Article | December 8, 2021
A digital twin is a virtual model of an object or system that comprises its lifecycle. It is updated with real-time data and aids decision-making through simulation, machine learning, and reasoning for the production system.
IoT sensor data from the original object is used to create a digital twin of the system. This cloud-connected data allows engineers to monitor systems and model system dynamics in real-time.
Modifications can be tested on the digital twin before making changes to the original system.
Considering that digital twins are supposed to replicate a product's complete lifecycle and are used throughout the production process, it's not unexpected that digital twins have become prevalent in all stages of manufacturing.
“More than a blueprint or schematic, a digital twin combines a real-time simulation of system dynamics with a set of executive controls,”
– Dr. Daniel Araya, consultant and advisor with a special interest in artificial intelligence, technology policy, and governance
Companies will increasingly embrace digital twins to boost productivity and decrease expenses. As per recent research by Research and Markets, nearly 36% of executives across industries recognize the benefits of digital twinning, with half planning to implement it by 2028.So how does this digital twin technology benefit modern manufacturing? Let's have a look.
How the Digital Twin Drives Smart Manufacturing
Digital twins in manufacturing are used to replicate production systems. Manufacturers can develop virtual representations of real-world products, equipment, processes, or systems using data from sensors connected to machines, tools, and other devices.
In manufacturing, such simulations assist in monitoring and adapting equipment performance in real-time. With machine learning techniques, digital twins can predict future events and anticipate potential difficulties.
For maintenance, digital twins allow for quick detection of any problems. They collect real-time system data, prior failure data, and relevant maintenance data. The technique employs machine learning and artificial intelligence to predict maintenance requirements. Using this data, companies can avoid production downtime.
Digital Twin and Artificial Intelligence (AI) in manufacturing
Using digital twins and AI in production can enhance uptime by predicting potential failures and keeping equipment working smoothly. In addition, there are significant cost savings in the planning and design process as digital twins and AI can be used to replicate a specific scenario.
Maintenance is another area that has seen significant progress with the use of digital twin manufacturing. A Digital Twin powered by AI can predict when a piece of equipment will fail, allowing you to arrange predictive maintenance that is not simply taking information from OEM manuals but can significantly cut maintenance expenses along with reducing downtime.
Using the digital twin, it is feasible to train virtual workers in high-risk functions, similar to how pilots are trained using flight simulators. It also frees up highly skilled workers to upgrade the plant and streamline operations.
General Electric Created the Most Advanced Digital Twin
General Electric Company (GE) is a multinational business based in Boston that was founded in 1892. It has developed the world's most advanced digital twin, which blends analytic models for power plant components that monitor asset health, wear, and performance with KPIs (Key Performance Indicators) determined by the customer and the organization's objectives. The Digital Twin is powered by PredixTM, an industrial platform built to manage huge amounts of data and run analytic algorithms. General Electric Company provides extra "control knobs" or "dimensionality" that can be utilized to improve the operation of the system or asset modeled with GE Digital Twin.
Given the numerous advantages of digital twin manufacturing, the potential for digital twins to be used in manufacturing is virtually endless in the near future. There will be a slew of new advancements in the field of digital twin manufacturing. As a result, digital twins are continually acquiring new skills and capabilities. The ultimate goal of all of these enhancements is to create the insights necessary to improve products and streamline processes in the future.
What is a digital twin in manufacturing?
The digital twins could be used to monitor and enhance a production line or perhaps the whole manufacturing process, from product design to production.
How digital twin benefit manufacturers?
Using digital twins to represent products and manufacturing processes, manufacturers can save assembly, installation, and validation time and costs.
What is a digital thread?
A digital twin is a realistic version of a product or system that replicates a company's equipment, controls, workflows, and systems. The digital thread, on the other hand, records a product's life cycle from creation to dissolution.
"name": "What is a digital twin in manufacturing?",
"text": "The digital twins could be used to monitor and enhance a production line or perhaps the whole manufacturing process, from product design to production."
