Tag: industry 4.0

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OI Session:Leveraging AI in Manufacturing Sector

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Events

OI Session:Leveraging AI in Manufacturing Sector

OI session, AI in Manufacturing, was held on October 28th, the leaders discussed how artificial intelligence (AI) is drastically changing the manufacturing industry by improving operating efficiency, anticipating equipment problems, and streamlining production processes. The several uses of artificial intelligence (AI) in manufacturing, with particular attention paid to generative AI, predictive maintenance, and the combination of AI with information technology (IT) and operational technology (OT) were discussed. The discussion also included obstacles to the adoption of AI, such as moral dilemmas and the requirement for human participation in decision-making.

The leaders that participated at the event,are:

•              Jemin Tanna, Digitization R&D Head at Siemens Industrial Automation, Generative AI and Startup Focus

•              Achyut Chandra, Manager and Lead at HCL’s Open Innovation vertical, integrating startup solutions into large enterprises

•              Harini Gorla, Associate Director / Senior Principal Enterprise Architect, GSK

•              Ramachandran S, Principal Consultant in the thought leadership unit at Infosys Knowledge Institute, leading engineering, manufacturing, sustainability, and energy transition initiatives

This session also featured exclusive startup pitches from the country’s hottest innovators in the field.

•              ⁠Energy ETA: An IoT-AI enabled platform that combines proprietary hardware, communication, and software to deliver reliable, secure, and sustainable digitization, Founder- Jagjit Singh

•              ⁠Visionbot: Pioneers of plug-and-play visual inspection, driving unmatched efficiency and accuracy for businesses; Founders – Amit C.  Prabhu Prakash  Michael McCoy

•              ⁠SyskeyOT Cybersecurity: Purpose-built OT/ICS cybersecurity, safeguarding critical assets with seamless protocol integration and enhanced visibility, Founder- Rajendran Chellamuthu

The Role of AI in Revolutionizing Manufacturing

Introduction

The use of AI technology is poised to bring about a significant change in the manufacturing sector. AI is expected to boost the global manufacturing industry by about $4 trillion a year by 2030.

Key Applications of AI in Manufacturing

1. Predictive Maintenance

Predictive maintenance is made possible by AI technologies, which analyze data from operations and equipment to anticipate breakdowns before they happen. This strategy enables businesses to reduce maintenance expenses and downtime, which eventually results in significant savings. For supply chain inefficiencies like the bullwhip effect to be accurately identified, data from several sources must be integrated.

2. Generative AI in Design

Manufacturers may produce numerous design iterations that strike a balance between performance and aesthetics because to generative AI’s significant advantages in product design. Demand forecasting and product end-of-life management, including robotic recycling, are just a few of the operations that this technology may help with. However, copyright and ethical issues are brought up by the use of generative AI, which calls for human oversight.

3. AI and Automation Integration

The efficiency of manufacturing is increased by combining automation technologies with AI. Finding appropriate AI solutions requires a cooperative strategy that encourages departmental cooperation. Applications of AI can be found in the aftermarket industry and prototype creation, offering chances for cost savings and increased operational effectiveness.

Challenges of AI Adoption

1. Ethical and Legal Concerns

AI adoption in manufacturing is hampered by issues with prejudice, ethics, and intellectual property rights. Fostering a culture of responsible AI use requires addressing these problems. In the age of Industry 5.0, where AI technologies should support human labor rather than replace it, the human element is still crucial.

2. Skills Gap

There is a skills gap in the manufacturing workforce as a result of the quick development of AI technologies. Upskilling staff members to improve their capacity to collaborate with AI systems must be a top priority for businesses. Training in AI that prioritizes explainability and the democratization of AI across all organizational activities should be part of this reskilling initiative.

Strategies for Successful Implementation

1. Focus on Measurable Outcomes

Instead than just implementing various AI models, organizations should focus on attaining quantifiable results. This emphasis will guarantee that AI projects complement corporate goals and yield observable advantages.

