Valoptim and Sculpteo: 3D printing architectural models for customers!

LUCIE GAGET | October 10, 2018
3D printing is well used for architecture. We can see that 3D printed houses are becoming a reality and that 3D printing could become a common manufacturing technique in architecture. Additive manufacturing can also be used for other application in this sector, and that is precisely what we can see with, Valoptim, a property development company which decided to work with us to 3D print architectural models.

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Mega Techway, Inc.

Mega Techway was founded in 2004 to specialize in contract manufacturing of custom cable assemblies, wire harnesses, and electromechanical interconnect assemblies to meet a wide array of markets and OEM customer requirements.

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Cyber Threats to Manufacturing Companies and Ways to Mitigate

Article | March 4, 2022

Cyber manufacturing is a term that refers to a modern manufacturing system that allows for asset management, reconfiguration, and productivity maintenance in a way that is easy to see and use. Industry 4.0 anticipates an era of enormous opportunity for innovation and prosperity. Additionally, it introduces new risks and challenges in today's manufacturing cyber scene. “Cybersecurity is starting to become more prevalent within organizations, so opportunities to grow in this industry will never end if you have the correct drive and determination.” – Joe Boyle, SEO of SaltDNA Numerous manufacturing organizations are experiencing an increase in cyber-attacks on control systems used to oversee industrial processes. Some of these systems may include programmable logic controllers and distributed control systems, as well as embedded systems and industrial Internet of Things (IoT) devices. To help you develop a strong and secure manufacturing operation, this article will outline the multiple sorts of cyber-attacks in manufacturing and how you may improve manufacturing security. Let's begin with the importance of cybersecurity in the manufacturing industry. Why is Cybersecurity in Manufacturing Crucial? From January to March of 2019, the number of ransomware attacks in the manufacturing industry has increased by 156%. This is a big change, so it's important to have strong cyber security in the manufacturing process. Wherever software is in use, there is a high probability of cyber-attacks. The manufacturing industry is digitizing itself with cutting-edge technologies connected via the internet and various software. Therefore, the manufacturing industry is particularly vulnerable to cyber-attacks. The following are some of the key reasons why manufacturers should prioritize manufacturing cybersecurity: Increase in the use of IoT devices in the industry Increase in the cost of data breaches Increase in the number of cyber-attacks across industries Increase in the severity of cyber-attacks Increase in the use of widely accessible hacking tools Increase in the use of remote workers Five Major Types of Manufacturing Cyber Attacks Ransomware Due to the rising value of ransomware, cybercriminals have switched their attention away from selling personal and financial data. Unfortunately, industrial companies stand to lose a lot. Until the hacker's demands are met, this malware locks files on a network and makes them impossible to use. If a ransom (typically millions) is not paid, threat actors may sell or leak important data. Until the ransom is paid, ransomware users render the company's network inaccessible. This strategy works well for attackers in the manufacturing industry because downtime is costly, and no manufacturer would like to encounter it for a long time. Ransomware assaults generally occur on weekends or holidays to maximize damage before the attack is realized. This allows hackers to wait in comfort during a busy manufacturing period. Manufacturing enterprises are a desirable target for numerous reasons. A wide network of OT devices and a long supply chain make many endpoints and security flaws. Phishing Phishing is the most common type of network assault. Phishing emails are frequently used to gain access to a target firm to carry out further detrimental assaults or acts. For instance, in 2016, a CEO sent an email to a global solar panel manufacturer’s employee. The email claimed that precise information about internal employees was required. The employee transmitted the data without confirming it. The CEO received the information. Unfortunately, the CEO was a cybercriminal, and the employee was phished, disclosing firm secrets. Perhaps the next generation of thieves will commit even more advanced and sophisticated penetrations and attacks. Phishing attacks are characterized by the following characteristics: Emails with malicious attachments Emails with hyperlinks that differ from well-known websites and are misspelt Emails with an attention-grabbing title or content Emails from an unusual sender Urgent orders or to-do items Supply Chain Attacks In the manufacturing business, no single firm can complete the entire production cycle. It must rely on several manufacturers' parts and components to complete the manufacturing and assembly of the entire product. As a result, numerous parties should coordinate to ensure an effective production process. This technique introduces the risk of supply chain attacks. Numerous criminals utilize supply chain hacks to steal critical data and intellectual property rights from manufacturers. If a malicious attacker gets permission from the manufacturer's partner to access their network, they may steal critical information or data, and even essential manufacturing records, wreaking havoc on the business. Additionally, manufacturers' external software or hardware poses security vulnerabilities, and there is a danger of attack along the equipment and system supply chain. Most products