"name": "How digital twin benefit manufacturers?",
"text": "Using digital twins to represent products and manufacturing processes, manufacturers can save assembly, installation, and validation time and costs."
"name": "What is a digital thread?",
"text": "A digital twin is a realistic version of a product or system that replicates a company's equipment, controls, workflows, and systems. The digital thread, on the other hand, records a product's life cycle from creation to dissolution."
Article | December 7, 2021
Machine learning in manufacturing is becoming more widespread, with businesses like GE, Siemens, Intel, Bosch, NVIDIA, and Microsoft all investing heavily in machine learning-based ways to enhance manufacturing.
Machine learning is predicted to expand from $1 billion in 2016 to USD 9 billion by 2022at a compound annual growth rate (CAGR) of 44% throughout the forecast period, according to Markets & Markets.
The technology is being utilized to cut labor costs, achieve better transition times, and increase manufacturing speed.
“I advocate business leaders get to know more about what AI can do and then leverage AI in proofs of concept.”
– Michael Walton, Director and Industry Executive, Microsoft speaking with Media 7
Machine learning can help enhance manufacturing processes at the industrial level. This can be achieved by assessing current manufacturing models and identifying flaws and pain factors. Businesses can rapidly address any difficulties to keep the manufacturing pipeline running smoothly.
Let us explore how machine learning is transforming manufacturing operations.
How Machine Learning Is Transforming Manufacturing Operations
“The greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.”
- Pedro Domingos
Machine learning in manufacturing is revolutionizing manufacturing operations and making them more advanced and result-oriented, so let's have a look at how this is unfolding.
Allows for Predictive Maintenance
Machine learning provides predictive maintenance by forecasting equipment breakdowns and eliminating wasteful downtime. Manufacturers spend far too much time correcting problems instead of planning upkeep. In addition to enhancing asset dependability and product quality, machine learning systems can forecast equipment breakdown with 92% accuracy. Machine learning and predictive analytics increased overall equipment efficiency from 65% to 85%.
Increases Product Inspection and Quality Control
Machine learning is also utilized for product inspection. Automated inspection and supervision using ML-based computer vision algorithms can discriminate between excellent and bad products. These algorithms simply need excellent samples to train; therefore a fault library is not required. However, an algorithm that compares samples to the most common errors can be built. Machine learning reduces visual quality control costs in manufacturing. Forbe's says AI-powered quality testing can boost detection rates by up to 80%.
Logistics-related Tasks Are Automated
To run a production line, industrial companies need considerable logistics skills. The use of machine learning-based solutions can improve logistics efficiency and save expenses. Manual, time-consuming operations like logistics and production-related documentation cost the average US business $171,340 annually. It saves thousands of manual working hours every year to automate these everyday procedures. Using Deep Mind AI, Google was able to lower its data center cooling bill by 40%.
Creates More Business Opportunities
Machine learning is frequently used in the production process. Substantial data analysis is required to create new items or improve existing products. Collection and analysis of huge amounts of product data can help find hidden defects and new business opportunities. This can help improve existing product designs and provide new revenue streams for the company. With machine learning, companies can reduce product development risks by making smarter decisions with better insights.
Protects Company’s Digital Assets
On-premise and cloud-based machine learning systems require networks, data, and technological platforms to function. Machine learning can help secure these systems and data by restricting access to vital digital platforms and information. Humans’ access sensitive data, choose applications, and connect to it using machine learning. This can help secure digital assets by immediately recognizing irregularities and taking appropriate action.
Harley Davidson's Sales Climbed by 40% Using Albert – The ML & AI-Powered Robot
Today, traditional marketing is harder to break through. It's easy to see why Albert (an AI-powered robot) would be a good fit for Harley Davidson NYC. Thanks to machine learning and artificial intelligence, robots are producing news stories, working in hotels, controlling traffic, and even running McDonald's.
