict compliance with regulations, and even a slight deterioration in quality can have extreme repercussions. It is impossible to diligently inspect each product, even for a large human workforce. Machine vision systems can check even the smallest of details and ensure optimal product quality standards.Also, Read 7 APPLICATIONS OF MACHINE VISION

Robotic process automation has reached the next level, with vision automation making it faster, more accurate, intelligent, cost-effective, and versatile.  It is a must-have solution for industries that face challenges like severe shortages of labor, dangerous tasks, or repetitive work.

What is Vision Guided Robotics?

Vision-guided robotic systems are a combination of a robotic application aided by a Machine Vision system. A specialized camera takes a picture of an object and analyses the image to send precise coordinates to a robotic arm. The robotic arm then moves to the desired position.

They have two main components –

Industrial robotic arm

Industrial vision system

Industrial Robotic Arm

It is usually made up of 4-6 joints and resembles a human arm, with a wrist, forearm, elbow and shoulder. Robotic arms come in varying styles, sizes and abilities, providing simple linear motions to complex seven-axis motions.

The six-axis robot has six degrees of freedom. It can move in six different ways, unlike the human arm, which has seven degrees of freedom. Industrial robotic arms provide programmable motion to pick-and-place a part at the desired location with a specified speed.

Robotic software allows the robotic application to be programmed to perform different robotic motions.

An industrial robotic arm is usually made of steel or cast. A robotic controller rotates motors that are attached to each joint.

Industrial Vision  System

Machine Vision works as the eyes of the robot application. Machine vision systems in robotics comprise a machine vision camera, appropriate lighting, and image processing software. Depending on the technology involved, industrial vision systems can be used in various applications that involve precision and accuracy.

Also, Read Robotic Process Automation – Carving the Future of Different Businesses

Robot and Vision Systems Work Hand in Hand

In a robot vision system, the camera takes a picture of the working area or object.  The robot will manoeuvre, and the software directs the robot.

Integrated Machine Vision With Robotic Guidance Applications

Robot vision systems perform a variety of tasks:

Welding

Assembly of parts

Handling raw materials and finished goods

Packaging of products

Placing parts in different places, positions, and assemblies

Dispensing fluids in precise locations

Palletization

Inspection and quality control

Measurement

Pick and Place

In systems where machine vision is integrated with robotic guidance applications, the machine vision system aided by robot vision helps the robot discover the location of an object in space so that the robotic arm can be guided to pick and place it at the desired point.

2D Guidance

2D machine vision is done with a digital camera and software that analyses a digital image of the part’s 2D location and orientation for robotic processing. It provides the place of a part or product in a single plane even if the product is moving like on a conveyor.

3D Guidance

A 3D Machine Vision system processes parts randomly located across three dimensions and can accurately discover each part’s 3D orientation. This kind of flexibility provides greater opportunities for using Robotic process automation in a growing base of applications. All 3D images are similar, but how the basic image is acquired and processed differs.

Passive Imaging

With a single camera or multiple cameras, passive 3D imaging uses standard machine vision illumination to acquire the camera’s image and requires no special lighting techniques. It is not the best suited for 3D imaging.

Active Imaging

It is a widely used 3D imaging method that uses patterned lighting and a single camera or multi-camera to create visible features on the surface of the scene.  

Laser Triangulation Scanning

Here, a laser transmitter, a camera, and the object to be scanned are brought together for digitization and to get the object’s spatial coordinates.

Time of Flight

Time-of-flight imaging uses specialized imaging sensors or cameras to calculate the distance between the camera and the object by timing pulsed or phase-modulated light reflections.

Challenges In Using Vision Guided Robots

Vision guided robots are more advanced than ever, but there are some challenges around them –

Camera-Robot Coordination

Precise and complex integrated engineering is required for vision-guided robotics to integrate machine vision and robot control software platforms to calibrate the robot’s actions to the camera’s field of view (FOV) and any objects within it.

Axes of Retrieval

The vast majority of articulated robots have six axes of movement, also called six degrees of freedom. But it still can’t mimic a human arm that can move in seven axes of freedom.

Random Bin Picking

Bin picking is challenging due to randomness, object identification and differentiation, grip optimization, and path planning.

Also, read our case study– Robot Automation Assistance

Benefits of Vision Guided Robotics

Vision guided robotics have the promise to automate extremely complex tasks and offer many benefits –

They respond well to changes in the operating environment leading to reduced downtime.

They increase the throughput in assembly lines allowing companies to get ROI quicker.

They can improve product quality and reduce production costs.

Future of Manufacturing with Robotic Guidance

Robot vision systems play a crucial role in industries, and the future looks bright. With innovation in 2D and 3D vision technology, they can be customized for more applications and small and big businesses. As companies increase customization in their products and services, the demand for robotic vision systems will grow. They can play a crucial role in 24×7 production.