2. Collaboration with Startups

Established manufacturers can investigate novel AI technologies and jointly create value by collaborating with creative startups. Specific issue statements should be the focus of collaborative partnerships so that each party can contribute as much as possible and get as much information as possible.

3. Bridging IT and OT

Effective use of AI in manufacturing requires the confluence of IT and OT. A more unified approach to AI deployment will be made possible by this combination, which will also improve cybersecurity and operational effectiveness.

Success Stories

Leading the way in offering AI-powered manufacturing solutions are a number of firms. Businesses that specialize in cybersecurity for OT environments, for example, are tackling security flaws brought on by the confluence of IT and OT. To protect industrial networks, they offer asset detection solutions and log management systems.

Additionally, operational procedures are being optimized through the deployment of AI models that apply reinforcement learning, which increases energy savings and boosts overall efficiency.

Takeaway

AI has the ability to completely transform industry by increasing output, cutting waste, and stimulating creativity. However, resolving ethical issues, closing the skills gap, and guaranteeing that human expertise continues to play a crucial role in decision-making are all necessary for the successful deployment of AI technologies. To fully utilize AI and bring about significant change in the sector, manufacturers must place a high priority on cooperation, ongoing education, and strategic alliances.

By doing these things, businesses can make sure they stay viable and competitive in a world that is becoming more automated and AI-driven.

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Applied Innovation

How Digital Platform for Smarter End-to-End Production is Revolutionizing Manufacturing

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Applied Innovation

How Digital Platform for Smarter End-to-End Production is Revolutionizing Manufacturing

In the ever-changing industrial scene, businesses are embracing cutting-edge technology to turn old processes into smarter, more efficient, and highly linked systems. Adopting a digital platform that enables smart end-to-end production is a critical component of this shift. This technique is gaining traction across sectors, transforming how goods are planned, produced, and brought to market.

Smart End-to-End Manufacturing:

Smart end-to-end production is analogous to upgrading a typical plant with cutting-edge technology. It entails integrating sophisticated technology throughout the full product lifetime, from design to production, to provide a streamlined and data-driven process.
These alterations are not restricted to a single stage, but rather affect all aspects of production, making the entire process from concept to product more efficient, adaptable, and responsive to market needs.

The Function of a Digital Platform:

The digital platform is at the core of this transition, serving as the nerve centre for orchestrating a symphony of technological innovations. This platform provides a holistic solution by bringing together numerous digital tools and services to simplify the complex procedures involved in manufacturing.

Microservices: The Specialised Workforce.

Microservices are critical components of the digital platform, similar to specialised workers in a factory. Each microservice is focused on a single activity, such as design, procurement, quality control, or another component of the manufacturing process. Microservices, like a group of pals working together to build distinct portions of a Lego castle, provide efficiency and specialisation in digital product development.

Flexibility and Adaptability

The digital platform’s success stems from its capacity to adapt and grow swiftly. A Lego castle may be updated without having to rebuild the entire structure, while a digital platform with microservices allows for simple upgrades and modifications without disturbing the entire production process. This flexibility is critical for responding to market developments and client requests in real time.

Improving Collaboration and Connectivity:

A digital platform fosters cooperation and connectedness, similar to friends working together on a common cause. It allows for communication between multiple phases of production, resulting in a unified and synchronised operation. Real-time updates and tracking provide stakeholders access into the manufacturing process, allowing them to make educated decisions quickly.

Empowering Manufacturers of Every Size:

One of the most impressive characteristics of this digital transition is that it is open to manufacturers of all sizes. Companies, whether huge enterprises or fast-growing startups, may compete in the dynamic and demanding industrial industry by using the potential of a digital platform.

The emergence of digital platforms for smart end-to-end production heralds a new era in the business. As technology advances, organisations gain from enhanced efficiency, flexibility, and cooperation. While many organisations are adapting to these changes, it is evident that the digital transformation of manufacturing is more than a fad; it is a fundamental movement towards a more intelligent and responsive future.