Albert works well with social media and email marketing. It analyzed which customers are more likely to convert and modifies the personal creative copies on its own for the next process.
Harley-Davidson is the only company to employ Albert in its business. The company evaluated customer data to find prior consumers who made purchases and spent more time browsing the website than normal. Albert used this data to categorize customers and scale up test campaigns.
Using Albert, Harley-Davidson's sales climbed by 40% and leads increased 2,930%, with half coming from high-converting ‘lookalikes' detected by AI and machine learning.
The groundbreaking benefits of machine learning are the pillars of machine learning applications in manufacturing. Machine learning in manufacturing helps enhance productivity without compromising quality. According to Forbes, Amazon has automated warehouse logistics picking and packing using a machine learning system. With Kiva's help, Amazon's typical ‘click to ship' time dropped from 60-75 minutes to 15 minutes. So, industry leaders are seeing fantastic outcomes, and machine learning in manufacturing is the future.
How is machine learning used in manufacturing?
Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process.
Which two forms of machine learning are there?
Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data.
What is a machine learning model?
A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm.
"name": "How is machine learning used in manufacturing?",
"text": "Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process."
"name": "Which two forms of machine learning are there?",
"text": "Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data."
"name": "What is a machine learning model?",
"text": "A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm."
Article | December 16, 2021
Computer-aided manufacturing (CAM) is a technology that revolutionized the manufacturing business. Pierre Bézier, a Renault engineer, produced the world's first real 3D CAD/CAM application, UNISURF CAD. His game-changing program redefined the product design process and profoundly altered the design and manufacturing industries.
So, what is CAM in its most basic definition?
Computer-aided manufacturing (CAM) is the application of computer systems to the planning, control, and administration of manufacturing operations. This is accomplished by using either direct or indirect links between the computer and the manufacturing processes. In a nutshell, CAM provides greater manufacturing efficiency, accuracy, and consistency.
As technology takes over and enhances many of the processes we used to handle with manual labor, we are freed up to use our minds creatively, which leads to bigger and better leaps in innovation and productivity.”
– Matt Mong, VP Market Innovation and Project Business Evangelist at Adeaca
In light of the numerous advantages and uses of computer-aided manufacturing, manufacturers have opted to use it extensively. The future of computer-aided manufacturing is brightening due to the rapid and rising adoption of CAM.
According to Allied Market Research, the global computer-aided manufacturing market was worth $2,689 million in 2020 and is expected to reach $5,477 million by 2028, rising at an 8.4% compound annual growth rate between 2021 and 2028.
Despite all this, each new development has benefits and challenges of its own. In this article, we'll discuss the benefits of CAM, the challenges that come with it, and how to deal with them. Let's start with the advantages of computer-aided manufacturing.
Benefits of Computer Aided Manufacturing (CAM)
There are significant benefits of using computer-aided manufacturing (CAM). CAM typically provides the following benefits:
Increased component production speed
Maximizes the utilization of a wide variety of manufacturing equipment
Allows for the rapid and waste-free creation of prototypes
Assists in optimizing NC programs for maximum productivity during machining
Creates performance reports automatically
As part of the manufacturing process, it integrates multiple systems and procedures.
The advancement of CAD and CAM software provides visual representation and integration of modeling and testing applications.
Greater precision and consistency, with similar components and products
Less downtime due to computer-controlled devices
High superiority in following intricate patterns like circuit board tracks
Three Challenges in CAM and Their Solutions
We have focused on the three primary challenges and their solutions that we have observed.
Receiving Incomplete CAD Updates
Receiving insufficient CAD updates is one of the challenges. If, for example, the part update from a CAD engineer does not include the pockets that are required in the assembly, to the CAM engineer.
SOLUTION: A modeler that enables developers of a CAM programs to create intuitive processes for features such as feature extraction and duplication across CAD version updates. A modeler is capable of recognizing and extracting the pocket's architecture and the parameters that define it. Additionally, the CAM application can enable the engineer to reproduce the pocket in a few simple steps by exploiting the modeler's editing features such as scaling, filling, extruding, symmetrical patterning, and removing.
