AI-based Visual Inspection and Integration with Deep Learning
Table of Content Why Vision-based inspection needs AI How Vision-based inspection can make good use of AI Using the Deep Learning Model Computer vision (also known as Machine Vision) is a growing field, and incorporating AI-based visual inspection is a great way to use the power of artificial intelligence. While human eyes may have difficulty detecting specific patterns, AI can recognize them. The advantages of AI extend beyond the cost savings to freeing up personnel for higher-value tasks. As component sizes and quality standards continue to tighten and production environments become more dynamic, manufacturers are turning to machine vision solutions to improve the quality and speed of their products. AI is becoming more commonplace in manufacturing environments, and the introduction of this technology in machine vision systems helps integrators stay competitive. Why Vision-based inspection needs AI Machine vision inspection solutions have long been at the forefront of industrial automation. Traditional machine vision analyzes images and photos to identify defects. While this helps reduce human error and increase productivity, this technology falls short of addressing more complex defects and adjusting to changing environments. But with the introduction of AI, manufacturers can use visual inspection with artificial intelligence-powered models to improve quality and reduce costs. Although human inspectors and rules-based machine vision still play a crucial role in visual inspection, sending out a single defective piece to a customer is not a good idea and could cost a company its reputation. AI-powered vision inspection systems identify manufacturing anomalies much faster than human inspectors. These AI solutions can be deployed quickly and customized to a specific scenario.They can also be used to inspect high-volume, complex parts and products. The advent of AI platforms such as Google Cloud’s Visual Inspection AI allows manufacturing companies to implement proof-of-concept (PoC) or deployable solutions in weeks rather than years. The benefits of AI-powered visual inspection solutions are numerous, and their potential is immense. How Vision-based inspection can make good use of AI Computer vision algorithms can help with visual inspection in a variety of settings, from industrial settings to infrastructure, in the following ways: A trained AI model needs a powerful computer to perform the inspection. GPU is necessary for real-time results. The performance of an inspection model depends on many factors, including the types of defects, image resolution, and lighting conditions. For existing applications, the embedded device mimics a camera. It then runs an AI algorithm over the camera feed to process. Then, the processed data to the inspection application over GigE Vision. Moreover, an embedded device can be programmed to save incoming images for offline training. This feature can be very useful when training AI systems. The process of deploying AI into a traditional computer vision inspection system is simplified with “no-code” AI software training packages. AI-powered vision-based inspection solutions can help reduce production costs and increase productivity by up to 90%. The AI-enabled machine vision system can alert employees to unsafe zones and flag mistakes. It can also measure cycle time, a key production index. It can also improve worker efficiency by monitoring employee position and behavior. The perks of incorporating AI in vision-based inspection seem to be unmatched, and its accuracy and efficiency can be increased with time since it feeds on new data and deep learning. If your business requires AI-based visual inspection solutions, look no further than Prescient Technologies. Prescient’s flagship product, iNetra, dwells extensively in smart vision and helps clients from different verticles address their vision-based inspection and smart surveillance requirements. Using the Deep Learning Model Machine vision and deep learning technology have combined to create a new way to inspect products and services. While machine learning is more appropriate for precision alignment, deep learning is a powerful tool for computer vision. Deep learning technology uses neural networks to mimic the human brain’s ability to learn by example. Integrating Deep Learning with Vision-based Inspection Artificial intelligence (AI) vision-based inspection systems are gaining ground in Production and Manufacturing. AI solutions for this task harness deep learning to automate inspections with high accuracy and improved decision-making processes. They can perform a multitude of tasks, from image classification to defect detection. Image classification models are based on a set of training and testing images. The training set contains images of various products that do not have defects, and the testing set includes images of products with defects. Using a CNN model for inspection allows for advanced learning with many defect images. By using this technique, AI vision systems can detect and classify defects in various environments. Deep learning models have become an essential part of inspection software. They are trained by analyzing thousands of images and gradually learn to detect significant deviations from the bolt standard appearance. Deep learning models can solve a range of inspection problems using a combination of tasks, such as Object Detection, semantic segmentation, image classification, and OCR models. If you’re looking for AI vision-based inspection, it’s a great time to explore these advanced technologies. If you’re looking for experienced professionals to take care of these advanced technologies for you, it’s a great time to contact Prescient technologies.