For more in-depth information, write to us at open-innvator@quotients.com and embark on a journey of manufacturing excellence.

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Applied Innovation Industry 4.0 Uncategorized

How XR Technology is transforming the Maintenance and Repair Industry

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Applied Innovation Industry 4.0 Uncategorized

How XR Technology is transforming the Maintenance and Repair Industry

Everyone is raving about the new, intriguing technology known as XR. But what does XR actually imply, and when can this technology be used? Cross reality, also known as expanded reality, is a general word for several distinct but connected technologies, which include Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR).

XR, MR, VR, and AR

The terms XR, MR, VR, and AR are frequently used synonymously but there is a definite distinction between these. Virtual Reality is a type of XR that uses a head-mounted display (HMD) or smartphone, or other gadgets placed about an inch away from the viewer’s eyes to fully immerse the viewer in a simulated world. Here the display of the gadget completely fills the user’s field of view. However, not all XR is virtual reality (VR).

Similarly, Augmented Reality is a subset of XR that uses digital enhancements to improve the user’s perception of the actual world. Typically, a device’s camera is used for this, which records the real-world scene and superimposes digital components on top of it. For instance, augmented reality (AR) may phone’s camera to project game figures onto the screen of a smartphone, giving the impression that they are in the same room as you.

MR, or “mixed reality,” is a fusion of VR and AR. In other words, Virtual reality and augmented reality are combined to create Mixed Reality. Users can engage with both physical and digital objects thanks to MR’s anchoring of digital components to the real world. Usually, specialized gear, like Microsoft’s HoloLens, is used to accomplish this. Overall, it’s critical to comprehend the distinctions between XR technologies because each has particular advantages and disadvantages and is best suitable for particular uses.

Application of XR in Different Industries

XR technology by combining the physical and virtual worlds improves safety, efficiency, and productivity while reducing costs and downtime. It has the potential to revolutionize the way we work and is impacting many industries like Gaming and Entertainment, Employee Training, Customer Support, Healthcare, Property and Real Estate, and Retail.

Application of XR in Maintenance and repair


Maintenance and repair work is one of the most important applications where XR is having an impact. Technicians can obtain real-time information, such as directions, blueprints, and troubleshooting manuals, about the equipment they are working on by using AR overlays. This technology may increase the precision and effectiveness of maintenance and repair work, decrease delay, and eventually result in cost savings for companies.

Traditionally, techs have user manuals and blueprints to identify and fix equipment. This approach, though, can be laborious and error-prone. On the other hand, XR technology gives workers a visual depiction of the equipment, enabling them to swiftly recognize the issue and fix it. This is especially advantageous for sophisticated apparatus and equipment, where even a small error can result in expensive downtime and maintenance costs.

Additionally, step-by-step directions on how to fix or keep the equipment can be provided using AR overlays. This benefits technicians who may be inexperienced with a specific sort of machinery or lack the required knowledge. Technicians can more swiftly and correctly identify and fix machinery with the help of real-time guidance.

Repair workers as well can be trained using XR technology on how to operate and manage equipment. The use of VR models can give techs access to a regulated and secure virtual world where they can practice maintenance and repair tasks. This can lower the possibility of mishaps and equipment harm while training, as well as increase the techs’ effectiveness and accuracy in real-world situations.

Remote assistance can also be provided using AR graphics. With the aid of smart eyewear, techs can communicate in real-time with specialists who can assist them virtually throughout the maintenance or repair process. When working with complicated equipment that calls for specialized knowledge and experience, this can be especially helpful.

In general, XR technology has the power to completely transform the servicing and repair sector. Businesses can increase the precision and effectiveness of maintenance and repair tasks, decreasing delay and eventually saving money, by giving workers real-time information, step-by-step directions, and online assistance. It will be fascinating to observe how XR technology develops in the future and how it will help companies run more effectively and efficiently.

