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 6, 2021
Aerospace manufacturing and design are getting advanced with additive manufacturing. However, the limitations of traditional manufacturing techniques sometimes make it incompetent to produce technologically oriented products. Additive Manufacturing (AM)helps the aircraft system run more efficiently by creating lightweight aircraft parts.
This is one of the reasons that additive manufacturing is gaining traction in aerospace and other industries. According to recent analysis and data, the global additive manufacturing market is expected to grow from USD 9.52 billion in 2020 to USD 27.91 billion in 2028. The expanding technologies and materials used in additive manufacturing will indeed stimulate industry growth shortly.
It’s important to note that there isn’t one channel that is the silver bullet. Most of the time, a combination of different channels will help drive a more powerful outcome.”
– Wendy Lee, Director of Marketing at Blue Prism
However, the aerospace industry encounters some challenges with additive manufacturing, which is the focus of this article. Scalability, multi-material capabilities, professional workers, high-cost materials, and quality compliance norms are all constraints that aerospace professionals are dealing with. Here we will discuss the top three challenges of additive manufacturing in aerospace and their solutions.
Future of Additive Manufacturing in the Aerospace Industry
Even though additive manufacturing has been around for a while, it has only lately become advanced enough to be used in the aerospace sector.
In the aerospace business, additive manufacturing has the potential to deliver significant benefits. Cost savings, design freedom, weight reduction, shorter time to market, fewer waste materials, better efficiency, and on-demand production are just some of the benefits.
Although additive manufacturing cannot make every part, it provides an exciting opportunity to explore feasible alternatives, either supplementing or replacing traditional manufacturing processes. However, it must be taken into account early in the development phase. Additionally, knowledge must be embedded in aircraft design teams to ensure the successful use of additive manufacturing.
However, in recent years, AM has become more prevalent in end-to-end manufacturing. According to Deloitte University Press, the future of AM in aerospace may include:
Directly embedding additively produced electronics
3D printing engine parts
Making battlefield repair components
Top 3 Additive Manufacturing Challenges in the Aerospace Industry and Solutions
While problems are inherent in any new technology, experts overcome them by identifying solutions. Let's look at the top three challenges that the aerospace industry is currently facing and the solutions to overcome them.
Lack of Qualified Experts
Using 3D printers in production and automating work processes are skills that are lacking. However, the obstacles are natural, and the skilled manufacturing workforce is aging and reluctant to adapt to new design models. This is creating the skills gaps surrounding manipulating AM technology.
How to Overcome
Less time spent educating employees is better for business. For example, the US National Additive Manufacturing Institute and the European ADMIRE initiative offer accelerated courses via remote learning websites.
Of course, you'll need to provide numerous additive manufacturing opportunities to attract the key technologists, either on-site or off-site. They will oversee new hires' activities and help them translate their knowledge of 3D printing into designs and final items.
Over Budget Material
The typical cost of AM equipment is $300,000. Industrial consumables cost between $100 and $150 per item (although the final price is formed after choosing the material; plastic, for example, is the most budget-friendly option).
How to Overcome
To overcome this obstacle, you must plan a long-term implementation strategy based on the manufacturing-as-a-service model. On-demand manufacturing reduces manufacturing costs and speeds up product development. You can also go with cheap 3D printers that use cheap welding wire that hasjust come onto the market. They cost $1,200 and may suit your needs.
Fresh Quality Compliance Guidelines
As 3D printing and CNC manufacturing technologies constantly evolve, there are no established norms or regulations for 3D printed objects. However, 3D printed solutions do not always match traditional quality, durability, and strength. For example, a 3D-printed mechanical part. Can someone order 500 similar parts a few months later? Consistency standards and product post-processing may have a negative impact in such circumstances. So, in such a case, traditional manufacturing wins over 3D printing.
How to Overcome
You might endeavor to set quality criteria for your 3D-printed products to ensure they are comparable to traditional ones. You can also apply the ANSI AMSC and America Makes standards, which define quality criteria for 3D printed products.
How Boeing Applies Additive Manufacturing Technology?
Boeing is focusing its efforts on leveraging and speeding up additive manufacturing to transform its manufacturing system and support its growth. The company operates 20 additive manufacturing facilities worldwide and collaborates with vendors to supply 3D-printed components for its commercial, space, and defense platforms.