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What is CAD Automation and its Impact on Manufacturing
Running a manufacturing business is no child’s play. Amid cutthroat competition, manufacturers must develop innovative ways to stay afloat in the market by churning out well-made products, addressing rapid customer demands, boosting sales, etc. Often, time-consuming issues have been a setback for such businesses. The back and forth between engineering and sales teams have been a grueling, arduous task since creating CAD models and drawings takes meticulous work and a long time. However, CAD automation is a game-changer and, in all honesty, a lifesaver. CAD Automation denotes automated tools and processes to create CAD models, manuals, and setup CAD workflows. CAD automation adds value to an engineer’s craft by pushing innovation and also helps the sales team to provide accurate CAD data to boost sales. How CAD automation helps the manufacturing industry CAD automation can dramatically improve the productivity of a manufacturing organization by automating the design process. This is an essential advantage of CAD automation. It will enable your manufacturing business to improve its productivity and increase its profits. CAD automation makes it easier for manufacturers to produce high-quality products quickly. By eliminating the need for manual processes, manufacturing teams can concentrate on developing new products and improving customer satisfaction. CAD automation allows design engineers to focus on improving the manufacturing process, enabling businesses to become more cost-efficient and innovative. Manufacturers can also reduce the number of misunderstandings and delays in the design process. Therefore, CAD automation will help manufacturers achieve speed and accuracy through correct product logic. The use of CAD automation for manufacturing can reduce the number of errors by 80% or more. CAD software is also a valuable tool for reducing costs by increasing productivity and ensuring product quality. Manufacturing engineers will be able to see the design of the products and their functionality more quickly. Using 3D modeling for design simulations will also help manufacturers anticipate any potential problems with a product. Further, CAD automation can help manufacturers reduce waste and streamline design-to-production processes. In a recent study, a UK-based stainless steel retail furniture manufacturer improved customer satisfaction by incorporating an automated furniture configurator. Using this tool, they were able to generate 100% accurate sales quotes and documents. Inventory management is another critical element for smooth production flow, as an unavailability of certain parts can put the production on hold. In addition, a lack of timely replenishment of raw materials increases lead time. How CAD automation and CPQ collaborate CAD integration with CPQ (Configure, Price, Quote) solutions can streamline the sales process and make it easier for sales representatives to customize products for customers. Previously, sales reps had to take a customer’s specifications to the engineering department, which would generate a drawing, BOM, and quote. This process could take days or even weeks. Now, CAD automation enables the sales representative to manipulate the CAD model in real-time to address customers’ requests. The customer can go to a company website and customize their products using a visual configurator. One of the primary reasons a CPQ solution is valuable is that it helps manufacturers make decisions in minutes instead of hours. Having a single product data source can prevent costly mistakes, thereby increasing customer satisfaction. CAD automation can also be used to automate the creation of standardized products. Manufacturers can send CAD models directly to the production floor by using a product configurator. With the right CPQ system, the entire process can be automated, making it easy for salespeople to generate CAD drawings within minutes. Another advantage of CPQ software is that it can help elevate the manufacturing performance of a company. Its use frees up the engineering department from constant meetings and long email chains, allowing them to focus on product development. Sales reps can also generate CAD drawings without assistance, producing realistic quotes without guesswork or special skills. Engineers can focus on higher- value tasks because the CPQ software is automated. If you’re looking for a high-quality solution for your manufacturing business, CAD automation is the perfect solution. Prescient Technologies has extensive experience working with almost all CAD platforms and creating customized CAD workflows.
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Benefits of CAD Automation
Table of content CAD automation automates repetitive tasks CAD automation automates systems integration CAD automation helps in cost-cutting CAD automation boosts collaboration CAD automation helps Sales process CAD automation reduces the time We have read what CAD automation is and its primary contribution to the entire CAD model and product development workflow. CAD automation has brought immense benefits to the manufacturing sector (refer to What is CAD Automation and its impact on Manufacturing). But to say CAD automation has brought immense benefits to the manufacturing sector is an understatement since it is increasingly hard to find a manufacturing firm without automated workflow in place. Although listing out CAD automation’s contributions is a bit hard since different players have their own success stories with this technology, here are the primary benefits that CAD automation brings to the table: CAD automation automates repetitive tasks CAD automation is a powerful tool that can save time by automating repetitive tasks and events in design. CAD Automation is a great way to free up your design engineers’ time for more productive tasks. Whether designing a new building, designing a product, or developing a system, you can automate repetitive tasks with CAD automation. The process of CAD automation helps engineers focus on higher- level tasks. Examples of CAD automation software include feature catalogs and template designs. CAD automation automates systems integration CAD integration enables engineers to create 3D designs instantly, removing the need for engineering consultation. In addition, CAD integration lets customers see a 3D model of their product without requiring the engineer’s input. This advancement is the next step in the manufacturing industry’s evolution. Automating the design process will allow companies to move the configuration process along faster and ensure the products are appropriately designed. And, with today’s faster processors and improved graphics, CAD automation is becoming more popular. CAD automation helps in cost-cutting CAD automation helps to simplify the process of designing and manufacturing products. By automating the entire design process, CAD engineers can focus on innovation and cost-cutting. Additionally, CAD automation can prevent costly mistakes that may cause delays in production and customer returns. It can also empower your engineering team to produce high-quality products faster. CAD automation also makes your business more cost-efficient and innovative. CAD automation boosts collaboration CAD automation can increase collaboration. If you have an engineering team and a sales team, CAD automation can help you make better product designs more quickly. It can help you reduce your sales costs and streamline the entire sales and engineering process. Plus, CAD automation can be used for sales and engineering, so your teams can focus on higher-value work instead of pre-sales activities. It can also help you increase your collaboration and productivity by reducing errors. CAD automation helps Sales process CAD integration can accelerate the sales process. Sales associates can customize products by referring to the CAD model. Without CAD integration, the sales associate would have to go to the engineering department and request the design they needed. Once the engineering team had the requirements, they would create a drawing, BOM, and quote. The entire process could take days. By using CAD automation, the sales associates can make custom products in a matter of hours rather than days. CAD automation reduces the time CAD automation offers many benefits. For starters, it frees up engineers’ time for more critical problem- solving tasks. With such a solution, engineers can work on their most challenging projects instead of spending hours repeating the same steps over again. Additionally, according to research, it improves employee morale and the quality of their work. You may have reasons to implement CAD automation in system workflows, but we would love to hear all of it and provide our two cents to help you automate effectively. Contact us today to know more about our offers. Prescient’s product configurators offer you complete design automation to manage your product variants.
