Bridging a Skills Gap with Micromachining

JEDD COLE| May 30, 2019
BRIDGING A SKILLS GAP WITH MICROMACHINING
On the surface, R&D Manufacturing may look like any other 100-employee U.S. manufacturer working in sectors like aerospace or automotive, designing and producing metal end products. But R&D Manufacturing manufactures jewelry parts for brands like Tiffany and Co. and Bryan Anthonys, which may order as many as 1 million pieces a year.

Spotlight

Gebr. Pfeiffer

Gebr. Pfeiffer has a long and successful tradition of providing the highest level of quality and service. In a rapidly changing environment, the company remains focussed on the individual requirements of its international customers. Thanks to its commitment to these standards, Gebr. Pfeiffer and its staff will continue to shape the future of processing technology.

OTHER ARTICLES

Manufacturing in 2022 - Five Emerging Trends in the Industry

Article | October 8, 2021

The trends in the manufacturing industry for 2022 are expanding and altering the industry's conventional face. The future of manufacturing is going to merge with digitalization and technological applications. As a result, all operation methods, products, and manufacturing outcomes will be modernized with new technology applications. To brighten the future of manufacturing, manufacturing companies must examine new trends in the industry before developing their manufacturing plans for 2022. Technological advancements are the next game-changer in the manufacturing business. Adeaca's Vice President of Market Innovation and Project Business Evangelist had recently quoted in an interview with Media7 as, “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 The new trends in manufacturing are leveling up every part and element of the industry. In this article, we'll look at a new trend for each industry aspect that's assisting manufacturers in speeding up the production process, increasing ROI, and propelling their manufacturing business to new heights. Additionally, it will assist you in addressing current industry challenges such as forecasting product demand, addressing skilled manpower shortages, and increasing manufacturing plant efficiency. Five Manufacturing Industry Trends to Watch in 2022 Emerging trends in manufacturing provide a chance to review your production strategy for products and processes. Check out below the upcoming trends in manufacturing that are getting attention in the industry. Customer Engagement and Purchase Experience Creating an exceptional digital customer experience is a new trend in manufacturing. According to industry experts, mapping the customer journey and their interactions with your products is the first step towards establishing a positive connection with your potential consumers. A few of the most popular strategies to improve the consumer purchasing experience and engagement are as follows: Build a knowledge base for your products on your business website Create a comprehensive FAQs page that addresses all of the buyer's possible queries Create a chatbot to provide immediate help to the buyer with further inquiries Create a brand story and a comprehensive description of your manufacturing business If possible, provide product statistics and success stories, and content about consumer satisfaction with your product Create a product functionality video or explanatory picture material to familiarize the potential customer with your product These are some of the trends that engage your prospective buyers and increase their purchasing experience through a range of product-related information and educate them about you and your products. Smart Technology-enabled Products Smart is the new norm in every industry. The old operations and goods that were once a part of everyone's life have now been replaced by technology. Manufacturing is no exception to this alteration. Due to the increasing demand for smart products among customers, every company is now looking forward to inventing and manufacturing smart products. Explore and understand how you may incorporate cutting-edge technology (Artificial Intelligence, Machine Learning, Edge Computing, and Digital Twins, and more) into your products to help them stay updated with manufacturing trends. Virtual and Augmented Reality in Manufacturing (Industry 4.0) Transforming traditional manufacturing systems and processes into smart, tech-savvy ones is a new trend in manufacturing. The future of manufacturing is expected to witness this digitization in 2022 and beyond. Therefore, you must convert your conventional manufacturing plants into smart ones, i.e., as per the concept of Industry 4.0 – the fourth industrial revolution. Discover how prominent companies are implementing Industry 4.0 The following are some popular transformations that many popular manufacturing factories are adopting to become part of the industry revolution. To achieve a zero-carbon footprint, manufacturers may use analytics systems to determine the amount of trash they create and develop ways to eliminate it. (Implemented by Whirlpool) Utilize an analytics platform to decipher usage data for energy, water, and other utilities. (Implemented by Whirlpool) Utilize technology such as Siemens' Mindsphere, which enables online analysis of several aspects of a production plant and helps manufacturers create digital models using real-time data. (Implemented by Siemens) Utilize a combination of IoT and cloud-based technologies to avoid downtime and gather analytics data. (Implemented by Hirotec – a Japan based manufacturing company) Machine learning technology can be used to foretell and avoid system failures in your manufacturing plant. (Implemented by Hirotec – a Japan based manufacturing company) Utilize robotics and to accelerate manufacturing across many verticals. (Implemented by Ford) Utilize 3D printing to improve the precision of product design and to avert product defects during the early production stage. (Implemented by Aerospace: Airbus) These are some examples that other well-known manufacturing companies in the market, such as Hewlett-Packard, Ford, Whirlpool, and Siemens are currently using. So, consult an expert and determine how to leverage emerging technology to turn your production plant into a smart manufacturing unit. Internet of Things (IoT) to Boost Revenue Manufacturing companies have begun to leverage the Internet of Things to establish connectivity between machines and operational procedures throughout manufacturing. This linkage between machine and operation significantly decreases the human supervision required for each step and completely automates them. Manufacturers intend to incorporate these IoT trends in manufacturing into both their products and operational processes. IoT further enables manufacturers to operate and monitor their work remotely. As a result, they can concentrate on developing new strategies and preparing for future ventures. Shifting Focus from B2B to B2C Model Several manufacturers skip intermediaries and connect directly with their consumers to sell efficiently to their target consumer group. This purposeful approach has multiple benefits, which are outlined below. Manufacturers may skip the lengthy retail sales cycle and achieve a shorter time to market The absence of a third party between the manufacturer and the customer reduces the risk of brand misinterpretation or dilution Direct interaction with customers enables manufacturers to obtain more accurate consumer data, product feedback, and requirements for new product development Manufacturers can control the price of their products due to the absence of a third party between them and the target consumer group These benefits of the B2C model attract manufacturers and encourage them to develop added production techniques with these benefits in mind. Final Words Technology, innovation, and digitization are the future of manufacturing. The IoT trends in manufacturing are essential for industrial production and will allow the manufacturing industry to obtain a new competitive edge. Hence, manufacturers must keep in mind this industry revolution (industry 4.0 and 5.0) while developing strategies for their manufacturing operations in 2022. FAQs What are the benefits of adopting the Internet of Things in manufacturing? IoT devices can monitor industrial operations, manufacturing cycles, and other warehouse data management processes automatically. This benefit decreases the amount of time spent monitoring individual operations and increases production speed. What role will smart manufacturing play in the future? According to a grand view research analysis, the smart manufacturing market was worth USD 236.12 billion in 2020 and is expected to extend at a 12.4 percent compound annual growth rate to reach USD 589.98 billion by 2028. What are the critical components of the smart factory of the future? Robotics, the Internet of Things, big data, and cloud-based administration will be critical components of the future smart factory.

