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visual quality inspection for manufacturing

Why are Manual Inspection Methods Not Effective Nowadays?

Have you been considering manual Defect Detection in Manufacturing so far? Don’t you think it is high time to switch to automation for faster and better-quality products? You are most likely to fall behind in the competition if you keep putting most of your time and resources into manual testing that does not even guarantee accuracy in some cases.   Manual testing might work in some areas, but it is not preferred every time since it comes with various drawbacks such as:   Low Accuracy   Humans are prone to make errors, and manual testing is more likely to make more errors than an automated solution. For example, in the case of glass manufacturing, human eyes might miss dust particles in a hassle, or they miss subtle scratches appearing on the glass surface. As soon as they notice it, there are chances the defective pieces have already found their way ahead.  Mandatory Human Interactions   In current times, where social distancing is the topmost priority, we need to avoid human interactions as much as possible. Batch testing (executing a series of tests) is possible. However, in each test case execution, human interaction becomes inevitable. If you switch to a Defect Detection Computer Version, everything gets automatic and needs no/less human interference.   Demands More Time and Resources  To cover each application area, you need to perform more Manufacturing Defect Detection tests. Creating test cases and their respective execution eats up a great deal of human effort and time. Checking up on each product, and informing the concerned person to take relevant steps, and taking up the same tests, repeatedly, can be exhausting and time-consuming.  Impractical Data Collection   Unnecessary data collection is one of the biggest drawbacks of manual inspections, collecting and comparing substantial amounts of data is impractical without automation. Preparing reports on products and reporting them to the relevant personnel can be time-consuming and prone to errors.   How does an automated intelligent machine system help?  The demand for higher productivity and better-quality output within the manufacturing industry has demanded an upgraded production inspection. A Visual Inspection in Manufacturing is more beneficial; you can assure better and faster defect detection. A Machine Vision Inspection can offer tremendous benefits such as:    Boosts Manufacturing Output   With automatic vision inspection, you can detect defects faster and make necessary modifications, you no longer must put your time and efforts into manual testing and reporting, instead spend it on other productive tasks and get a boosted ROI. The inspection is done in one station and there is no need to set multiple inspection sections.   Minimizes Inspection Time   In visual quality inspection, everything is done by the machine itself so you do not have to fuss on its accuracy and speed. There is no doubt that the machine version surpasses human vision in every level provided by its speed, repeatability, and speed. Machine vision can easily identify small objects and detect defects with lesser errors and reliability. Machine vision can inspect thousands of products and their parts in a minute on a production line.   Better Perception   Machine vision has a high resolution depending upon the technology and hardware that are being used for the object’s illustration. Automatic Defect Detection in Manufacturing has a better spectrum of visual perception. Moreover, the system is more reliable and can be programmed as required.   Anywhere Deployment   Such systems can be deployed anywhere, even in locations that are hazardous for humans. It is best for manufacturing products or solutions that may cause serious health hazards or injuries to humans.   Production Optimization   With this technology, you can easily interpret defects and solve them in the closest time possible. Analyze first output and resolve the core issue of the quality defects. You may as well use the permit fixers to highlight defective components and effectively digitalize the defect reporting process.   Promotes the Desired Outcome   With a Machine Vision Inspection, you can optimize your products and workforce. You can also eliminate the need for sampling products while using this technology. Also, you can revise all the defective products from the data storage and identify the root cause of defeated data in real-time. Automating these activities can make your production goals easier to achieve.   Minimize Future Costs   You can easily cut costs with a digital quality inspection (including rejected or returned items that could be damaged due to shipment-oriented packaging defects). Remove them before getting shipped and improve the end customer’s product quality experience.  Final Words   Manual Defect Data Detection in Manufacturing has become outdated. Automating your quality inspection can eliminate all the issues arising under manual quality inspection. Trident Information Systems is one of the best Vision Quality Inspection service providers.  

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How smart manufacturing can optimize your factories for the new era