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Issues Faced in Industry 4.0
Table of content Issues Faced in Industry 4.0 The 21st century is the era of digital transformation where companies are trying to blend operations with automation information and data exchange. The amalgamation of cyber-physical systems, the Internet of Things (IoT), Artificial Intelligence, and cloud computing gave rise to a new generation known as Industry 4.0 or the 4th industrial revolution. The rise of Industry 4.0 and digital hegemony has changed the face of the manufacturing, industrial, transport, and service sector. All these divisions are giving rise to a new kind of workplace which is popularly called Smart Factories. Smart Factories are just digitalized, automated workspaces capable of accomplishing their assigned objectives with limited human intervention. A PWC survey suggests that 91% of industrial companies have invested in creating smart factories. Keeping the agendas and ROI on a clear viewpoint, companies now understand the long-term implications of such investment, and they are willing to be in the race putting time and work as much as possible. However, there are so many cases of businesses that stagger through the process despite setting the ball in motion, only to fail in a successful large-scale rollout. Why do such companies fail to effectively transform their digital, automated ambitions into a full-scale fruitful output? There are specific backlogs that hamper the smooth journey of a successful digitalized business. Very few have been able to use industry 4.0 to its true potential. Every journey is rife with hurdles; this is a part of every story. There are challenges to face, but there are ways to address & overcome these challenges. If the right step is not taken, the challenges will act like a monkey wrench thrown into a full throttle locomotive engine. Competition is good, and everyone should strive to stay ahead in the race these days. Let us check out some challenges that cause issues to a company’s successful implementation of industry 4.0. We will also look into the tips that can help overcome these challenges. Cyber threat and security: Cyber connectivity is one of the most prominent pillars of building an industry 4.0 standard workspace. Digitalization and interconnectivity are the prime features while connecting the entire system via the Internet of Things. It opens up the possibility of cyber-attacks and presents a cybersecurity challenge. A cyber attack can cripple your organization and your reputation as a trustworthy company. Cyber-attacks can be a nemesis unless you have a robust cyber security infrastructure in place, whether it is malware, ransomware, or a DDoS attack. The threat of cyber-attack is such that a Deloitte survey suggests 48% of respondents believe cyber- attacks will increase due to growing industry 4.0. To address this issue, ensure the following steps:Create an up-to-date inventory of digital assets and chalk up a schematic workflow of how the entire network works. It is will you in understanding existing loopholes. Build an authentication system to safeguard physical, digital, and workforce assets. Your organization must have a sound monitoring system to detect any anomaly and raise an alert. You should also have a dedicated team of experts to carry out rectification & maintenance tasks if needed. Build an effective risk management contingency plan in case the situation goes south. In such a way, you will have processes, instructions ready to recover lost data and IT assets. Usable data & its interpretation: Industry 4.0 has enabled data collection. The 21 st century is all about how to manipulate and leverage accumulated data. However, a lot of many businesses struggle to interpret such data. Understanding data can go a long way, like improving key performance indicators, understanding market & consumer trends, analyzing workforce, ROI forecast, etc. The correct evaluation data allows for the proper implementation of the right tech tools. Usually, business owners and manufacturers don’t know what to do with such a large amount of data and sit back wondering how to interpret them. Since data interpretation and implications are massive, business owners must use the right technologies to carry out such operations. AI is of great importance in this regard. Data collection can be automated, and machines can leverage data and give back valuable insights. Machines can perform calculations, predict probable scenarios on recorded history, simulate different situations to analyze the best possible outcome, and give clear output. Return on Investment (RoI): Every business ultimately strives for profit. RoI is a complicated subject that is intimidating for business owners and discourages them from stepping up further. Companies wish to look towards the future; they want to know what’s in store for them after an investment, whether it is worthy or not, and understandable. To address that, companies need to look further than just what the numbers predict. They must understand technology in terms of numbers and predictions and productivity, efficiency, & cost-cutting. There should be a system for detecting flaws and human factor errors. The focus should not only be on the proof of concept but also proof of value. It is essential to know that consumers see the value and not the journey a product or service goes through to reach them. There must be a dedicated CRM, Salesforce, Cloud service to understand the consumer point of view. A Deloitte survey has revealed that 25% of manufacturing companies have incorporated customer insights in their development and production processes. Up-to-date workforce technical skills: One must beg for a question, is the workforce well versed with digitalization? Businesses often face a problem with their crew that somehow fails to compliment the advantages industry 4.0 technology offers. Either they are not aware, or they do not want to familiarise themselves with ever-evolving tech. Understandably, there is a shortage of highly skilled employees who understand the ins and outs of these technologies. Still, companies can spare some capital to educate their employees to speed up the familiarization process. Businesses must look for employees who possess digital dexterity to understand digital systems and tools involved. A properly trained workforce can ensure the smooth functioning of operations, thereby productivity.