Read More

It's Time to Redesign Your Business with Manufacturing Analytics

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 Predictive Maintenance 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 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. Demand Forecasting 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 Analysis 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. Final Words 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. FAQ 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.

Read More

Quality Digest Defines Enhanced Webinar Events

Article | May 18, 2021

For twenty years as an editorial contributor to Quality Digest magazine, I have had the pleasure of authoring or collaborating more than 80 articles for the publication. During this two-decade tenure, I have worked with Dirk Dusharme (pictured left), Editor in Chief of Quality Digest. Quality Digest’s website receives more than three million page views each year, which provide editorial content, live broadcasts, videos, and on-demand webinars presented by industry experts on international quality standards, leadership, manufacturing, metrology, statistical process control, training, and more. Quality Digest continues its important role as companies navigate a post-COVID reality with a critical role of safety, quality, efficiency, and resiliency. Quality elements are no longer an after-thought. It is essential when examining automation, lean manufacturing, and new paradigms for best practice. During COVID, all of us became more remote savvy and the demand for visionary content and information essential. According to Dusharme, “Since their debut almost a decade ago, Quality Digest's "enhanced" webinar events have raised the bar for the traditional webinar experience. Our audience has come to expect concise, informative, and engaging presentations with subject matter experts who know what they are talking about. Apart from the traditional quality topics, we delve into areas that broaden our audience’s knowledge. These topics range from cybersecurity, to supply chain management, to understanding and dealing with cognitive biases. Our goal is to provide up-to-date, actionable information that our audience can immediately put to use. Live video feeds of the presenters and the products, interactive Q&A sessions, surveys, and valuable downloads all make up our usual webinar experience, followed by next-day access to the on-demand recording and materials.” Enhanced Webinars from Quality Digest feature real-time streaming video of host, subject matter expert, and a case study in action. Users can email questions, chat, or download files in real-time. This modality is ideal for visual case studies/product demos, team or customer training, and new product/new service announcements.

Read More

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." } }] }

Read More

Spotlight

Gebr. Pfeiffer

Gebr. Pfeiffer has a long and successful tradition of providing the highest level of quality and service. In a rapidly changing environment, the company remains focussed on the individual requirements of its international customers. Thanks to its commitment to these standards, Gebr. Pfeiffer and its staff will continue to shape the future of processing technology.

Events