The focus of every industrial revolution has been increasing the productivity of production systems. The fourth industrial revolution is here, and it’s seeking to improve both production and management systems. Digital transformation driven by smart manufacturing (also known as Industry 4.0) is the basis of this latest one – creating opportunities to achieve levels of productivity and specialization not previously possible. Combining data generated through the Industrial Internet of Things (IIoT) and analytics creates a new set of capabilities known as predictive maintenance and quality. Fueled by smart manufacturing, these new capabilities are changing the way we do and see business, helping recognizing patterns and predicting failures or product quality issues before they happen. Introducing the new industrial IoT platform Most factories are composed of operation technology (OT) assets such as machines, equipment lines and robotic devices that aren’t always connected. The current trend is leaning toward smart manufacturing with a more IT-based factory floor to help save time, labor, cost and maintenance and upkeep. With OT and IT converging, the IIoT platform is emerging as a new, innovative concept for smart manufacturing with artificial intelligence (AI)-based technologies, including analytics, big data and cognitive manufacturing. Smart manufacturing can spur a new surge of manufacturing productivity. Targeting the pain points for key manufacturing personnel In order to understand the impact of Industry 4.0 solutions, we must examine the key people involved in all aspects of a factory. True transformation happens when all unique challenges and each pain point is targeted. Transforming your factory with a three-tiered architecture solution from IBM Keeping the needs of different types of workers in mind and using our extensive manufacturing experience, IBM developed a three-tiered distributed architecture to implement smart manufacturing more efficiently. The model addresses the autonomy and self-sufficiency requirements of each production site and balances the workload between the three tiers. Mapping IBM’s three-tiered architecture. Edge level. The most physical part of the factory where product-related activities are performed. Plant or factory level. Where plant and local activities are orchestrated and connected. Enterprise level. Where analysis of all levels of information happens, and information storage for visualization and analytics is provided. Leveraging the three architecture tiers to drive performance IBM offers a suite of enterprise asset management (EAM) solutions to help drive cost savings and operational efficiency across the factory value chain. The portfolio of EAM solutions from IBM analyzes a variety of information from workflows, context and the environment to drive quality and enhance operations and decision making. The portfolio of EAM solutions from IBM helps deliver a smart manufacturing transformation. Production quality insights use IoT and cognitive capabilities to sense, communicate and self- diagnose issues to optimize each factory’s performance and reduce unnecessary downtime. Insights help reduce unplanned downtime.

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Advancing Azure service quality with artificial intelligence: AIOps

We are going to share our vision on the importance of infusing AI into our cloud platform and DevOps process. Gartner referred to something similar as AIOps (pronounced “AI Ops”) and this has become the common term that we use internally, albeit with a larger scope. Today’s post is just the start, as we intend to provide regular updates to share our adoption stories of using AI technologies to support how we build and operate Azure at scale. Why AIOps? There are two unique characteristics of cloud services: The ever-increasing scale and complexity of the cloud platform and systems The ever-changing needs of customers, partners, and their workloads To build and operate reliable cloud services during this constant state of flux, and to do so as efficiently and effectively as possible, our cloud engineers (including thousands of Azure developers, operations engineers, customer support engineers, and program managers) heavily rely on data to make decisions and take actions. Furthermore, many of these decisions and actions need to be executed automatically as an integral part of our cloud services or our DevOps processes. Streamlining the path from data to decisions to actions involves identifying patterns in the data, reasoning, and making predictions based on historical data, then recommending or even taking actions based on the insights derived from all that underlying data.   Figure 1. Infusing AI into cloud platform and DevOps. The AIOps vision AIOps has started to transform the cloud business by improving service quality and customer experience at scale while boosting engineers’ productivity with intelligent tools, driving continuous cost optimization, and ultimately improving the reliability, performance, and efficiency of the platform itself. When we invest in advancing AIOps and related technologies, we see this ultimately provides value in several ways: Higher service quality and efficiency: Cloud services will have built-in capabilities of self-monitoring, self-adapting, and self-healing, all with minimal human intervention. Platform-level automation powered by such intelligence will improve service quality (including reliability, and availability, and performance), and service efficiency to deliver the best possible customer experience. Higher DevOps productivity: With the automation power of AI and ML, engineers are released from the toil of investigating repeated issues, manually operating and supporting their services, and can instead focus on solving new problems, building new functionality, and work that more directly impacts the customer and partner experience. In practice, AIOps empowers developers and engineers with insights to avoid looking at raw data, thereby improving engineer productivity. Higher customer satisfaction: AIOps solutions play a critical role in enabling customers to use, maintain, and troubleshoot their workloads on top of our cloud services as easily as possible. We endeavor to use AIOps to understand customer needs better, in some cases to identify potential pain points and proactively reach out as needed. Data-driven insights into customer workload behavior could flag when Microsoft or the customer needs to take action to prevent issues or apply workarounds. Ultimately, the goal is to improve satisfaction by quickly identifying, mitigating, and fixing issues. Figure 2. AI for Cloud: AIOps and AI-Serving Platform. AIOps Moving beyond our vision, we wanted to start by briefly summarizing our general methodology for building AIOps solutions. A solution in this space always starts with data—measurements of systems, customers, and processes—as the key of any AIOps solution is distilling insights about system behavior, customer behaviors, and DevOps artifacts and processes. The insights could include identifying a problem that is happening now (detect), why it’s happening (diagnose), what will happen in the future (predict), and how to improve (optimize, adjust, and mitigate). Such insights should always be associated with business metrics—customer satisfaction, system quality, and DevOps productivity—and drive actions in line with prioritization determined by the business impact. The actions will also be fed back into the system and process. This feedback could be fully automated (infused into the system) or with humans in the loop (infused into the DevOps process). This overall methodology guided us to build AIOps solutions in three pillars. Figure 3. AIOps methodologies: Data, insights, and actions. AI for systems Today, we’re introducing several AIOps solutions that are already in use and supporting Azure behind the scenes. The goal is to automate system management to reduce human intervention. As a result, this helps to reduce operational costs, improve system efficiency, and increase customer satisfaction. These solutions have already contributed significantly to the Azure platform availability improvements, especially for Azure IaaS virtual machines (VMs). AIOps solutions contributed in several ways including protecting customers’ workload from host failures through hardware failure prediction and proactive actions like live migration and Project Tardigrade and pre-provisioning VMs to shorten VM creation time. Of course, engineering improvements and ongoing system innovation also play important roles in the continuous improvement of platform reliability. Hardware Failure Prediction is to protect cloud customers from interruptions caused by hardware failures.  Microsoft Research and Azure have built a disk failure prediction solution for Azure Compute, triggering the live migration of customer VMs from predicted-to-fail nodes to healthy nodes. We also expanded the prediction to other types of hardware issues including memory and networking router failures. This enables us to perform predictive maintenance for better availability. Pre-Provisioning Service in Azure brings VM deployment reliability and latency benefits by creating pre-provisioned VMs. Pre-provisioned VMs are pre-created and partially configured VMs ahead of customer requests for VMs. As we described in the IJCAI 2020 publication, As we described in the AAAI-20 keynote mentioned above,  the Pre-Provisioning Service leverages a prediction engine to predict VM configurations and the number of VMs per configuration to pre-create. This prediction engine applies dynamic models that are trained based on historical and current deployment behaviors and predicts future deployments. Pre-Provisioning Service uses this prediction to create and manage VM pools per VM configuration. Pre-Provisioning Service resizes the pool of VMs by destroying or adding VMs as prescribed by the latest predictions. Once a VM matching the customer’s request is identified, the VM is assigned from the pre-created pool to the customer’s subscription. AI for DevOps AI can boost engineering productivity and help in shipping