Conclusion

Computer vision is composed of a system with high, PC-based processing capabilities for analyzing the visuals that it collects. It can identify, predict and observe trends and also study a lot of variables at once. Computer vision has found significant traction in multiple industries that have to analyze mountains of data being churned out every day.

Introduction to Machine Vision

The term ‘Machine Vision’ has gathered quite some hype these days, emerging as a powerful and cost-effective solution to accomplish repetitive and erroneous tasks. Unlike the human workforce that fails in inspecting all the details of the products and incur considerable time and costs, machine vision can get the same job done with incredible speed and accuracy. Some of the plethoras of benefits offered by Machine Vision systems are the following:

Automating repetitive and arduous tasks ensures seamless workflow and spares employees the time to indulge in creative thinking and problem-solving.

Drastically reduces operational and maintenance costs and saves hours of manual labor.

Delivers unparalleled accuracy and precision, detecting even miniature defects.

Minimizes human intervention and hence, eliminates possibilities of contamination and errors.

Can perform tedious tasks in hazardous environments.

In 2017, MarketWatch cited that the machine vision market valued at USD 6.41 billion in 2017, is expected to reach USD 13.53 billion by 2023, at a CAGR of 13.27% during the forecast period 2018-2023. This is statistical evidence that corporations are increasingly leveraging machine vision to achieve greater profits.

Also, Read Automation Trends to Watch in 2021

How Machine Vision System Works

Let’s assume our machine vision system inspects products. In this scenario, first, the sensors detect if any product is present. After verifying this, the sensor triggers the camera to capture the image and the illumination system to highlight its features. Next, a frame grabber takes this image and translates it into a digital output. After being stored in the computer’s memory, the system software analyzes the image based on predetermined criteria. If the product fails to pass the quality tests, it is automatically rejected.

Also, Read Identifying Requirements for a Machine Vision Project

Types of Vision Systems

A massive range of machine vision systems are available in the market, each being characterized by different levels of flexibility, performance, and cost. Vision systems can be divided into three classes, which are the following:

1. Smart camera-based vision systems

Smart cameras consist of a sensor, processor, and I/O in a compact arrangement, generally no bigger than a standard industrial camera. These solutions offer a simple and intuitive interface that facilitates easy operations with minimal training. To configure machine vision systems for inspection tasks, a computer needs to be connected to the smart camera via a network interface. This connection, however, is not required during the runtime. A major advantage of smart cameras is their compact design and easy communication of results.

Also, Read Smart Cameras VS Multi-Camera Vision: How to choose the right fit for you

2. PC based vision systems

The classical PC-based machine vision systems have an industrial computer at its heart that manages and communicates with all the other peripheral devices like cameras and lights. After processing the inputs from cameras and analyzing the information via the software, the computer communicates the decisions to the other devices. When the application requirements demand high processing power, a number of cameras, or dedicated FPGA processors, PC based vision systems come into the picture.

3. Compact vision systems

The compact version system is much like a ‘lighter’ version of a PC based vision system. It is based on embedded processing technology and usually includes a graphics card that captures and transfers the data to a separate peripheral device like an external monitor to be viewed. Generally, compact vision systems also have an inbuilt graphical user interface that is operated easily using a touch screen monitor or mouse. Sometimes, compact vision systems manage not only manage first-level inputs like the camera and trigger inputs but also have embedded ones.

Which type of system is suitable for you?

To choose the perfect kind of machine vision system to address your requirements, it is imperative to know the characteristics of each vision system type.

Also, Read Camera Types for Machine Vision Applications

1. Smart camera-based

The simplest and most cost-effective vision systems are based on smart cameras.

Recommended for simpler applications.

Easy to set up and deliver the basic functionalities of vision systems in a compact form.

2. PC based vision system

Solutions based on PC systems offer the highest degree of flexibility.

High performance and computing power.

Occupy a significant amount of space and are quite expensive.

Recommended for complex applications that demand multiple inspection tasks to be carried out at a fast rate.

3. Compact vision systems

Compact vision systems offer optimized heat dissipation.

Enable the usage of fast processors to achieve maximum performance in a robust and compact structure.

Allows simultaneous operation of multiple cameras.

Recommended for less demanding applications.

Also, Read How Machine Vision Cameras Have Transformed Electronics Inspection

Conclusion

Machine vision systems ensure seamless workflows in tedious and repetitive operations like positioning, measurement, counting, and flaw detection, which can be mind-numbing if done manually. Some of its prominent benefits include enhanced productivity, quicker turnaround,

compliance with standards, and higher flexibility. Quality control is a major factor driving its incorporation into industries like automotive, pharmaceutical, and F&B.

In this article, we categorized machine vision systems into three major types: Smart camera-based vision system, Compact vision system, and PC based vision system. Once correctly implemented, modifying or replacing the machine vision systems can incur you a lot of your valuable time and a considerable amount of money. Thus, it is essential that you set clear business objectives and have reasonable demands from your vision systems before jumping to the selection process. We have comprehensively explained the types of vision systems, their features, characteristics, and limitations to help you choose the apt kind of system to meet your requirements.