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Introduction to Industry 4.0
Backdrop Let’s admit it; the last three centuries have witnessed dramatic changes in global human living standards and development index, a lot more than the previous 1000 years of human history. The fact that technology and rapid industrialization could bring in such a massive transformation in the way humans carry out work or interact is astounding. We owe this transformation to the age of the industrial revolution.The Industrial revolution altered world trade & economic scenarios forever. Although we have read about the 18th-century industrial revolution in history class; how steam and water-powered engines and factories flipped the European and American economy & society for good; we should bear in mind that that was not the zenith of the revolution. In fact, according to Klaus Schwab in his book The Fourth Industrial Revolution, there are four distinct eras of the industrial revolution in history, including the one undergoing currently. This blog emphasizes the fourth one, which is popularly known as Industry 4.0. Before diving into Industry 4.0, we need to take a step back and revisit the previous eras of industrial changes. The First Industrial Revolution Everything started right here. The first industrial revolution began around the early 1700s when it was found that heating water produces steam which moves things with enough pressure. Then, around the beginning of 1760, the development of steam engines changed the face of locomotion. This newfound technology put a stop to animal and human labor-powered tools and machines. The Second Industrial Revolution This era appeared at the start of the 20th century, introducing assembly lines and steel to mass-produce products with more tenacity and durability. This era also saw the emergence of gas, oil, electricity as significant power sources to drive factories, modes of transport, and overall productivity. Add advanced communications such as the telephone and telegraph; the 2nd industrial revolution pretty much set the tone for the 21st-century world. The Third Industrial Revolution This is the post-WW II era, which begins in the 1950s. This age saw increased use of more digitally inclined technologies like computers, electronic devices, the internet, semiconductors. In addition, the digitization of factories helped automate the manufacturing process to a great extent, thereby relieving manual and analog dependence on manufacturers. The Fourth Industrial Revolution Experts suggest that this falls under the current era, which is the 21st century, characterized by increased digital technology, automation, and cyber-physical systems. Usage of automation, data collection, data analysis, physical-digital connectivity has made production far more efficient, smoother, and better than the previous systems. Thus, this era of the fourth industrial revolution is also known as Industry 4.0. What is Industry 4.0 To define Industry 4.0, we need to understand the concept that sums up this era. Nowadays, more and more domains are being interconnected via digital means, making it easier to relay & process system information. Moreover, since products and means of production are intertwined, they can communicate, enabling efficient output, smoother optimization, and value creation. Hence, Industry 4.0 can be defined as “a new age intelligent system setup between machines and processes which relies on information, data, and analysis to drive production and value.” Industry 4.0 had revolutionized the meaning of interconnectivity in epic proportions. It presents a more comprehensive, connected, and holistic approach to manufacturing and productivity. It encourages physical and digital medium overlap, allowing for better collaboration across various departments, partners, vendors, and domains. Since it has enabled automation to permeate every aspect of manufacturing, it provides for data generation and evaluation. This helps business owners to have a better understanding of their process. As a result, they can have better control on production. It allows them to leverage data to boost output and drive growth. Computers were first introduced in the third industrial revolution. Back then, computers were a new kind of technology for the already established factories. Now thanks to the new wave of the industrial revolution, computers encompass every field of industry. Thanks to the effective combination of computers with cyber-physical systems, Internet of Things (IoT), and Internet of Systems, intelligent factories have become a reality today. The development of intelligent factories means a lot of new benefits for the manufacturing industry. As machines keep getting smart, they can access more data to evaluate and give back customized solutions and help us make better decisions. Such advancement also reduced human involvement drastically, thereby reducing the chances of human errors and miscalculations. Industry 4.0 Principles Industry 4.0 strives to work on the principles below, and every business owner thinking of incorporating Industry 4.0 must consider these points. Interoperability: It is the ability to communicate across all platforms in the factory premises, from men to machines.Decentralization: It is the ability to carry out tasks via autonomous sub-systems with AI and cyber-physical systems.Real-time Analysis: It can collect and evaluate a large quantity of information via automated AI systems and monitor and optimize processes.Virtualization: It is the ability to create a virtual, digital copy of existing processes to facilitate simulation & testing.Scalability: It is the ability to be adaptive enough to customize itself based on the needs of the consumer market and scale technical prowess as per the technical necessities. Industry 4.0 technologies Since Industry 4.0 pertains to new-age technologies coupled with factories to facilitate high production and smart operation, here are the key players in this landscape: Internet of Things (IoT) IoT is probably the most important breed of technology to impact the industrial domain. IoT involves specific electronic devices connecting machines with other web-enabled devices to enable data collection in large quantities. This extensive collection of data can be analyzed, exchanged, and used for end-to-end decision-making. Cloud Computing Cloud computing is the keystone of Industry 4.0 architecture. Smart factories demand great connectivity and integration of various departments such as engineering, logistics, supply chain, sales & distribution, service. This is where cloud computing comes into play. A significant X factor of cloud computing is its ability to store a large quantity of information with minimum costs. This helps small and medium-sized companies in keeping, managing their data,
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Benefits of Industry 4.0
In the previous article, we read about what Industry 4.0 is, its basic principles, and the prime technologies that surround it. Industry 4.0 is another name for the fourth industrial revolution to run a recap, which is a continuation of the three earlier revolutions. Industry 4.0 is an umbrella tag that refers to immense changes sweeping across the industrial value chain system. These changes are driven by the rise of new-age technologies that offer a smoother, efficient way to organize, optimize, and manage all processes within a manufacturing factory ecosystem. It incorporates new production & communication tools to connect every aspect into a single well-oiled digital system. In addition, it adopts unique methods to streamline operations across various domains within a production cycle. As industry 4.0 brings in uncapped changes in every sector, it also plays a significant role in the ROI factor in businesses. Since it aims to merge physical and digital systems into unified cyber-physical machinery, it has dropped considerable alterations in the way productions roll. We have already seen how Industry 4.0 aims to mold automation, big data, AI with machines. Here in the blog, we will go through all the points highlighting