Last Minute Design Updates
The second major challenge is last-minute design changes may impact manufacturers as a result of simulation.
SOLUTION: With 3D software components, you may create applications in which many simulation engineers can work together to make design modifications to the CAD at the same time, with the changes being automatically merged at the end.
Challenging Human-driven CAM Manufacturing
The third major challenge we have included is that CAM engineers must perform manual steps in human-driven CAM programming, which takes time and requires expert CAM software developers. Furthermore, when the structure of the target components grows more complicated, the associated costs and possibility of human failure rise.
SOLUTION: Self-driving CAM is the best solution for this challenge. Machine-driven CAM programming, also known as self-driving CAM, provides an opportunity to improve this approach with a more automated solution. Preparing for CAM is simple with the self-driving CAM approach, and it can be done by untrained operators regardless of part complexity. The technology handles all of the necessary decisions for CAM programming operations automatically. In conclusion, self-driving CAM allows for efficient fabrication of bespoke parts, which can provide substantial value and potential for job shops and machine tool builders.
Computer Aided Manufacturing Examples
CAM is widely utilized in various sectors and has emerged as a dominant technology in the manufacturing and design industries. Here are two examples of sectors where CAM is employed efficiently and drives solutions to many challenges in the specific business.
Virtual 3D prototype systems, such as Modaris 3D fit and Marvellous Designer, are already used by designers and manufacturers to visualize 2D blueprints into 3D virtual prototyping. Many other programs, such as Accumark V-stitcher and Optitex 3D runway, show the user a 3D simulation to show how a garment fits and how the cloth drapes to educate the customer better.
Aerospace and Astronomy
The James Webb Space Telescope's 18 hexagonal beryllium segments require the utmost level of precision, and CAM is providing it. Its primary mirror is 1.3 meters wide and 250 kilograms heavy, but machining and etching will reduce the weight by 92% to just 21 kilograms.
What is the best software for CAM?
Mastercam has been the most extensively utilized CAM software for 26 years in a row, according to CIMdata, an independent NC research business.
How CAD-CAM helps manufacturers?
Customers can send CAD files to manufacturers via CAD-CAM software. They can then build up the machining tool path and run simulations to calculate the machining cycle times.
What is the difference between CAD and CAM?
Computer-aided design (CAD) is the process of developing a design (drafting). CAM is the use of computers and software to guide machines to build something, usually a mass-produced part.
Article | October 20, 2021
Machine vision is becoming increasingly prevalent in manufacturing daily across industries. The machine vision manufacturing practice provides image-based automated inspection and analysis for various applications, including automatic inspection, process control, and robot guiding, often found in the manufacturing business.
This breakthrough in manufacturing technology enables producers to be more innovative and productive to meet customer expectations and deliver the best products on the market.
A renowned industry leader Mr. Matt Mongonce conveyed in an interview with Media7,
As technology takes over and enhances many of the processes we used to handle with manual labor, we are freed up to use our minds creatively, which leads to bigger and better leaps in innovation and productivity
-Matt Mong, VP Market Innovation and Project Business Evangelist at Adeaca.
Why is Machine Vision so Critical?
The machine vision manufacturing process is entirely automated, with no human intervention on the shop floor. Thus, in a manufacturing process, machine vision adds significant safety and operational benefits. Additionally, it eliminates human contamination in production operations where cleanliness is critical.
For instance, the healthcare business cannot afford human contamination in some circumstances to ensure the safety of medicines. Second, the chemical business is prohibited from allowing individuals to come into touch with chemicals for the sake of worker safety. Thus, machine vision is vital in these instances, so it is critical to integrate machine vision systems into your production process.
Machine Vision Application Examples
To better understand how businesses are utilizing machine vision in production, we will look at five cases.