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

CONCLUSION

Introduction to Predictive Maintenance

Are you worried about huge maintenance costs and high maintenance frequency eating up your profits? Do you often witness unprecedented breakdowns that incur substantial time and resources? If you nod in silent agreement, then predictive maintenance might be a plausible and effective solution to your worries.

Predictive maintenance (PdM) is a kind of condition-based maintenance that continually monitors the performance and condition of assets using sensor devices. These devices supply data in real-time, to predict and prevent any imminent failures. The ultimate purpose of predictive maintenance is to predict when the equipment might break down, followed by preventing the failure through regularly scheduled and corrective maintenance. This ensures that the maintenance frequency is as low as possible, without incurring high maintenance costs.

Also, Read Understanding Camera Sensors for Machine Vision Applications

Advantages of Predictive Maintenance

Some of the most prominent benefits of predictive maintenance are the following:

Reduced downtime and longer life

Asset failures can be quite stressful and expensive. Predictive maintenance can predict issues, reducing downtime. A PWC report claims that PdM enhances uptime by 9% and extends the lifetime of aging assets by 20%.

Reduced maintenance costs

Since planned maintenance is carried out based on a schedule, there might be instances when maintenance is carried out even when it is not required. Predictive maintenance eliminates such inefficiencies. From the symptoms interpreted from the data, technicians can focus on only the necessary equipment, saving costs and time.

Improved safety

Predictive maintenance can help reduce workplace accidents by alerting the maintenance teams regarding any imminent equipment failures. According to PWC, predictive maintenance in manufacturing can reduce safety, health, and environmental risks by 14%.

Enhanced productivity

If equipment breaks down during a critical operation, the entire workflow gets disrupted. Discontinuity in operations and the forthcoming repairs can take away valuable time and resources. By preventing any unprecedented equipment breakdowns, predictive maintenance ensures operational continuity and seamless workflows.

Also, Read Automation Trends to Watch in 2021

Disadvantages of Predictive Maintenance

Despite its vast set of advantages that provide a considerable impetus to some companies’ net throughput, predictive maintenance comes with its own challenges. A few of such challenges that make it unsuitable for some companies are:

Scheduling takes time

It takes a considerable amount of time to plan and implement a PdM schedule.

Additional costs

Given the complex nature of predictive maintenance, plant personnel needs to be trained on using the equipment and interpreting the analytics. It also involves investment in maintenance tools and systems. Tersely, condition monitoring has a high upfront cost.

Applications of Predictive Maintenance

Predictive maintenance can find application in all industries where machines produce significant amounts of data and where data analysis can support maintenance and fine-tuning. Mentioned below are some industries where PdM is already gaining prominence are the following:

Automotive

In an industry that relies heavily on production and assembling, equipment failure can result in disruption and might incur the company millions. It is no surprise that the automotive industry will embrace PdM technology that reduces downtime and ensures continuous and efficient workflows.

Transportation

Airlines have to consistently, closely monitor sensor data from the airplanes’ complex equipment. Proper functioning of equipment is of paramount importance to ensure passengers’ safety. Complex machinery present in trains can also benefit from predictive maintenance.

Oil & Gas

The Oil & Gas industry utilizes costly equipment in extraction and refining processes that can lead to health and environmental hazards in case of failure.

Ports

Since port equipment is continuously exposed to harsh conditions, their conditions deteriorate quickly. For example, cranes are crucial tools but are prone to failure. Crane downtime can lead to more waiting time for ships and lower throughput for ports. Reducing downtime can play a critical role in enhancing service quality and minimizing waste.

Also, Read Effects Of Augmented Reality Startups On The Logistics Industry

Is predictive maintenance suitable for your organization?

By maximizing the equipment’s reliability and minimizing the maintenance frequency, predictive maintenance can lead to substantial cost savings. These cost savings, however, come at a price. Some condition monitoring techniques and equipment are costly and require expert personnel for data analysis to be useful. Therefore, it should also be predetermined if predictive maintenance is plausible for the concerned application. PdM is not suitable for applications that do not serve a critical function and don’t have a failure mode which might be cost-effectively predicted.

How to implement predictive maintenance?

Some critical steps to be followed before implementing predictive maintenance are:

Analyzing the need and ROI cases.

Establishing definitions, realistic expectations, and building a case for PdM.

Educating the major stakeholders and training the maintenance staff and machine operators.

Complete an equipment inventory and assess the current conditions.

Affix relevant sensors and IIoT devices to the asset under consideration.

Develop a computerized maintenance management system (CMMS) and connect the IIoT devices to it.

Develop maintenance schedules accordingly.

Conclusion

Predictive maintenance presents you with the best time to work on an asset so that maintenance frequency is minimal and reliability is as high as possible while eliminating unnecessary costs. However, there are few disadvantages to predictive maintenance like high start-up costs and the need for specialized personnel.