Connect with us

AI

10 Top AI Manufacturing Use Cases [ Updated] 2020

The post 10 Top AI Manufacturing Use Cases [ Updated] 2020 appeared first on USM.

Published

on

Smart Factory digital technologies such as Artificial Intelligence (AI), Machine Learning (ML), and predictive analytics enabling manufacturers to increase productivity. Also, smart technologies also empowers employees to perform smart operation!

According to the research report of Tractica, The companies of manufacturing are now adopting AI technology in their environments at a moderate, yet steady pace. The worldwide annual manufacturing Industry investment in AI services, hardware and software services is projected to rise from 2.9 US billion dollars in 2018 to 13.2 US billion dollars by the year 2025, according to Market Intelligence. Increasing the use of AI in manufacturing sector will lead to cost reduction in production processes and increment in operational efficiency.

Here the detail production breakdown for manufacturing industry by using Artificial intelligence in 2018 to 2025

On the other side, Artificial intelligence in manufacturing market is predicted to develop from 1.0 billion US billion dollars in the year 2018 to 17.2 US billion dollars by 2025, at a compound annual growth rate (CAGR) of 49.5% over the estimate time frame. The enormous accessibility of big data technology and the growth of venture capital investments are the main factors that are fueling the development of AI in the market of this industry.

AI opportunities in manufacturing industry

In this blog, we captured a few best use cases of artificial intelligence in manufacturing. Let’s have a look into the below sessions.

Top 10 uses cases of AI in manufacturing industry

#1 Quality Checks

Some internal defects in manufacturing equipment’s cannot be found that much easily with eyes. Even experience professionals were also some time unable to detect the flaws in products. Thanks to artificial intelligence and machine learning technologies. The can detect smallest flaws in machinery.

Using intelligent algorithms, smart machines can continuously monitors the productivity of machinery and spots failures, if any. AI-powered inspection tools offers fully automated flaw detection processes. The intelligent device flaw detection tools in manufacturing monitors the equipment performance and its quality. Microscopic faults will also be identified using AI tools in manufacturing.

Hence, AI-enabled systems identifies product defects, marks them all, and sends alerts to human experts.

USM bring innovation in your manufacturing procedures. Know how?

#2 Predicts Equipment Failure

Manufacturers face challenges with machinery/products failures in many ways. A product might look perfect from outside, but it may damage once we use it. Yes, it happens to machinery, and also leads huge loses to manufacturers.

With the availability of vast data on how the products are tested and how they function, artificial intelligence-based tools and machines identifies the specific areas that need to be tested efficiently.

#3 Equipment Predictive Maintenance

Predictive maintenance of devices allows manufacturer to avoid device damage overheads. Using ML-powered predictive analytical solutions, you can predict when machineries require maintenance services. Machine Learning is one of the outmost technology that can prevent unplanned downtime.

Not only analytics solutions, cloud and the Internet-of-Things (IoT) sensors are also playing a vital role in modernizing manufacturing industry. They embedded in machinery to better predict the maintenance and thus overcomes equipment issues that has to be occur in the future.

Many manufacturing companies are reaping the benefits of artificial intelligence. LG, Roland Busch, and Siemens etc.

USM’s AI-enabled manufacturing solutions bring automation across your manufacturing processes. Our AI services and applications for manufacturing helps to achieve smart manufacturing operations.

Our technical team understand the depth in the client request. We delivered an AI-powered mobility solution to improve the systems and processes in the manufacturing industry. Our AI solution helped our client in providing the internal condition of the equipment.

Get your AI-powered manufacturing app design quote.

#4 Digital Twins

AI helps to completely virtualize the infrastructure, products or services. The process of virtual representation of a manufacturing unit is called a digital twin. Using data gathering tools like sensors and cameras, the physical representation of manufacturing environment will be completely virtualized.

To make sure that digital twins are working properly, you should integrate all smart components like sensors that are collecting data from equipment’s. Using a cloud connection, the data generated by smart components will be collected, stored and processed. As AI-based systems needs vast amount of data, further, Ai systems retrieves data from cloud and makes it workable for the company.

#5 Supply-Chain Management

Use of artificial intelligence in the supply chain management is rapidly increasing. The technology is gaining momentum across supply chain management operations. Machine learning, natural language processing, computer vision, robotics and speech recognition are making supply chain management tasks smarter.

AI has multiple applications in supply chain management. They include:

• Establishes a strong communication channel among departments

USM’s supply-chain management solution for manufacturing industry brings different management streams of an enterprise at single platform. Thus, the best communication channel among teams helps to improve overall business performance.

• Warehouse management & logistics

Artificial intelligence tools and apps can optimize the warehouse management and logistic operations. From product storing to delivery and receiving, everything can be analyzes using AI.AI-enabled devices and tools can also manage and track fleet operations efficiently.

• Development of autonomous vehicles for logistics

Artificial Intelligence in manufacturing is going to its next level in the form of autonomous vehicles. To better manage the distribution centers, the manufacturing companies are investing in AI-powered autonomous vehicles to automate the logistic operations.

Hence, together with artificial intelligence robotics and tools, self-driving vehicles reduces dependency on human drivers. A big thanks to artificial intelligence technology.

USM AI Mobility Solutions for logistics operations are incredible. Once have a look at what our artificial intelligence solutions offer:

#6 Forecast Product Demand

Artificial intelligence systems using predictive analytics can also forecast the product demand efficiently. AI tools for manufacturing collects data from various sources and based on it, they can accurately forecast the product demand.

#7 Inventory Management

Artificial intelligence app in manufacturing allows you to manage order records and delete/add new inventories. Here, we should talk about machine learning technology. It was one of the most significant technology that is used for managing supply, demand, and inventories.

#8 Price Forecasts

Using historical data of product prices, and analyzing pricing structure of various competitor’s product prices, machine learning algorithms can forecast the price of a product. Competitive prices are always offers more profits to the companies.

#9 Robotics in Manufacturing

We are all well aware of use of robots in manufacturing processes. It’s a fact that machines can perform more efficiently than humans. Of course, they need a support of human workforce. But, any way machine are very much faster than humans in doing tasks. AI-powered robots for manufacturing performs repetitive tasks without being programmed. This is one of the best application of AI and ML for manufacturers.

#10 Customer Management

AI applications for manufacturing customers help in increasing sales, productivity, and business performance through managing their customers smartly. Yes, with the use of smart AI apps for manufacturing, service providers can quickly understand the customer issues and resolve them, and also personalize their experience.

Let’s have a look at the benefits of AI service and solutions in customer service:

  • Quick response time
  • Personalized experience
  • Improved relations using CRM (Customer Relationship Management) tool
  • To make informed decision using customers data

Is AI the future of manufacturing?

100% Yes. Artificial intelligence will be the future of manufacturing industry. Not only manufacturing, it’s a game-changer for all industries. AI technology is now more accessible for all businesses.

Driven by increased product demand, manufacturing industry will always open up to adopt new technologies like AI, ML and etc. Process optimization, low cost overheads, high productivity, quick decision-making, and improved customer services, everything will be obtained using AI in manufacturing.

Get more information of USM’s AI services and solutions for manufacturing industry. Let’s see might our AI solutions in manufacturing help your business.

Get In Touch

Source: https://www.usmsystems.com/ai-in-manufacturing-use-cases/

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

Trending