Even a few seconds of production line downtime might result in a significant financial loss in the manufacturing industry. Machine vision systems are used in industrial processes to assist manufacturers in predicting flaws or problems in the production line before the system failure. This machine vision capability enables manufacturing processes to avoid breakdowns or failures in the middle of the manufacturing process.
How is FANUC America Corporation Avoiding the Production Line Downtime with ROBOGUIDE and ZDT?
FANUC is a United States-based firm that is a market leader in robotics and ROBOMACHINE technology, with over 25 million units deployed worldwide. In addition, the company's professionals have created two products that are pretty popular in the manufacturing industry: ROBOGUIDE and ZDT (Zero Down Time).
These two standout products assist manufacturers in developing, monitoring, and managing production line automation. As a result, producers can enhance production, improve quality, and maximize profitability while remaining competitive.
Inspection of Packages
To ensure the greatest possible quality of products for their target consumer groups, manufacturers must have a method in place that enables them to inspect each corner of their product. Machine vision improves the manufacturing process and inspects each product in detail using an automated procedure.
This technology has been used in many industries, including healthcare, automation, and electronics. Manufacturers can detect faults, cracks, or any other defect in the product that is not visible to the naked eye using machine vision systems. The machine vision system detects these faults in the products and transmits the information to the computer, notifying the appropriate person during the manufacturing process.
Assembly of Products and Components
The application of machine vision to industrial processes involves component assembly to create a complete product from a collection of small components. Automation, electronics manufacturing, healthcare (medicine and medical equipment manufacturing), and others are the industries that utilize the machine vision system in their manufacturing process. Additionally, the machine vision system aids worker safety during the manufacturing process by enhancing existing safety procedures.
Manufacturers are constantly endeavoring to release products that are devoid of flaws or difficulties. However, manually verifying each product is no longer practicable for anybody involved in the manufacturing process, as production counts have risen dramatically in every manufacturing organization. This is where machine vision systems come into play, performing accurate quality inspections and assisting producers in delivering defect-free items to their target clients.
Earlier in the PCB penalization process, where numerous identical PCBs were made on a single panel, barcodes were used to separate or identify the PCBs manually by humans. This was a time-consuming and error-prone process for the electronics manufacturing industry. This task is subsequently taken over by a machine vision system, in which each circuit is segregated and uniquely identified using a robotics machine or a machine vision system. The high-tech machine vision system "Panel Scan" is one example of a machine vision system that simplifies the PCB tracing procedure.
The use of machine vision in the manufacturing business enables firms to develop more accurate and complete manufacturing processes capable of producing flawless products. Incorporating machine vision into manufacturing becomes a component of advanced manufacturing, which is projected to be the future of manufacturing in 2022. Maintain current production trends and increase your business revenue by offering the highest-quality items using a machine vision system.
What is the difference between computer vision and machine vision?
Traditionally, computer vision has been used to automate image processing, but machine vision is applied to real-world interfaces such as a factory line.
Where does machine vision come into play?
Machine vision is critical in the quality control of any product or manufacturing process. It detects flaws, cracks, or any blemishes in a physical product. Additionally, it can verify the precision and accuracy of any component or part throughout product assembly.
What are the fundamental components of a machine vision system?
A machine vision system's primary components are lighting, a lens, an image sensor, vision processing, and communications.
"name": "What is the difference between computer vision and machine vision?",
"text": "Traditionally, computer vision has been used to automate image processing, but machine vision is applied to real-world interfaces such as a factory line."
"name": "Where does machine vision come into play?",
"text": "Machine vision is critical in the quality control of any product or manufacturing process. It detects flaws, cracks, or any blemishes in a physical product. Additionally, it can verify the precision and accuracy of any component or part throughout product assembly."
"name": "What are the fundamental components of a machine vision system?",
"text": "A machine vision system's primary components are lighting, a lens, an image sensor, vision processing, and communications."