Please write to us at open-innovator@quotients.com to learn more about such innovative solutions and partnership opportunities.

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Applied Innovation Industry 4.0

How Microfactories will transform Manufacturing?

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Applied Innovation Industry 4.0

How Microfactories will transform Manufacturing?

A microfactory is a small-to-medium-sized, highly automated, technologically advanced manufacturing setup with a diverse set of process capabilities. It is often a production facility, the output of which may be scaled up by reproducing such setups in huge numbers. Because of the high-tech automated operations, microfactories use less energy, less material, and a smaller workforce. The microfactory idea also encourages the shrinking of manufacturing equipment and systems based on product dimensions. This helps to reduce the size of the plant, which requires less capital and decreases operational expenditures.

As micro-factories proliferate over the world, they will establish a uniform style of working and running, resulting in global production unity. On Similar lines to Cloud computing, Cloud manufacturing, a new production idea that converts traditional manufacturing resources into services and makes them available via the Internet by utilizing cloud computing, the Internet of Things (IoT), and virtualization is emerging. As companies like AWS offer standardized platforms on which a wide range of services and applications run, cloud manufacturing in the near future will have corporations supplying standardized micro-factories that handle the majority of contemporary industrial output.

Manufacturing as a Service (MaaS)

Cloud Manufacturing also referred to as MaaS or Distributed Production is one of the most significant opportunities made available by widespread company digitalization. It enables organizations to focus on product innovation while outsourcing the actual process, as well as crucial services and technology, to an outside party by using new technologies and linked services to improve production efficiency and business leanness.

Manufacturing as a service is the production of commodities using a networked manufacturing infrastructure. To put it another way, manufacturers utilize the internet to share production equipment in order to cut costs and produce better goods.

Just as cloud computing allows for instant access to pooled computer resources such as software, hardware, and data, similarly MaaS is driven by Cloud networked manufacturing. The cost of manufacturing infrastructure—machines, maintenance, software, networking, and other costs—is shared by all consumers. For starters, this results in cheaper production costs, more uptime for each machine, and increased manufacturing capacity for each organization.

These MaaS platforms are rapidly gaining traction in key industries such as automotive, aerospace, health care, and military. Major industrial enterprises have begun to use these platforms as well.

Our innovators have developed a new business model around MaaS that is expected to have a deep effect on the global manufacturing sector. We offer manufacturing services with complete visibility with significant lead time and capacity that is available for both startups and industries. CCTV camera-based machine vision capabilities assist in visualizing production processes digital twin and automate production data capture, monitoring, and analysis.

We would be delighted to hear from you and would like to discuss the opportunities for collaboration. Please write to us at open-innovator@quotients.com

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Applied Innovation Industry 4.0

Self-driving Robots for Industrial Automation

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Applied Innovation Industry 4.0

Self-driving Robots for Industrial Automation

Autonomous vehicles (AVs) have long caught the attention of futurists and technology enthusiasts, as indicated by constant research and development in autonomous vehicle technologies over the last two decades.

Rapid advancements in robotics, artificial intelligence, computer vision, and edge computing capabilities are culminating in robots that may be able to think, see, hear, and move. AVs in the form of self-driving vehicles have generated both enthusiasm and fierce rivalry among automakers and technology firms.

Self-driving vehicle prototypes outfitted with lidars, radars, cameras, and ultrasonic sensors — as well as hefty computational powers beneath the hood to detect and avoid obstacles — are becoming prevalent in many places. We are now on the verge of fast deployment of advanced autonomous vehicle technologies in industrial applications, and the convergence of the Internet of Things (IoT) and AV technologies is set to re-make and re-imagine industries.