the benefits of incorporating Industry 4.0 principles. Benefits of Industry 4.0 Industry 4.0 in business processes and production lines may vary from one entity to another. It depends on which technologies are prioritized, utilized more, and how it affects the existing workforce & working processes. Without going into complications, here are the commonly observed plus points that a business benefits from Industry 4.0. Higher Production & improved Productivity Since Industry 4.0 results in smart factories, it assures higher, faster production with improved quality of productivity. As a result, one can allocate resources more effectively. Additionally, the production line faces less downtime because of advanced automated monitoring and error-free decision-making capability. Ultimately, it improves the general Overall Equipment Effectiveness. One in six businesses also expects a 20% sales rise owing to Industry 4.0 application. Optimization A great side of Industry 4.0 is that it permits optimization. Since it involves automation, there is scope for self-optimization, leading to zero downtime of factory machines. Optimization drives better maintenance of any equipment by having the needed resources at the right location and time. The ability to harness production capacity consistently is a better option than extensive downtime or an overhaul. Customization Industry 4.0 requires every domain to stay interlinked, which is means intelligent factories and the Industrial Internet of Things (IIoT) must be interconnected and constantly in a loop. It provides business owners and manufacturers with the current market trends, eliminating any middle man to bridge these two entities. Such end-to-end constant connections allow for smooth scaling of production up or down depending upon market demand. Higher Efficiency Efficiency is a parameter that covers every aspect of a production line. As mentioned before, higher efficiency means less downtime, faster output, better finish, faster changeovers, automatic tracing, and automated report generation. In addition, higher efficiency guarantees better turnover and customizing opportunities depending on the consumer market. Increased collaboration and knowledge share We know how traditional manufacturing factories operate. They work in close-knit units where one unit is unlikely to come in touch with the other, thus minimizing knowledge share. It isn’t the case with Industry 4.0 laden smart factories. The cyber-physical system ensures every production line, from the factory floor to business process to sales & distribution, is well connected and updated should there be any new revelation. It surpasses location, time zone, platform constraints. Picture this; if one smart sensor pulls in unique insight and wants to update the system about it, the automated process will make sure it does without any human intervention. Agility and Flexibility A significant benefit of Industry 4.0 is that it enables easy scaling of production based on requirements. Thus, one can introduce new products based on market demand, increase or decrease output and make new opportunities. Ease of Compliance Since industry compliance is a great necessity for any business, particularly consumable items such as pharmaceuticals and food products, automating the complaint process goes a long way than a manual process.Industry 4.0 makes it possible to automate compliance actions, thereby easing the load off human employees. Quality Customer Service Industry 4.0 also presents ample chances to improve the customer experience. For example, an automated supply chain will highlight the products and their availability in the supply chain without having the human workforce keep a tab in such a painstaking task.Automated report generation helps giving sales reports, market analysis, and customer feedback for the business owner to reassess his resources and business strategies. Automated tracking and tracing help keep an eye on the journey chain of any product. The best part is IoT. Business owners can create AI chatbots to address customer grievances & queries without any human involvement. All these automated operations generate data and information which can be analyzed, manipulated further for production & process enhancement. Cost Reduction Setting up a smart factory laden with Industry 4.0 tech is not child’s play. “Rome was not built in a day,” and so is a smart factory. Moreover, it requires a considerable investment which is the upfront costs. That being said, the returns of such investment are wholesome if done correctly. Moreover, since implementing Industry 4.0 involves laying down system integration, automation, big data management, intelligent surveillance, and AI, all these technologies play a huge role later on in terms of cost savings. Reason being: Any business owner will agree on the perks of Industry 4.0 in terms of the cost factor. Increased Innovation Industry 4.0 provides excellent scope for innovation. Whether it is manufacturing production line, supply distribution chain, or business domains, the 4.0 techs present a good amount of knowledge that can be harnessed to improve product quality or bring up a new product itself. Higher revenues and profits A business owner’s prime goal is to reap maximum profits with minimum expenditure involved. Industry 4.0 provides the chance to reap those
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New Product Development: Desktop tool vs on Cloud?
Desktop-based product or Cloud-based product? When developing a new product it is always a central question and now with more and more traditional desktop products are moving to the cloud it makes a lot of sense to give this question serious consideration. This article tries to go into details of various aspects that one has to weigh while making this decision. When we trace the origins of the Computer-Aided Design system (CAD) back in the middle of the 20th century, a mainframe was supposed to be the hardware. The software was primarily built by large corporations that could afford to operate mainframes and develop their CAD. With time, the mainframe was supplanted by minicomputers, and CAD became commercially available with a much affordable price tag. Later on, evolution in hardware brought about the microcomputer. Personal Computers and Unix workstations grew in popularity with companies and independent vendors, professionals alike. A remarkable aspect was that CAD systems could ride upon the local processing power of the PC without the need for a centralized server. All data storage was done centrally in the case of mainframes and minicomputers. An operator could log on and access data from any terminal. On the contrary, everything was managed locally in case of microcomputers hence the data was difficult to access unless one had his dedicated PC. To counter this issue, dedicated corporations introduced client/server architectures to facilitate local processing and centralized data storage. Nowadays, companies either manage their hardware and software on their premises, or offload everything to the cloud, or maybe somewhere in between. The new nomenclature was introduced to make distinctions: either “on-premises” (also called as “on-premise” and “on-prem”) or for true cloud-native solutions, it is “Software-as-a-Service” (SaaS). There are many other hybrid solutions, but this article solely focuses on these two aspects. For many design decisions, the most important thing is to know the requirements. In the end, we need to solve a given problem as efficiently as possible. One of the most debated topics in the world of Product development is: Whether to go for a desktop or a web-based solution for New Product Development. Now let’s look at the most common non-functional requirements, which play a role in the design choice between desktop and web: Deployment aka Set-up effort: Deployment refers to how easy and fast one can set-up the required tooling and the runtime for executing a system. Usually this is mainly referring to the developer tooling and its runtime(s) since it needs to be repeated for every developer. Portability: This refers to how difficult