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 | December 21, 2021
Consumer demand has shifted dramatically in recent years, and manufacturers are trying to adapt to this shift. To maintain high product quality, minimize costs, and optimize supply chains, manufacturing analyticshas become essential for manufacturers.
Manufacturing analyticsis the process of gathering and analyzing data from various systems, equipment, and IoT devices in real-time to get essential insights.
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
Manufacturing analyticscan assist in maintaining production quality, boost performance with high-profit returns, decrease costs, and optimize supply networks.
This article will outline manufacturing analyticsand present a list of possible application cases. It will also highlight the benefits of manufacturing analyticsfor any shop floor or factory.
Manufacturing analytics: An Overview
With manufacturing analytics, we can streamline and speed up the entire process. Data interchange and automation helps in speeding up the production process. Manufacturing analyticsuses predictive manufacturing, big data, Industrial IoT, network virtualization, and machine learningto produce better scalable production solutions.
Manufacturing analyticscollects and analyses data from many sources via sensors embedded in machinery to identify areas for improvement. Data is collected and presented in an easy-to-understand structure to illustrate where difficulties emerge throughout the process.
In short, manufacturing analyticscollects and analyses large volumes of data to reveal insights that might improve performance. Users can also obtain automated business reports to reply in real-time.
Why Manufacturing analytics is Vital for Leading Businesses
There are numerous benefits of manufacturing analyticsthat drive any company’s production and overall manufacturing business growth. The benefits of manufacturing analyticsfall into three distinct categories as below.
It reduces the overall cost: Analytics may save a significant amount of money if used more efficiently. Labor costs are also reduced due to automation and semi-autonomous machinery. Similarly, preventive and prescriptive maintenance programs may save money while enhancing productivity.
It boosts profits for businesses: Manufacturers can respond swiftly to changes in demand using real-time insights in production, inventory management, and demand and supply forecasting. For example, assume the data indicates that they are approaching their maximum capacity. In such instances, they can increase over time, increase capacity, modify procedures, or tweak other production areas to adapt and maintain delivery times.
Other unforeseen benefits: There are several advantages to the increased capabilities enabled by manufacturing analytics. These benefits include lower energy use, safer environmental practices, fewer compliance failures, and more customer satisfaction.
Five Real-world Applications of Manufacturing Analytics
A machine's analytics uses aggregate data from real-time detectors to anticipate when it needs to be replaced or functioning irregularly. This process helps predict machine failure or equipment defects.
Analytics can assist in determining a plant's capacity and how many products are produced by the unit in every production cycle, which is helpful in capacity planning. In addition, analytics may help determine the ideal number of units to create over time by considering capacity, sales predictions, and parallel schedules.
Predictive analytics solutions can automate maintenance requests and readings that shortens the procedure and reduce maintenance expenses.
Product development is an expensive process in manufacturing. As a result, businesses must invest in R&D to develop new product lines, improve existing models, and generate new value-added services.
Earlier, this approach was in place by repeated modeling to get the finest outcome. This approach can now be modeled to a large extent, with the help of data science and technologically superior analytics. Real-world circumstances can be replicated electronically using "digital twins" and other modeling approaches to anticipate performance and decrease R&D expenses.
Many factors that might help in the plan significant capital expenditures or brief breakdowns can be explained using historical data and a few high-impact variable strategies. For example, consider the seasonality of products like ice cream. As a result, historical market data and a few high-impact factors can help explain numerous variables and plan major capital expenditures or short-term shutdowns.
In addition to demand forecasting, predictive analytics incorporates advanced statistical techniques. With predictive analytics, a wide range of parameters, including customer buying behavior, raw material availability, and trade war implications, may be taken into consideration.
Warranty support may be a load for many manufacturers. Warranties are frequently based on a "one-size-fits-all" approach that is broader. This approach introduces uncertainty and unanticipated complications into the equation.
Products may be modified or updated to decrease failure and hence expense by using data science and obtaining information from active warranties in the field. It can also lead to better-informed iterations for new product lines to minimize field complaints.
Managing Supply Chain Risks
Data may be recorded from commodities in transit and sent straight from vendor equipment to the software platform, helping to enable end-to-end visibility in the supply chain.
Manufacturing analyticsallows organizations to manage their supply chains like a "control tower," directing resources to speed up or slow down. They may also order backup supplies and activate secondary suppliers when demand changes.
Businesses should adapt to changing times. Using analytics in manufacturinghas altered the business industry and spared it from possible hazards while boosting production lines. Industry 4.0's route has been carved. Manufacturing analyticsis the key to true Industry 4.0, and without it, the data produced by clever IoT devices is meaningless. The future is data-driven, and success will go to those who are ready to adopt it. The faster adoption, the sooner firms go ahead of the competition.