Rapid automation in e-commerce distribution centers and industrial facilities has resulted in a vibrant subset of robotic logistics centered on supply chains and automated material transportation. A combination of variables, including but not limited to a spike in e-commerce, mass customization of items, technological improvements, and shifting economics in supply chains, has resulted in a surge in demand for automation in materials handling.

We offer some products based on the use of technologies such as computer software and robotics to control machinery and processes to perform specific functions. Some of these are described below that can be used to achieve digital factory goals in a time-bound manner.

Autonomous Forklift: An autonomous forklift that is appropriate for material delivery applications in warehouses or manufacturing plants, as well as outdoors. It can move easily on asphalt, concrete, and cemented walkways.

It has a payload capacity of 2 Tonne/ 3 Tonne/ 5 Tonne for various circumstances and can lift goods up to a height of 3 Meter. It is a counterbalanced forklift that performs well on slopes and uneven floor surfaces.

Autonomous Pallet Transfer Robot: We also offer a self-driving industrial-grade pallet transfer robot capable of automatically carrying a payload of 1000 kg/2000 kg. It has dual 3D cameras located in the front and back. The back 3D camera aids with accurate pallet localization and a safe alignment technique for lifting pallets from the ground.

Autonomous Trolley Transporter Robot: With this, we also have an autonomous trolley transporter robot, an industrial-grade model designed for autonomous trolley transportation. It can move carts of various sizes and weights ranging from 100 kg to 2000 kg by attaching to them from beneath. It has 3D camera-based trolley localization and safely align technology, which allows it to accurately fasten the trolleys from beneath. The robot has a fully autonomous guiding system with dynamic obstacle avoidance.

To know more about these solutions and for product demo please write to us at open-innovator@quotients.com

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Applied Innovation Industry 4.0

Visual Inspection System improving Production Process

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Applied Innovation Industry 4.0

Visual Inspection System improving Production Process

Manufacturing operations attempt to provide the greatest level of quality at all stages of the manufacturing or assembly process. This requires quality checks that need visual confirmation to verify that the pieces are in the correct places, have the correct form, color, or texture, and are free of flaws such as scratches, pinholes, foreign particles, and so on. Because of the volume of inspections and product variation, as well as the fact that flaws can appear anywhere on the product and be of any size, automating these visual quality checks is extremely challenging.

A visual inspection enables the production process to be improved. The vision inspection system has sensors and cameras and relies upon computer vision technology. A visual inspection machine compares two objects in order to provide a response or result. For performing the comparison, vision inspection systems contain all of the information required to categorize all of the items included in the inspection.

To decide which elements will pass the comparison and which will fail, the visual inspection machine incorporates photographs of past tests that were deemed successful. The cases of ideal elements, the elements to be classed, and those that will pass the tests are included in the system, both visually and by information from another class. Those elements that are included through images serve as a guide to be able to compare all the others, as stated earlier this is the visual comparison.

Visual comparison occurs when one piece is placed next to another, observed from multiple perspectives, and able to produce some form of link between them. The qualities to be compared are observed from various perspectives, first to make them correspond in their orientation and allow for a more accurate comparison, or it may also be accomplished by re-creating the photos in 3D format, superimposing one over the other, and describing the differences.

We have solutions for object recognition, fault detection, and process control. Our visual defect and dimensional sorting solutions for a variety of items deliver high productivity with excellent product handling and better inspection efficiency. These solutions are designed with the customer’s needs in mind and are highly customizable to the user. It is combined with cutting-edge in-house software that is dependable and accurate, resulting in the best final product.

Some key features of the solution are:

– Real-Time Monitoring
– Resident Database SQL
– Remote Control and Setting
– Vision Software Programmable by User
– Statistical Reports easily accessible through UI
– Machine Vision Software
– Accuracy up to ±10 microns
– Support for multiple Camera units
– Run as a Turn-Key system
– Simple Setup and Adjustments
– Short Setup Time for multiple codes

To learn more about this product and how you can use it to grow your business, increase productivity, improve quality, and create a better working environment for your employees, please contact us at open-innovator@quotients.com.