is it to port a tool to another platform/hardware. The typical case is accessing all development resources from any platform, e.g. also on your mobile device.Performance and responsiveness: Performance and responsiveness refer to how a tool performs and its responsiveness to users or functionalities. Usability: Usability refers to the level to which a software can be used by specified users to achieve specific project goals with accuracy, effectiveness, efficiency, and satisfaction. Online Data Storage: Data storage for a Cloud-based app is typically done on cloud-based servers. It makes it very easy for the users to access this data from anywhere using any device. Collaboration: A cloud-based product is more suitable for collaborative development. Cost: Cost is probably one of the most important criteria to consider. The cost takes to form an Integrated Development Environment (IDE), tooling, extension, or the required development runtime. Let us understand these in some detail now. 1.Deployment Deployment is probably the most prominent advantage that is advertised for web-based solutions. The intention is to simply log into a system via a browser and start coding without installing anything specific. Further, you do not need to install any updates anymore, as they are applied centrally. The first interesting aspect of this is how much time you can save with improving the installability. This is connected to the number of developers that you have on-board to use the tooling and the number of people who are occasionally using those tools. Further, it plays a role in how long a developer would use the tool after installation, the shorter the usage the more significant the set-up time would be. One of the important aspects related to installability that needs consideration is updatability. While of course an update to the tooling is hopefully not the same as installing it from scratch, most considerations we had for the installability should be applied for the update case as well. 2 Portability Portability is the second most advantage of a cloud-based solution over a desktop-based. Portability is considered as the ability to access the tool from anywhere on any device. It facilitates access to tooling and runtime through any device with a browser. As a result, you can ideally fulfill your development use case at any location, even from a mobile device. A disadvantage of a pure cloud-based solution is that they often rely on a constant internet connection. While this issue becomes less and less relevant, at least it must be considered. Some cloud-solutions already provide a good work-around, e.g. the offline mode of Google Mail. 3 Performance Performance is a very interesting requirement to consider. In contrast to deployment and portability, there is no clear winner between desktop-based and cloud-based tooling. One can find valid arguments for both to be superb performers. The major reason for this tie is the fact that we have to consider the specific use case when talking about performance. While writing code, engineers need fast navigation, key bindings, and coding features. Although IDEs have caught up a lot in recent years, a desktop tool is typically more performant for those local use cases. However, in cases such as compiling, a powerful cloud-instance can certainly compile a project faster than a typical laptop. 4 Usability Usability also doesn’t have a clear winner in the comparison between desktop-based to cloud-based IDEs. While advocates of both platforms would claim a clear advantage, this is a matter of personal taste. Nowadays, web technologies
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Optimization Problems and Techniques
Table of content Optimization Problems Linear and Quadratic programming Types of Optimization Techniques When discussing the mathematics and computer science stream, optimization problems refer to finding the most appropriate solution out of all feasible solutions. The optimization problem can be defined as a computational situation where the objective is to find the best of all possible solutions. Using optimization to solve design problems provides unique insights into situations. The model can compare the current design to the best possible and includes information about limitations and implied costs of arbitrary rules and policy decisions. A well-designed optimization model can also aid in what-if analysis, revealing where improvements can be made or where trade-offs may need to be made. The application of optimization to engineering problems spans multiple disciplines. Optimization is divided into different categories. The first is a statistical technique, while the second is a probabilistic method. A mathematical algorithm is used to evaluate a set of data models and choose the best solution. The problem domain is specified by constraints, such as the range of possible values for a function. A function evaluation must be performed to find the optimum solution. Optimal solutions will have a minimal error, so the minimum error is zero. Optimization Problems There are different types of optimization problems. A few simple ones do not require formal optimization, such as problems with apparent answers or with no decision variables. But in most cases, a mathematical solution is necessary, and the goal is to achieve optimal results. Most problems require some form of optimization. The objective is to reduce a problem’s cost and minimize the risk. It can also be multi-objective and involve several decisions. There are three main elements to solve an optimization problem: an objective, variables, and constraints. Each variable can have different values, and the aim is to find the optimal value for each one. The purpose is the desired result or goal of the problem. Let us walk through the various optimization problem depending upon varying elements. Continuous Optimization versus Discrete Optimization Models with discrete variables are discrete optimization problems, while models with continuous variables are continuous optimization problems. Constant optimization problems are easier to solve than discrete optimization problems. A discrete optimization problem aims to look for an object such as an integer, permutation, or graph from a countable set. However, with improvements in algorithms coupled with advancements in computing technology, there has been an increase in the size and complexity of discrete optimization problems that can be solved efficiently. It is to note that Continuous optimization algorithms are essential in discrete optimization because many discrete optimization algorithms generate a series of continuous sub-problems. Unconstrained Optimization versus Constrained Optimization An essential distinction between optimization problems is when problems have constraints on the variables and problems in which there are constraints on the variables. Unconstrained optimization problems arise primarily in many practical applications and the reformulation of constrained optimization problems. Constrained optimization problems appear in applications with explicit constraints on the variables. Constrained optimization problems are further divided according to the nature of the limitations, such as linear, nonlinear, convex, and functional smoothness, such as differentiable or non-differentiable. None, One, or Many Objectives Although most optimization problems have a single objective function, there have been peculiar cases when optimization problems have either — no objective function or multiple objective functions. Multi-objective optimization problems arise in engineering, economics, and logistics streams. Often, problems with multiple objectives are reformulated as single-objective problems. Deterministic Optimization versus Stochastic Optimization Deterministic optimization is where the data for the given problem is known accurately. But sometimes, the data cannot be known precisely for various reasons. A simple measurement error can be a reason for that. Another reason is that some data describe information about the future, hence cannot be known with certainty. In optimization under