How can data analytics help manufacturers?
Data analytics tools can help manufacturers analyze machine conditions and efficiency in real-time. It enables manufacturers to do predictive maintenance, something they were previously unable to accomplish.
Why is data so crucial in manufacturing?
Data helps enhance manufacturing quality control. Manufacturers can better understand their company's performance and make changes by collecting data. Data-driven manufacturing helps management to track production and labor time, improve maintenance and quality, and reduce business and safety concerns.
What is Predictive Manufacturing?
Predictive manufacturing uses descriptive analytics and data visualization to offer a real-time perspective of asset health and dependability performance. In addition, it helps factories spot quality issues and takes remedial action quicker by eliminating the waste and the cost associated with it.
Article | April 1, 2022
Digital twin technology in the manufacturing industry is playing a vital role in evaluating current and future production line conditions to increase OEE, productivity, and business profitability. It has become the most critical component of industry 4.0 because it collects precise data about your manufacturing process and uses that data to help you make wiser decisions.
In other words, manufacturers can utilize digital twins to check and assess physical assets, processes, and systems in a virtual environment.
In this article, we will discuss some of the major applications of digital twin technology in the manufacturing industry. Additionally, we also have a look at how this technology helps businesses increase their ROI.
Digital Twin Technology Applications
Product development is a long and intricate process. For example, it might take up to six years to develop and launch a new automobile model. The shift from the previous model to the new model must be seamless. A minor error during this process might have a detrimental effect on the brand's value and revenue. A digital twin software enables the integration of data between previous-generation models and the new concept's digital representations. Additionally, twinning facilitates communication between product designers, end users, and other stakeholders. When it comes to product testing, having digital twin platforms eliminates the need to wait for performance data from car trials to determine the product's performance and quality.
As consumers become more intelligent, and demand personalized items in a timely manner, the manufacturing industry will become increasingly competitive. According to an Industry Week Special Research Report on the future of manufacturing, industrial enterprises of all sizes place a premium on process improvement and customer relationship strengthening, while small businesses focus on addressing customer demand for product customization. To assist in the customization process, manufacturers use Twin Design Customization, which enables the virtual design and re-design of goods prior to generating a physical product that fully meets consumer specifications.
Shop Floor Performance Improvement
The shop-floor digital twin concept helps businesses to be proactive as the system is capable of identifying anomalous situations. This demands attention and process improvements prior to them escalating into a real problem or standstill.
Individual digital twin examples for equipment or manufacturing processes can detect deviations that indicate the need for preventative repairs or maintenance prior to the occurrence of a serious problem. Additionally, they can aid in the optimization of load levels, tool calibration, and cycle times.
Can Digital Twin Boost Business Revenue?
According to a recent study by Juniper Research, revenue from digital twins (a virtual representation of a connected physical product, process, or service throughout its lifecycle) will reach $13 billion by 2023. This is an increase from an estimated $9.8 billion in 2019, representing an average annual growth rate of 35%. The study also discovered that increased deployments of advanced sensors for data collection and technological advancements such as machine learning, artificial intelligence, and high-performance computing are enhancing the benefits of digital twins.
So, how does a digital twin help your company's ROI?
The digital twin improves transformation efficiency by providing platforms and technologies that simulate the impact of process changes in your supply chain – in a safe, secure, and digitally isolated environment – using real-time scenario modeling generated parallel with live supply chain operations. More than a visualization, a digital twin can help accelerate innovation, foster consensus, and save time and money by iteratively modeling changes, testing how components or systems operate, and inexpensively troubleshooting malfunctions in a virtual world.
The digital twin platforms benefit manufacturing organizations across all verticals, including supply chain management, manufacturing operations, and logistics. Thus, technology is accelerating and enhancing the manufacturing industry to obtain more positive results and, in turn, increase its efficiency and, as a result, its return on investment. The digital twin will gain popularity as businesses learn to use it to their advantage. According to a 2020 analysis conducted by Research and Markets, up to 89% of all IoT platforms will incorporate digital twins by 2025. Thus, the future of digital twins is bright, and we may witness increased use of digital twins in the next few years.
How digital twin help businesses?
Digital twins can help businesses make better data-driven decisions. Businesses utilize digital twins to understand the state of physical assets, respond to changes, optimize operations, and add value to systems.
How does digital twin save money?
A digital twin can save time and money by iteratively modeling modifications, testing component or system functionality, and resolving faults in virtual reality.
What are the essential components of digital twin technology?
The concept of the digital twin is composed of three unique components: the physical product, the digital or virtual product, and the linkages between the two.
Article | December 14, 2021
Do manufacturing businesses require Business Intelligence (BI)? The answer is YES. Manufacturing is one of the most data-intensive businesses, producing massive amounts of data ranging from supply chain management to shop floor scheduling, accounting to shipping and delivery, and more.