Boeing is now designing missiles, helicopters, and airplanes using 3D printing technology. A small internal team contributes roughly 1,000 3D-printed components to the company's flight projects. Boeing claims that addressing design as an "integrated mechanical system" considerably improves manufacturability and lowers costs.
Additive manufacturing is altering the way the aerospace industry designs and manufactures aircraft parts. Aerospace advanced manufacturing is making aircraft production easier. We've explored solutions to some of the snags that you may encounter. However, other concerns, such as limited multi-material capabilities and size constraints, require solutions, and industry specialists are working on them. Despite these challenges, additive manufacturing is still booming and rocking in a variety of industries.
Why is additive manufacturing used in Aerospace?
It allows the industry to build quality parts quickly and inexpensively. Reduce waste and build parts for aircraft that are difficult to manufacture using existing methods.
How does additive manufacturing help in Aerospace applications?
Environmental control system (ECS) ducting, custom cosmetic aircraft interior components, rocket engine components, combustor liners, composite tooling, oil and fuel tanks, and UAV components are examples of typical applications. 3D printing helps in producing solid, complicated pieces with ease.
Which aerospace firms use additive manufacturing/3D printing?
Boeing and Airbus are two of the many aircraft businesses that use additive-created parts in their planes. Boeing incorporates additive manufacturing (AM) components into both commercial and military aircraft. Airbus also employs AM metal braces and bleed pipes on the A320neo and A350 XWB aircraft.
"name": "Why is additive manufacturing used in Aerospace?",
"text": "It allows the industry to build quality parts quickly and inexpensively. Reduce waste and build parts for aircraft that are difficult to manufacture using existing methods."
"name": "How does additive manufacturing help in Aerospace applications?",
"text": "Environmental control system (ECS) ducting, custom cosmetic aircraft interior components, rocket engine components, combustor liners, composite tooling, oil and fuel tanks, and UAV components are examples of typical applications. 3D printing helps in producing solid, complicated pieces with ease."
"name": "Which aerospace firms use additive manufacturing/3D printing?",
"text": "Boeing and Airbus are two of the many aircraft businesses that use additive-created parts in their planes. Boeing incorporates additive manufacturing (AM) components into both commercial and military aircraft. Airbus also employs AM metal braces and bleed pipes on the A320neo and A350 XWB aircraft."
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 | November 12, 2021
Robotics industry growth has accelerated rapidly across several industries. It has aided manufacturers in overcoming numerous barriers related to real-time communication, workplace safety, and overall manufacturing cost and timeliness. However, if we trace its history back to 1961 when George Charles Devol introduced the first robot, dubbed 'UNIMATE,' it has exponentially grown and utilized across sectors to make operations more effortless, precise, and faster.
“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.
However, the industry has seen snags or difficult times due to market fluctuations, unfavorable situations, and the need to remain competitive in the drive for expansion. To thoroughly understand the robotics industry, let us examine each component that surrounds it.
Industrial Robotics Global Market Size
According to recent Allied Market Research studies, the global industrial robotics market was worth $37,875 million in 2016 and is expected to reach $70,715 million by 2023, rising at a 9.4% compound annual growth from 2017 to 2023.
Industrial Robotics Market Analysis
The global industrial robotics market is primarily driven by a global increase in labor costs, which has compelled firms to replace human labor with robots. As a result, Asia and Europe are the world's fastest-growing areas, with top companies such as ABB, Fanuc, KUKA, Kawasaki, and Yaskawa Electric Corporation headquartered in the region.
The global market of robotics has been segmented by its type, industry, and function.
Soldering and Welding
Electrical & Electronics
Healthcare & Medicine
Assembling & Disassembling
Rubber & Plastics
Painting and Dispensing
Others if any
Machinery & Metals
Cutting and Processing
Food & Beverages
Precision & Optics
Others if any
Others if any
Industries That Are Pioneering the Use of Robotics
As we have observed, the global robotic market will continue to rise in the future years. Therefore, let us examine which industries will extend their use of robotics in their operations.