uncertainty, it is called stochastic optimization when the uncertainty is incorporated into the model. Optimization problems are classified into two types: Linear Programming: In linear programming (LP) problems, the objective and all of the constraints are linear functions of the decision variables. As all linear functions are convex, solving linear programming problems is innately easier than non- linear problems. Quadratic Programming: In the quadratic programming (QP) problem, the objective is a quadratic function of the decision variables, and the constraints are all linear functions of the variables. A widely used Quadratic Programming problem is the Markowitz mean-variance portfolio optimization problem. The objective is the portfolio variance, and the linear constraints dictate a lower bound for portfolio return. Linear and Quadratic programming We all abide by optimization since it is a way of life. We all want to make the most of our available time and make it productive. Optimization finds its use from time usage to solving supply chain problems. Previously we have learned that optimization refers to finding the best possible solutions out of all feasible solutions. Optimization can be further divided into Linear programming and Quadratic programming. Let us take a walkthrough. Linear Programming Linear programming is a simple technique to find the best outcome or optimum points from complex relationships depicted through linear relationships. The actual relationships could be much more complicated, but they can be simplified into linear relationships.Linear programming is a widely used in optimization for several reasons, which can be: Quadratic Programming Quadratic programming is the method of solving a particular optimization problem, where it optimizes (minimizes or maximizes) a quadratic objective function subject to one or more linear constraints. Sometimes, quadratic programming can be referred to as nonlinear programming. The objective function in QP may carry bilinear or up to second-order polynomial terms. The constraints are usually linear and can be both equalities and inequalities. Quadratic Programming is widely used in optimization. Reasons being: Types of Optimization Techniques There are many types of mathematical and computational optimization techniques. An essential step in the optimization technique is to categorize the optimization model since the algorithms used for solving optimization problems are customized as per the nature of the problem. Integer programming, for example, is a form of mathematical programming. This technique can be traced back to Archimedes, who first described the problem of determining the composition of a herd of cattle. Advances in computational codes and theoretical research
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What is Digital Image Processing
Table of content Types of Image Processing Digital Image Processing and how it operates Uses of Digital Image Processing Previously we have learned what visual inspection is and how it helps in inspection checks and quality assurance of manufactured products. The task of vision-based inspection implements a specific technological aspect with the name of Digital Image Processing. Before getting into what it is, we need to understand the essential term Image Processing. Image processing is a technique to carry out a particular set of actions on an image to obtain an enhanced image or extract some valuable information. It is a sort of signal processing where the input is an image, and the output may be an improved image or characteristics/features associated with the same. The inputs to this process are either a photograph or video screenshot and these images are received as two-dimensional signals. Image processing involves three steps:Image acquisition: Acquisition can be made via image capturing tools like an optical scanner or with digital photos. Image enhancement: Once the image is acquired, it must be processed. Image enhancement includes cropping, enhancing, restoring, and removing glare or other elements. For example, image enhancement reduces signal distortion and clarifies fuzzy or poor-quality images. Image extraction: Extraction involves extracting individual image components, thus, producing a result where the output can be an altered image. The process is necessary when an image has a specific shape and requires a description or representation. The image is partitioned into separate areas and labeled with relevant information. It can also create a report based on the image analysis. Basic principles of image processing begin with the observation that electromagnetic waves are oriented in a horizontal plane. A single light pixel can be converted into a single image by combining those pixels. These pixels represent different regions of the image. This information helps the computer detect objects and determine the appropriate resolution. Some of the applications of image processing include video processing. Because videos are composed of a sequence of separate images, motion detection is a vital video processing component. Image processing is essential in many fields, from photography to satellite photographs. This technology improves subjective image quality and aims to make subsequent image recognition and analysis easier. Depending on the application, image processing can change image resolutions and aspect ratios and remove artifacts from a picture. Over the years, image processing has become one of the most rapidly growing technologies within engineering and even the computer science sector. Types of Image Processing Image processing includes the two types of methods:Analogue Image Processing: Generally, analogue image processing is used for hard copies like photographs and printouts. Image analysts use various facets of interpretation while using these visual techniques. Digital image processing: Digital image processing methods help in manipulating and analyzing digital images. In addition to improving and encoding images, digital image processing allows users to extract useful information and save them in various formats. This article primarily discusses digital image processing techniques and various phases. Digital Image Processing and how it operates Digital image processing requires computers to convert images into digital form using the digital conversion method and then process it. It is about subjecting various numerical depictions of images to a series of operations to obtain the desired result. This may include image compression, digital enhancement, or automated classification of targets. Digital images are comprised of pixels, which have discrete numeric representations of intensity. They are fed into the image processing system using spatial coordinates. They must be stored in a format compatible with digital computers to use digital images. The primary advantages of Digital Image Processing methods lie in their versatility, repeatability, and the preservation of original data. Unlike traditional analog cameras, digital cameras do not have pixels in the same color. The computer can recognize the differences between the colors by looking at their hue, saturation, and brightness. It then processes that data using a process called grayscaling. In a nutshell, grayscaling turn RGB pixels into one value. As a result, the amount of data in a pixel decreases, and the image becomes more compressed and easier to view. Cost targets often limit the technology that is used to process digital images. Thus, engineers must develop excellent and efficient algorithms while minimizing the number of resources consumed. While all digital image processing applications begin with illumination, it is crucial to understand that if the lighting is poor, the software will not be able to recover the lost information. That’s why it is best to use a professional for these applications. A good assembly language programmer should be able to handle high-performance digital image processing applications. Images are captured in a two-dimensional space, so a digital image processing system will be able to analyze that data. The system will then