All of this information would go to waste if not properly categorized and utilized. Scrutinizing and analyzing your data with business intelligence will help you become a more efficientand productive organization. Your organized data can show you where the gaps or inefficiencies are in your manufacturing process and help you fix it.
Many companies simply are not willing to change or think they are done once they make a change. But the truth is technology, consumer demands, the way we work, human needs and much more are constantly changing.
Michael Walton, Director, Industry Executive at Microsoft
BI has the potential to improve the operations of an organization and transform it into an organized one. According to Finances Online research, more than 46% of organizations are already employing a BI tool as a significant part of their company strategy, and according to Dresner Advisory Services research, 8 in 10 manufacturers who use BI for analytics have seen it function successfully.
How Manufacturing Operations Are Improving with Business Intelligence?
As revealed by the BI statistics above, we can see that business intelligence is critical in manufacturing. To further illustrate how business intelligence supports the manufacturing industry, let's look at some of the business intelligence benefits that are making a difference in the manufacturing industry.
Advances Operational Efficacy
While modern enterprises create massive amounts of data, not all of this data is relevant. Today's business intelligence solutions take all of the data from your organization and transform it into an easily comprehensible and actionable format. It enables you to minimize or fix errors in real-time. Additionally, it helps you to forecast raw material demand and assess procedures along the supply chain to ensure maximum efficiency.
Allows for the Analysis and Monitoring of Financial Operations
Business intelligence solutions provide insight into sales, profit, and loss, raw material utilization and can usually assist you in optimizing resources to increase your return on investment. Understanding your cost-benefit analysis, BI enables you to manage production costs, monitor processes, and improve value chain management.
Assists in the Management of Your Supply Chain
Manufacturing companies engage with various carriers, handling these multiple processes can be complicated. BI enables manufacturing companies to have more accurate control over shipments, costs, and carrier performance by providing visibility into deliveries, freight expenditures, and general supplies.
Contributes to the Reduction of Inventory Expenses and Errors
Overstocks and out-of-stocks are substantial barriers to profitability. Business intelligence can assist you in tracking records over time and location while identifying issues such as product faults, inventory turnover, and margins for particular distributors.
Determines the Efficiency of Equipment
Several factors can cause inefficient production. For example, errors with equipment due to improper installation, maintenance, or frequent downtime can reduce production. So, to keep industrial operations running well, one must monitor these factors.
Manufacturers can maintain their machines' health using data analytics and business intelligence. It provides real-time information about your production lines' status and streamlines production procedures.
How Business Intelligence Helped SKF (SvenskaKullagerfabriken) to Efficiently Plan Their Future Manufacturing
SKF is a key supplier of bearings, seals, mechatronics, and lubrication systems globally. The company posses its headquarter in Sweden and has distributors in over 130 countries.
Due to SKF's extensive worldwide reach and product diversity, they constantly need to forecast market size and demand for their products to modify their future manufacturing. Generally, SKF experts developed and kept their forecasts in traditional and intricate excel files. However, the efforts of maintaining and reconciling disparate studies were excessively high. As a result, SKF used require days to generate a simple demand prediction.
Later, SKF integrated its business data assets into a single system by utilizing business intelligence in production. Following that, they could swiftly begin sharing their data and insights across multiple divisions within their firm. They are now able to aggregate demand estimation fast and does not face cross-departmental issues about data integrity for the vast number of product varieties they manufacture.
This intelligent data management enabled SKF to plan their future production operations efficiently.
Business intelligence in manufacturing makes a big difference in the organization's entire operations. Given the benefits of business intelligence in manufacturing, a growing number of manufacturers are implementing it in their operations.
According to Mordor Intelligence, Business Intelligence (BI) Market was worth USD 20.516 billion in 2020 and is anticipated to reach USD 40.50 billion by 2026, growing at a 12% compound annual growth rate throughout the forecast period (2021-2026).
Hence, we may say that the business intelligence is crucial for manufacturing and is booming, thanks to its enormous potential and the numerous benefits it provides to various businesses.
Why is business intelligence so important in manufacturing?
Organization intelligence may assist businesses in making better decisions by presenting current and past data within the context of their business. Analysts can use business intelligence to give performance and competitive benchmarking data to help the firm run more smoothly and efficiently.
What value does BI add to manufacturing?
Business intelligence solutions provide insight into sales, profit, and loss, raw material utilization and can usually assist you in optimizing resources to increase your return on investment. Understanding your cost-benefit analysis enables you to manage production costs, monitor processes, and improve value chain management.
What is business intelligence's key objective?
Business intelligence is helpful to assist corporate leaders, business managers, and other operational employees in making more informed business