are developed using open-source or closed-source components, yet all these components have some level of security vulnerability. The following are common indicators that your network has been compromised by a third party: Incorrect usernames and passwords are used to access software systems Strange redirects to unknown websites Pop-up advertisements Ransomware messages Software freezes or crashes IoT Attacks As the intelligent transformation of manufacturing continues to progress, the Internet of Things' role in facilitating this process becomes increasingly critical. Manufacturers can optimize production processes more effectively and precisely by utilizing various IoT devices. For instance, businesses track assets, collect data, and perform analysis using IoT sensors embedded in devices. These sensors continuously monitor the various operating parameters of the equipment and critical data to enable automatic recovery and minimize maintenance downtime. Increased security risks occur because of the proliferation of various IoT devices in manufacturing plants. IoT devices have networking capabilities and can be easily connected to a network. Typically, manufacturers' IoT, industrial control, and office networks are not adequately isolated. They can get into the industrial control network through public flaws or zero-day attacks on IoT devices. They can then launch malicious attacks on critical production equipment, which can stop production and cause processing accidents. Insider Threats Most manufacturing cyber attacks are carried out by outsiders, but nearly 30% originate from insiders or those with access to the company. As with external hackers, these attacks are frequently motivated by financial gain. However, some employees or former employees attack a business out of rage or dissatisfaction. Internal threat actors do not require network access. They can access sensitive data by leveraging their existing knowledge or credentials. A threat actor is more likely to carry out an attack invisibly and undetected with pre-existing credentials. Unfortunately, former employees can typically access this information if passwords or entry methods are not changed to prevent such attacks. Because of the increased use of personal devices and remote work, employees can unintentionally be the cause of an internal breach. Most businesses were unprepared for the regulations that would accompany a global pandemic. As manufacturing companies looked for ways to stay afloat by maintaining employees remotely, few had the necessary technological equipment to keep each employee as safe as the company's employees. Many home-based employees discovered that working from home was not easy, as the line between personal and work time became increasingly blurred and eventually vanished. For hackers, these home networks and the use of unprotected personal devices have opened a new avenue for obtaining sensitive data from large andsmall businesses. How to Mitigate Manufacturing Cyber Attack Make Sure Your Software Is up to Date Install software patches to prevent attackers from exploiting known issues or vulnerabilities. Numerous operating systems include an automatic update feature. If available, ensure that this option is enabled. Utilize Current Antivirus Software Install software patches to prevent attackers from exploiting known issues or vulnerabilities. Numerous operating systems include an automatic update feature. Ensure that this option is enabled if it is available. Make Use of Strong Passwords Set up password rules. A stolen or default password is used in 63% of confirmed data breaches. Create strong passwords that are difficult to guess and use unique passwords for each program and device. Experts advise using passphrases or passwords of at least 16 characters. Make Use of MFA Tool MFA validates a user's identity using at least two identification components. This stops attackers from taking advantage of weak authentication mechanisms, which lowers the risk of someone getting into your account even if they know the login credentials. Train Employees on Security Awareness Security awareness training unites employees, eliminates risks and events, and protects both the company and the employees. Employees should also be taught how to look for and deal with threats like phishing. Final Word Industry 4.0 is all about smart technologies that operate with the help of the internet. It increases the probability of manufacturing equipment and software being hacked. Therefore, while you intend to create a smart environment in your manufacturing facility, you must take the necessary cyber security measures. The strategies mentioned in this article to mitigate the cyber-attacks will ensure that you take every precaution to keep the working environment safe. There are many ways to protect your manufacturing business from cyberattacks. The techniques and the types of attacks described in this article will help you know what to opt for and which attacks to look for in your manufacturing business. FAQ What are the most common cyber security threats? Phishing attacks are the most common cyber security threats that employees fall for. With the advancement of phishing attacks, many employees lack the knowledge necessary to spot a phishing email. Additionally, many employees have poor cyber security practices, such as using the same password for work and personal devices, which is also one of the reasons for rising phishing attacks. What are the cyber security challenges in Industry 4.0? Smart factories are vulnerable to the same types of attacks as conventional networks, including vulnerability exploitation, malware, denial of service (DoS), device hacking, and other typical attack tactics. What is CPS in manufacturing? CPS (Cyber Physical Systems) are defined as designed systems that are comprised of and reliant on the seamless integration of computer algorithms and physical components.