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Applied Innovation Industry 4.0

Cobots making automation simpler for businesses worldwide

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Applied Innovation Industry 4.0

Cobots making automation simpler for businesses worldwide

With Industry 4.0, robots’ role in intelligent production is expanding. The major objective is to increase production profitability, which is partly dependent on the organization of safety and efficient human-robot interaction in a shared work environment.

Robotic technologies incorporated with intelligent manufacturing can help to increase production automation while maintaining safety compliance. Robots’ mobility and compactness enable them to enhance the manufacturing process and increase efficiency.

The capacity to move robots outside of boundaries and set them near humans was a significant breakthrough in robotics. Every year, the market for robotic solutions expands. This suggests that there is a considerable demand for the use of robots in intelligent manufacturing. We can already observe how the major players in automated manufacturing have taken the first steps toward Industry 4.0.


A collaborative robot (cobot) is a robot designed to operate alongside humans. These are robots that are low-cost, safe, easy to install, and can make automation simpler than ever, even for small and medium-sized businesses worldwide. Cobots are meant to operate alongside people, making automation easier than ever for organizations of all sizes. All of these advantages have made cobots game changers in a wide range of applications. Additionally, these are excellent productivity tools for practically any manufacturing since they assist everyone in the organization in meeting performance goals.


We have solutions that make robots accessible to everyone. Human-robot interface technology that can be operated by experts or novices, without the need for them to be fluent in any programming languages or proprietary APIs. To do this, it is designed and manufactured from the ground up to seamlessly combine hardware and software with built-in collaboration capabilities, plug-and-play application adaptability, and, most crucially, the ability to operate securely alongside people without the use of any safety barriers.

These robots are totally software-driven, with a sophisticated software platform available via an intuitive and responsive interface that can be used to teach, collaborate, automate, and connect with a manufacturing ecosystem.

To know more about this product and learn how you may expand your business, boost productivity, enhance quality, and create a better working environment for your staff with these solutions please write to us at open-innovator@quotients.com.

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Applied Innovation Industry 4.0

Intelligent Video Analytics Transforming Manufacturing, Hospitality, Transportation, Healthcare, Retail, etc

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Applied Innovation Industry 4.0

Intelligent Video Analytics Transforming Manufacturing, Hospitality, Transportation, Healthcare, Retail, etc

Video analytics, also referred to as video content analysis or intelligent video analytics, in recent times has been at the center of attention in both the industry and the academic world.

Advances in Deep Learning aiding Video Analytics

With advances in Deep Learning research and expanded availability of video data, video analytics now allows for the automation of tasks that were once possible by a human intervention only. This allows it to be used in a number of applications ranging from monitoring traffic jams and alerting in real-time, to analyzing customers’ flow in retail to maximize sales.

In Deep Learning, a subset of AI, a machine is exposed to volumes of tagged data allowing it to learn and recognize and identify the same information in new data sets imitating the way a human works. Deep Learning offers advantages like faster analytic output, improved processing performance, and increased object detection, accuracy, and classification.

Intelligent video analytics automatically recognizes temporal and spatial events in videos and performs real-time monitoring but it can also be used to analyze historical data to find insights. It can recognize objects, object attributes, movement patterns, or behavior related to the monitored environment are detected.

Some applications

Video analytics has the potential to be widely used in industries such as manufacturing, hospitality, education, retail, and others. We are discussing a few of them.

Healthcare

Integrating video analytics into legacy CCTV systems can transform cameras into much more proactive intelligence tools that can be used to ensure the safety of patients, staff, and visitors. Some of the most common problems like theft, infant abduction, and drug diversion can be detected and checked.

Mental healthcare is another area in which video analytics can be used to analyze facial expressions, and body posture to alert the hospital staff. It can also play a role in the at-home monitoring of older adults or people with health issues

Further, the data collected can be used to generate insights that can help to shorten wait times and achieve business goals by managing the staff according to patterns in the footfall of patients.

