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Events

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

3D Digital Twins: The Key to a More Efficient, Safer, and Sustainable Future

3D Digital Twins: The Key to a More Efficient, Safer, and Sustainable Future
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Applied Innovation

3D Digital Twins: The Key to a More Efficient, Safer, and Sustainable Future

The idea of a 3D digital twin has arisen as a ground-breaking solution with broad ramifications in a society driven by technology and creativity. Imagine having a virtual counterpart that accurately captures data and replicates changes in the real environment in real time. A 3D digital twin is a technical marvel that is revolutionizing a wide range of industries, including manufacturing, healthcare, and energy.

Understanding 3D Digital Twins

A virtual depiction of a physical system or item that is continually updated with real-world data is known as a 3D digital twin. These inputs range widely, covering live video feeds, operational data, and sensor readings. The end result is a dynamic and accurate representation that gives decision-makers unprecedented access to their assets for interaction, analysis, and optimization.

Benefits that Go Beyond

The benefits of integrating 3D digital twins into different businesses are significant and varied, eventually fostering advancement and innovation on several fronts.

1. More Effective Decision-Making

The ability of 3D digital twins to simulate many scenarios and outcomes is one of its most remarkable advantages. Users obtain a greater grasp of how their assets or systems behave in various scenarios by simulating various scenarios. This knowledge improves judgment, enabling more intelligent decisions on how to use and manage assets effectively.

2. Lower Costs

Prevention is frequently more economical than problem-solving after the fact. With the help of 3D digital twins, companies can spot potential problems before they become major ones, which saves them a lot of money over the long term. Businesses may manage resources more efficiently and avoid costly downtime or repairs by anticipating and preventing issues.

3. Enhanced Effectiveness

Efficiency is mostly driven through optimization, and 3D digital twins provide a means of doing so. Organizations may optimize their processes and workflows to reduce waste, reorganize processes, and increase productivity. This increase in productivity might result in better overall performance and competitiveness.

4. Increased Security

Safety comes first in high-risk businesses. Workers may train in a controlled environment using 3D digital twins where they can become familiar with tools, processes, and possible risks. Employees can gain crucial skills while lowering the risk of accidents by practicing in a safe virtual environment.

Applications Across a Range of Industries

The integration of 3D digital twins across sectors, each with unique applications that rethink how processes are conceptualized and carried out, demonstrates the flexibility of this technology.

Design and testing transformation in manufacturing

3D digital twins are being used in manufacturing to develop, test, and create items with unmatched efficiency. To model the performance of jet engines before actual production starts, GE, for instance, uses 3D digital twins. The early identification and resolution of potential problems are made possible by this preventative strategy, which eventually leads to higher-quality goods and lower manufacturing costs.

Healthcare: Enhancing Precision and Care

3D digital twins are revolutionizing patient care and surgery planning in the healthcare industry. Using 3D digital twins, complex procedures are methodically planned at places like the Mayo Clinic, improving surgical success and lowering patient risks. These twins also help medical experts replicate the consequences of various therapies, promoting a more individualised and successful method of providing healthcare.

Energy: Providing Intelligent Management

3D digital twins are essential in the energy sector for monitoring and enhancing energy systems. This is demonstrated by the National Grid’s usage of 3D digital twins to manage the UK power grid. Monitoring electricity flow allows for the early detection and resolution of possible problems, reducing the likelihood of blackouts and assuring a steady supply of energy.

A Wide Range of Digital Twin Tools

There are four different sorts of digital twins, each of which is tailored to certain requirements and goals across diverse sectors.

Digital twins that are focused on certain portions or components of a larger system are called component or part twins. They make it possible to accurately track and evaluate the performance of individual parts.

Twins of an asset or product can help with resource management and optimization since they represent the full asset or product.

System or Unit Twins: These twins simulate complex systems, including manufacturing or power plants, allowing the simulation of behavior and performance.

Process Twins: Process Twins focus on streamlining particular workflows to improve the effectiveness of product production and service delivery.

Looking Forward: An Innovative Future

The potential for 3D digital twins to change industries is what is driving their widespread use. We should expect even more ground-breaking uses for digital twins as technology develops, driving companies toward increased productivity, sustainability, and safety.

The development of 3D digital twins is a shining example of human creativity and the ever-evolving capabilities of technology in the quest for a more interconnected and intelligent society. Industries may overcome obstacles, capture opportunities, and navigate the future with unheard-of knowledge and foresight by utilizing these virtual duplicates.

Quotients is a platform for industry, innovators, and investors to build a competitive edge in this age of disruption. We work with our partners to meet this challenge of the metamorphic shift that is taking place in the world of technology and businesses by focusing on key organizational quotients. Write to us open-innovator@quotients.com for knowing more about innovative solutions.

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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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