Healthcare & Medicine
Medical robots help surgeons optimize hospital logistics and free up the working staff to focus on patients. In the healthcare field, robots are revolutionizing surgery by speeding supply delivery and disinfection and freeing up time for doctors to interact with their patients.
da Vinci System – A General Surgical Robot
The da Vinci System is a surgical robot that focuses on a wide range of urological, bariatric, and gynecological surgical treatments. In addition, Stryker's MAKO System also specializes in orthopedic surgery, specifically partial and total knee replacements.
The da Vinci SP system is cleared for use in the United States exclusively for single-port urological procedures, lateral oropharyngectomy (often referred to as radical tonsillectomy), and tongue base excision.
Police robots are meant to gain access to areas inaccessible or dangerous to first responders, and they are capable of manipulating items and gathering data using several technologies. It encompasses robots capable of operating in various conditions and displaying a range of data and communication capabilities.
Agriculture & Food Industry
Farm equipment is now routinely equipped with sensors that utilize machine learning and robotics to identify weeds, compute the appropriate quantity of herbicide to spray, or learn to detect and pick strawberries, for instance.
Additionally, in the food business, robotics has been used to do repetitive tasks such as picking and placing food items and cutting and slicing food items during any given food item. For instance, the modern bakery business uses robotics to perform traditional craft skills and produce any product in large quantities while maintaining high quality and hygiene standards.
The transportation sector is highly leveraging robotics. The powerful transport capability, advanced control technology, and sensing precision are some of the benefits that make the transportation robots widely utilized in this sector. These benefits from robotics help the sector convey various commodities in factories, restaurants, and medical institutions, among other locations.
Robots are employed in manufacturing to do repeated jobs and streamline the overall assembly process. Additionally, robots and humans can also collaborate on product making. Robots can replace humans for hazardous tasks or processes that need large quantities of materials, which might be hazardous for a human employee to handle.
Factors Sustaining the Growth of the Robotics Industry
Reduces Manufacturing Costs: Robotics application in all industries reduces the overall manufacturing process running costs.
Improves Product Quality: The precision of robotics throughout the manufacturing process helps produce high-quality items that meet target client needs.
Offers Competitive Market: Increased income due to utilizing the benefits of robotics applications makes any industry more competitive.
Speed-ups Production Time: Robotics speeds up production and helps manufacturers increase output.
Offers Task or Process Flexibility: Robotics can weld, cast, mold, assemble, machine, transfer, inspect, load, and unload items, among other duties. So, it gives the manufacturer process flexibility.
Reduces Excessive Use and Waste of Production Materials: Robotics employs the exact quantity of material required for the manufactured product, reducing waste and overuse of materials.
Offers a Safe Working Place: Robotics improves employee health and safety by performing tasks that humans find risky. For example, in the chemical industry, a human employee may not do a hazardous task. In such instances, robots can replace people.
The rise of the robotics industry has accelerated dramatically, and it is now spreading its wings across industries. Research firm IDC provided a projection for the commercial robot market, forecasting that the market will exceed $53 billion by 2022, with a compound annual growth rate of more than 20%. In addition, several advantages of robotics such as safety, productivity, uniformity, and perfection are pushing its expansion and making it an essential element of industry 4.0.
Why are robots the future of the manufacturing industry?
The use of robots in manufacturing has improved process efficiency and product quality. As a result, robots are gaining favor in production and becoming the future of manufacturing.
Which industries make the most use of robotics?
Healthcare, agriculture, food, and manufacturing are the industries that are embracing robotics to get the most out of it.
How is manufacturing utilizing robotics?
Manufacturing uses robotics for repetitive tasks. This helps in the reduction of errors and human efforts. It also improves production efficiency.
"name": "Why are robots the future of the manufacturing industry?",
"text": "The use of robots in manufacturing has improved process efficiency and product quality. As a result, robots are gaining favor in production and becoming the future of manufacturing."
"name": "Which industries make the most use of robotics?",
"text": "Healthcare, agriculture, food, and manufacturing are the industries that are embracing robotics to get the most out of it."
"name": "How is manufacturing utilizing robotics?",
"text": "Manufacturing uses robotics for repetitive tasks. This helps in the reduction of errors and human efforts. It also improves production efficiency."