Transportation

Video analytics can be used in reducing accidents and traffic jams by dynamically adjusting traffic light control systems by monitoring traffic. By recognizing situations that may turn fatal in real-time, it can raise alerts, and even in the case of an accident, these systems can trigger an alarm to security and healthcare institutions to take action apart from that it can also serve as evidence in case of litigation.

Video analytics can also perform tasks like vehicle counting, speed cameras can detect traffic movements and license plate recognition can spots stolen vehicles or vehicles being used in a crime. It can also generate high-value statistics to assist in making infrastructure-related and other policy decisions.

Retail

The retail industry can use video analytics to generate insights and actionable information on customers’ behavior and buying patterns through their key characteristics like gender, age, duration and time of visit, walkways, etc. These algorithms can also be used to recognize previous customers and improve customer experience and provide personalized service. Video analytics can also play a role in developing anti-theft mechanisms by identifying shoplifters.

Manufacturing

Video analytics can improve productivity, reduce downtime and ensure staff health at the manufacturing facility by enhancing operations and management efficiency.

Smart cameras can be used to predict potential interruptions, evaluate specific bottlenecks and reduce downtime by generating alerts to take proactive action immediately. It can also optimize the number of employees in the production facility and improve overall productivity. Inventory management can also be enhanced by analytics as the warehouses can be monitored for their capacity. The use of machine vision can help in inspection and improve quality control.

Video analytics can also warn of situations that may pose threats to people, products, or machines by detecting movements and identifying conditions. Video analytics can provide round-the-clock security and alert commercial as well as residential buildings and prevent potential break-ins.

Video Analytics Approach

Video content analysis can be done in real-time or post-processing. Also, it can be centrally on servers that are generally located in the monitoring station or can be embedded in the cameras themselves, some times a hybrid approach is adopted.

There are startups that are working on Video Analytics and have successfully deployed their solutions across various sectors such as hospitality, retail, manufacturing, pharma, and food. To more about evolving use-cases and startups in different domains please write to us at Open-innovator@Quotients.com

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

Inspections are critical for attaining manufacturing excellence. Inspection of processes and products determine the business success and customer confidence in the brand. Companies are increasingly relying on next-generation inspection solutions to improve quality control and deliver defect-free products. 

AI-powered Computer Vision

Digitization and machine vision based on AI algorithms can identify manufacturing anomalies much faster than human inspectors and improve quality and reduce costs. It employs computer vision, a field of artificial intelligence (AI) that enables computers and systems to obtain useful insight from digital images, videos, and other visual inputs — and take actions or make recommendations.

AI-backed computer vision is finding applications in industries ranging from manufacturing to automotive – and the market is expanding rapidly. It can perform the functions like inspection and identification in much less time. By the use of cameras, data, and algorithms, a system can be trained to inspect products or watch a production asset and analyze a very wide range of products or processes and detect invisible defects at a rate exceeding human capabilities.

Deep learning, an aspect of machine learning technology, trains machines by feeding a neural network with examples of labeled data. This is used to identify common patterns based on these examples and then convert it into a ‘math equation’ that mimics a human visual inspection classifies forthcoming information and performs tasks like differentiating parts, abnormalities, and texture.

Use Cases:

Automatic Counting: Computer Vision can be used for counting applications in industries where small parts are manufactured in large numbers like in metal parts, foods, pharmaceuticals, food, rubber pieces, wooden products, jewelry, etc.

Detect absence/presence: Computer vision can also detect the absence and presence of something such as date print, tags, brand logos, codes, stamps, etc, and automatically confirm the completeness of the product.

Sorting: Vision systems powered by AI algorithms can identify the right and defective product types by imaging them at high speeds. For example, separating pills in the pharma industry and segregating broken and damaged items in jewelry. This can be followed by sorting the identified items into chosen categories.