analyze it using different algorithms to generate output images. There are four basic steps in digital image processing. The first step is image acquisition, and the second step is enhancing and restoring the image. The final step is to transform the image into a color image. Once this process is complete, the image will be converted into a digital file. Thresholding is a widely-used image segmentation process. This method is often used to segment an image into a foreground and an object. To do this, a threshold value is computed above or below the pixels of the object. The threshold value is usually fixed, but in many cases, it can be best computed from the image statistics and neighbourhood operations. Thresholding produces a binary image that represents black and white only, with no shades of Gray in between. Digital image processing involves different methods, which are as follows:Image Editing: It means changing/altering digital images using graphic software tools.Image Restoration: It means processing a corrupt image and taking out a clean original image to regain the lost information.Independent Component Analysis: It separates various signals computationally into additive subcomponents.Anisotropic Diffusion: This method reduces image noise without having to remove essential portions of the image.Linear Filtering: Another digital image processing method is about processing time-varying input signals and generating output signals.Neural Networks: Neural networks
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Image Processing Algorithms based on usage
There are many ways to process an image, but they all follow a similar pattern. First, an image’s red, green, and blue intensities are extracted. A new pixel is created from these intensities and inserted into a new, empty image at the exact location as the original. In addition, grayscale pixels are created by averaging the intensities of all pixels. Afterward, they can be converted to black or white using a threshold. Edge Detection The first thing to note about Canny edge detectors is that they are not substitutes for the human eye. The Canny operator is used to detect edges in different image processing algorithms. This edge detector uses a threshold value of 80. Its original version performs double thresholding and edge tracking through hysteresis. During double thresholding, the edges are classified as strong and weak. Strong edges have a higher gradient value than the high threshold while weak edges fall between the two thresholds. The next phase of this algorithm involves searching for all connected components and selecting the final edge based on the presence of at least one strong edge pixel. Another improvement to the Canny edge detector is its architecture and computational efficiency. The distributed Canny edge detector algorithm proposes a new block-adaptive threshold selection procedure that exploits local image characteristics. The resulting image will be faster than the CPU implementation. The algorithm is more robust to block size changes, which allows it to support any size image. A new implementation of the distributed Canny edge detector has been developed for FPGA-based systems. Object localization The performance of different image processing algorithms for object localization depends on the accuracy of the recognition. While the HOG and SIFT methods use the same dataset, region-based algorithms improve the detection accuracy by more than twofold. The region-based algorithms use a reference marker to enhance matching and edge detection. They use the accurate coordinates in the image sequence to fine-tune the localization process. A geometry-based recognition method eliminates false targets, improves precision, and provides robustness. The ground test platform is already established and has improved object localization. It can now detect an object with one-tenth of pixel precision. This embedded platform can process an image sequence at 70 frames per second. These works were conducted to make the vision-based system more applicable in dynamic environments. However, the subpixel edge detection method is quite time-consuming and should only be used for fine operations. Among the popular object detection methods, the Histogram of Oriented Gradients (HOG) was the first algorithm developed. However, it is time-consuming and inefficient when applied to tight spaces. HOG is recommended to be the first method when working in general environments but is ineffective for tight spaces. However, it has decent accuracy for pedestrian detection due to its smooth edges. In addition to general applications, HOG is also suitable for object detection in video games. YOLO is a popular object detection algorithm and model. It was first introduced in 2016, followed by versions v2 and v3. However, it was not upgraded during 2018 and 2019. Three quick releases of YOLO followed in 2020. YOLO v4 and PP-YOLO were released. These versions can identify objects without using pre-processed images. The speed of these methods makes them popular. Segmentation There are various image processing algorithms available for segmentation. These algorithms use the features of the input image to divide the image into regions. A region is a group of pixels with similar properties. Usually, these algorithms use a seed point to start the segmentation process. The seed point may be a small area of the image or a larger region. Once this segmentation is complete, the algorithm adds or removes pixels around it until it merges with other regions. Discontinuous local features are used to detect edges, which define the boundaries of objects. They work well when the image has few edges and good contrast but are inefficient when the objects are too small. Homogeneous clustering is another method that divides pixels into clusters. It is best suited for small image datasets but may not work well if the clusters are irregular. Some methods use the histogram to segment objects. In other techniques, pixels may be grouped according to common characteristics, such as the intensity of color or shape. These methods are not limited to color and may use gradient magnitude to classify objects. Some of these algorithms also use local minima as a segmentation boundary. Moreover, they have based on image preprocessing techniques, and many of them use parallel edge detection. There are three main image segmentation algorithms: spatial domain differential operator, affine transform, and inverse-convolution. A popular implementation of image segmentation is edge-based. It focuses on the edges of different objects in an image, making it easier to find features of these objects. Since edges contain a large amount of information, this technique reduces the size of an image, making it easier to analyze. This method also identifies edges with greater accuracy. The results of both of these methods are highly comparable, although the latter is the more complex approach. Context navigation Current navigation systems use multi-sensor data to improve localization accuracy. Context navigation will enhance the accuracy of location estimates by anticipating the degradation of sensor signals. While context detection is the future of navigation, it is not yet widely adopted in the automotive industry. While most vision-based context indicators deal with place recognition and image segmentation, only a few are dedicated to context-aware navigation. For example, a vehicle in motion can provide information about its surroundings, such as signal quality. However, this information is not widely used in general navigation. Only a few works have focused on context-aware multi-sensor fusion. In addition to addressing these challenges, future research should identify and analyze the best algorithm for a particular situation. To detect environmental contexts, multi-sensor solutions are needed. GNSS-based solutions can only detect the context of one area, and the underlying data is not reliable enough to extract every context of interest. Other data types, such as vision-based context indicators, are needed for
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