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Real-Time Data Collection in Manufacturing: Benefits and Techniques

Article | January 12, 2022

Real-time manufacturing analytics enables the manufacturing base to increase its efficiency and overall productivity in a variety of ways. Production data is an effective means of determining the factory's efficiency and identifying areas where it might be more productive. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore, an American Management Consultant and Author Creating a product-specific data collection may assist you in determining and visualizing what needs to be improved and what is doing well. In this article, we'll look at why manufacturing data collection is vital for your organization and how it may help you improve your operations. Why is Manufacturing Data Collection so Critical? Visibility is the key benefit that every manufacturer gets from manufacturing data collection. By collecting real-time data, or what we refer to as "shop floor data," manufacturers better understand how to assess, comprehend, and improve their plant operations. Manufacturers can make informed decisions based on detailed shop floor data. This is why having precise, real-time production data is critical. “According to Allied Market Research, the worldwide manufacturing analytics market was worth $5,950 million in 2018 and is expected to reach $28,443.7 million by 2026, rising at a 16.5% compound annual growth rate between 2019 and 2026.” For modern manufacturers, the advantages of data collection in manufacturing are numerous. The manufacturing industry benefits from production data and data-driven strategy in the following ways. Substantial reduction in downtime by identifying and addressing the root causes of downtime. It increases manufacturing efficiency and productivity by minimizing production bottlenecks. A more robust maintenance routine that is based on real-time alerts and machine circumstances. Improvements in demand forecasting, supplier scoring, waste reduction, and warehouse optimization reduce supply chain costs. Higher-quality goods that are more in line with customers' wishes and demands depending on how they are utilized in the current world. So, after looking at some of the significant benefits of real-time manufacturing analytics, let’s see what type of data is collected from production data tracking. What Sorts of Data May Be Collected for Production Tracking? Downtime: Operators can record or track downtime for jams, cleaning, minor slowdowns, and stoppages, among other causes, with production tracking software. In the latter scenario, downtime accuracy is optimized by removing rounding, human error, and forgotten downtime occurrences. The software also lets you categorize different types of stops. Changeovers: Changeovers can also be manually recorded. However, changeovers tracked by monitoring software provide valuable data points for analysis, considerably reducing the time required for new configurations. Maintenance Failures: Similar to downtime classification, the program assists in tracking the types of maintenance breakdowns and service orders and their possible causes. This may result in cost savings and enable businesses to implement predictive or prescriptive maintenance strategies based on reliable real-time data. Items of Good Quality: This is a fundamental component of production management. Companies can't fulfill requests for delivery on schedule unless they know what's created first quality. Real-time data collection guarantees that these numbers are accurate and orders are filled efficiently. Scrap: For manufacturers, waste is a significant challenge. However, conventional techniques are prone to overlooking scrap parts or documenting them wrong. The production tracking system can record the number and type of errors, allowing for analysis and improvement. Additionally, it can capture rework, rework time, and associated activities. WIP Inventory: Accurate inventory management is critical in production, yet a significant quantity of material may become "invisible" once it is distributed to the floor. Collecting data on the movement and state of work in progress is critical for determining overall efficiency. Production Schedule: Accurate data collection is essential to managing manufacturing orders and assessing operational progress. Customers' requests may not be fulfilled within the specified lead time if out of stock. Shop floor data gathering provides accurate production histories and helps managers fulfill delivery deadlines. Which Real-time Data Collection Techniques Do Manufacturers Employ? Manufacturers frequently employ a wide range of data collection techniques due to the abundance of data sources. Manual data collection and automated data collection are two of the most common data collection methods. Here are a few examples from both methods: IoT: To provide the appropriate information to the right people at the right time with the correct shop floor insight, IoT (Internet of Things) sensor integration is employed. PLC: The integration of PLC (Programmable Logic Controller) is used to measure and regulate manufacturing operations. HMI: It can provide human context to data by integrating line HMI (Human Machine Interface) systems (such as individual shop terminals like touch screens located on factory floor equipment). SCADA: Overarching management of activities with SCADA (Supervisory Control and Data Acquisition) systems. CNC and Other Machines: Integrating CNC and other machines (both new and older types) to keep tabs on production efficiency and machine well-being is a must these days. Final Words One of the most challenging aspects of shop floor management is determining what to measure and what to overlook. The National Institute of Standards and Technology recently conducted a study on assisting manufacturing operations in determining which data to collect from the shop floor.Additionally, you may utilize the manufacturing data set described above to obtain information from your manufacturing facility and use it strategically to improve operations, productivity, efficiency, and total business revenue in the long term. FAQ What is manufacturing analytics? Manufacturing analytics uses operations and event data and technology in the manufacturing business to assure quality, improve performance and yield, lower costs, and optimize supply chains. How is data collected in manufacturing? Data collection from a manufacturing process can be done through manual methods, paperwork, or a production/process management software system.