Surface Inspection: Computer Vision can identify surface anomalies for example scratches, dents, and pits accurately and at a high speed. Defects in some products like fabrics or automobile bodies are very small and undistinguishable, which can be detected only by monitoring the variation in intensity using deep learning algorithms.

Application:

Machine Vision is powering Industrial Automation. Using the latest 2D, 3D, and Artificial Intelligence solutions inspection systems are used across various industries like the pharmaceutical industry, automotive industry, printing and packaging industry, food and beverage industry, and textile industry. It offers huge benefits in eliminating human interventions and errors thus cutting down heavily on inspection cost and time.

There are startups working on this solution helping the above-mentioned as well as other sectors to greatly enhance their functioning through the acceptance and integration of new technologies into their existing systems. To know more about these and for collaboration and partnership opportunities please write to us at open-innovator@quotients.com

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Applied Innovation Industry 4.0

No Code RTLS Platform can enhance Safety and Productivity

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Applied Innovation Industry 4.0

No Code RTLS Platform can enhance Safety and Productivity

RTLS or Real-time locating systems are used to determine an item or person’s location accurately. RTLS, rather than a specific technology, is a collection of methods for locating and managing assets. These systems generate location data and depending on the application the collected data can be used for employee tracking and high-value asset tracking in industries such as manufacturing, mining, healthcare, etc. The components of RTLS consist of a transponder, a receiver, and software to interpret each data. Due to the increasing focus on industry 4.0, smart manufacturing, and technological advancements such as IoT, the demand for RTLS is soaring.

Location Data Ensuring Visibility


Location is also a key element in a comprehensive IoT solution that can have a significant impact on the revenues of any company. It can give businesses complete visibility into their operations, from the movement of people to the monitoring of assets. The analysis of this data can help an organization to derive valuable insights which can lead to more informed decision-making, optimization of processes, and streamlined costs.

Supply Chain Management

Timing is very crucial in the supply chain, location data can make the value chain completely visible to decision-makers helping them streamline operations and increase productivity. This also can ensure predictive analytics to refine performance and boost productivity by identifying lag time and areas for improvement. It can also bring down the operating costs with detailed and optimal transportation routes.

RTLS through data of the assets can help in optimal use and inventory stocking. With this, it enables better asset tracking, and material handling systems, reducing accidents and asset losses. Multiple activities can be monitored at once — supply chain, warehousing, transportation, etc so it can thus streamline processes and increase customer satisfaction.

Manufacturing

In Manufacturing Industry, workers’ data on their location and wearable sensors collect data about their health parameters that can be analyzed against standard parameters to predict behavior patterns and improve their productivity along with preventing workers’ injuries and ensuring safety. With this, by leveraging location data with IoT data, manufacturers can better sense the manufacturing and supply chain processes, improve demand forecasting, and achieve faster time to market.

Healthcare

In the Healthcare industry, RTLS through data collection on medical equipment location and usage and staff and patient interactions can ensure compliance reporting by gathering important data. RTLS data if integrated with other sources like electronic health records and maintenance management systems can be used to give comprehensive information across the healthcare enterprise leading to improvements for healthcare facilities and their patients.

No-Code/ Low Code RTLS Platform

There are some hurdles in the implementation of RTLS in any organization. Apart from the leadership hurdle, some enterprises focus on certain aspects more heavily than others. In this respect, the No-Code/ Low Code RTLS Platform is a programming platform that enables non-technical users to build applications by visual development interface and, dragging and dropping software applications. Such modular and configurable solutions can give flexibility to organizations to configure their rules and alerts according to their requirement and also lead to quick deployment.

Quotients is also engaged with matured startups that are working on this solution helping the above-mentioned as well as other sectors to greatly enhance their functioning through the acceptance and integration of new technologies into their existing systems. To know more about these and for collaboration and partnership opportunities please write to us at open-innovator@quotients.com

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