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Visual Quality Inspection Proved as Gigantic Power in Manufacturing

[vc_row][vc_column][vc_row_inner][vc_column_inner width=”1/2″][vc_column_text] Focus on providing confidence on quality Quality assurance is paramount parts of the manufacturing process. It is the part which comes at the end of the production where the final product is inspect for defects and errors before selling it to the customers. This is important because not only it minimize the errors in production, but also prevents humans from items which can be potentially be perilous. Industries which are involved in producing end products such as Auto parts, mirrors, sanitary ware, Laminates, pharmaceuticals, consumer goods, beverages and foods etc. can immensely profit from visual quality inspection systems[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][vc_single_image image=”7014″ img_size=”full”][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner width=”1/2″][vc_single_image image=”7015″ img_size=”medium”][/vc_column_inner][vc_column_inner width=”1/2″][vc_column_text] How Do Visual Inspection Systems Work? Visual Quality Inspection Systems take use of digital sensors, RFID Tags and QR Code which are protected inside the cameras. These cameras use the optical sensors for capturing images which are then processed by computers in order to measure specific characteristics and parameters for decision making[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text] Benefits of Visual Inspection Systems Save time for Inspection and more on production number: After Visual quality inspection systems have been planned and tuned in to the manufacturing process, they can do immense of production checking in a really short amount of time as compared to human inspection. Accurate Inspection without any human interference: In human inspection systems, there’s always a great chance of errors and no matter how experienced and focused the employees are, these errors can never brought down to a negligible level. Human capabilities have limitations which Visual Quality Inspection systems haven’t. And this is how they eliminate the chances of error in inspection to a great extent and deliver a higher quality of products. Compatible with every use-case: Another benefit of Visual Quality Inspections is that in case production method changes, these systems also alter accordingly with great ease. Increase in Accuracy: They also improve the production efficiency. They can identify errors at a faster rate. Analysis of these remark defects can be made quickly and necessary corrections can be made immediately. Remote Access: Unlike humans, these systems can work nonstop for twenty-four hours. They can also be operated and programmed from a remote place.[/vc_column_text][vc_column_text]By adopting the visual inspection system in the manufacturing process, companies can boost production and also prevent the wastage which is generated by defected and faulty products. This will save not only the revenues, but also ensure complete customer satisfaction. If your unit makes products at a mass level and you are looking for a modern and reliable inspection system, then buy Trident- Visual Quality Inspection platform which is trusted by numbers of industrial units for its accuracy and reliability.[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row]

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