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Building a Smart Factory is Possible Using Machine Learning

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. Final Words 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. FAQ 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. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How is machine learning used in manufacturing?", "acceptedAnswer": { "@type": "Answer", "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." } },{ "@type": "Question", "name": "Which two forms of machine learning are there?", "acceptedAnswer": { "@type": "Answer", "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." } },{ "@type": "Question", "name": "What is a machine learning model?", "acceptedAnswer": { "@type": "Answer", "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." } }] }

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Technologies That Will Keep You Ahead in the Manufacturing Realm

Article | November 20, 2021

Modern manufacturing methods are pioneering and adopting manufacturing industry advancements. To remain competitive in the present era and provide the most excellent industry solutions to your organization and target customer group in 2022, you must employ new manufacturing technologies in your manufacturing processes. Additionally, embracing current technologies is the ideal approach to tackle the industry's current challenges such as workplace safety, digitalization of operations, and a lack of skilled workers. This article will discuss some of the leading manufacturing technologies that transform traditional manufacturing facilities into smart manufacturing factories. So, let us begin. Manufacturing Technology & Innovations for 2022 To better understand industry 4.0, let's look at some of the manufacturing technologies that will dominate the manufacturing industry in 2022. 3D Printing Numerous industries, including aerospace, healthcare, electronics, and architecture, utilize 3D printing in manufacturing. It is the most widely used technology across industries and will remain so in 2022 and in the years to come. We may also anticipate more advancements in this technology to help overcome current barriers to 3D printing adoption, including equipment costs, material constraints, lengthier manufacturing times, a lack of knowledge, and legal issues. Additionally, it would assist manufacturers in overcoming current manufacturing challenges such as increasing product demand, increasing automation, and locating and retaining the workforce in manufacturing plants. It is vital to incorporate 3D technology into production processes to achieve greater precision and accuracy in manufacturing. IoT The Internet of Things is a critical component of the industry 4.0 revolution. It has altered the environment of data collection and analysis across sectors. For example, the Internet of Things is assisting manufacturers in better understanding manufacturing and supply chain operations, forecasting product demand, and boosting customer experiences. Implementing IoT in your manufacturing plant will also help you avoid production delays and increase the performance of your production lines. Additionally, it will decrease equipment downtime and improve process efficiency. It also enhances worker safety and enables more effective labor management. To begin implementing IoT in your manufacturing plant, you must first examine your manufacturing processes and research how other organizations have implemented IoT in their manufacturing processes or products. This method will assist you in determining the optimal location to begin integrating the IoT in your manufacturing plants and transforming them into smart ones. “Once you start to look at yourself in the right way and realize that projects are at the core of your business, it is easy to see how you should use technology to support your business.” – Matt Mong, VP of Market Innovation and Project Business Evangelist at Adeaca. GD & T The model created in the CAD program for any product is not exactly replicated with the exact dimensions during the production procedures. Thus, manufacturers or engineers utilize GD&T (Geometric Dimension &Tolerancing) to manage and communicate the permissible variation within a product assembly to manufacturing partners and inspectors. GD&T is a programming language that enables developers and inspectors to optimize functionality without incurring additional costs. The primary advantage of GD&T is that it expresses the design intent rather than the final geometry. However, as with a vector or formula, it is a representation of the actual item. AR & VR The two primary transformation aspects in the industry 4.0notion are augmented reality (AR) and virtual reality (VR). AR technology in manufacturing enables firms to operate more efficiently by reducing production time. Additionally, it discovers and resolves manufacturing process difficulties. Virtual reality technology benefits the industrial business in a variety of ways. It enables product designers to mimic their prototypes or models using powerful virtual reality software. This enables them to correct faults at the first stage of production and minimize production time and cost. Additionally, the technology provides additional benefits, such as increased workplace productivity and safety. ERP Enterprise Resource Planning (ERP) refers to a comprehensive end-to-end software solution that is used across sectors. It assists the manufacturing business in successfully maintaining production processes and other operational data by avoiding numerous roadblocks along the way. ERP technology enables enterprises to improve process efficiency and product quality by tackling industry-specific difficulties such as insufficient data, operation integration, inventory control, supply chain management, and on-time delivery. Discover How John Deere Manufactured Their Tractors Using Cutting-edge Technologies John Deere is a significant firm that embraces innovation and the Internet of Things. The company integrates Internet of Things sensors, wireless communication, and intelligent land management systems. It further integrates IoT tools into its manufacturing process, bridging the gap between technologies. Additionally, the company is a pioneer in GPS technology. Its most modern technology, which it incorporates into tractors, is accurate to within two centimeters. Additionally, the organization has implemented telemetry technology for predictive maintenance. Final Words Manufacturing innovations are assisting manufacturers in modernizing their traditional manufacturing processes. Modern manufacturing is equipped with modern technologies that aim to improve the processes and goods, increasing the manufacturers' commercial revenues. So, to remain competitive in this age of technological innovation, manufacturers must update their manufacturing processes to remain relevant in today's manufacturing world. FAQ What is manufacturing innovation? Manufacturing innovation includes new technology, supply chain modifications, and product and process improvements. As a result, businesses can benefit significantly from innovation and typically surpass their competitors. Which technologies are considered to be a component of advanced manufacturing? 3–D printing, robotics, IoT, nanotechnology, cloud computing, robotics, and big data are the significant components of advanced manufacturing. How are cutting-edge technologies assisting the manufacturing sector? The cutting-edge technology can precisely estimate demand to set production objectives, analyze machine data to predict when parts will break before a human operator can detect, and more. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is manufacturing innovation?", "acceptedAnswer": { "@type": "Answer", "text": "Manufacturing innovation includes new technology, supply chain modifications, and product and process improvements. As a result, businesses can benefit significantly from innovation and typically surpass their competitors." } },{ "@type": "Question", "name": "Which technologies are considered to be a component of advanced manufacturing?", "acceptedAnswer": { "@type": "Answer", "text": "3–D printing, robotics, IoT, nanotechnology, cloud computing, robotics, and big data are the significant components of advanced manufacturing." } },{ "@type": "Question", "name": "How are cutting-edge technologies assisting the manufacturing sector?", "acceptedAnswer": { "@type": "Answer", "text": "The cutting-edge technology can precisely estimate demand to set production objectives, analyze machine data to predict when parts will break before a human operator can detect, and more." } }] }

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Spotlight

Mega Techway, Inc.

Mega Techway was founded in 2004 to specialize in contract manufacturing of custom cable assemblies, wire harnesses, and electromechanical interconnect assemblies to meet a wide array of markets and OEM customer requirements.

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