Developments

Freedom to think

Top three applications for machine learning in manufacturing Machine learning means machines do not have to be programmed to perform exact tasks on a repetitive basis, they collect data and use it to make informed decisions about their next move. This allows them to correct any errors and improve their operational parameters. There are three key areas where manufacturers can benefit from this technology. Industrial maintenance According to McKinsey, artificial intelligence can generate a ten per cent reduction in maintenance costs, up to a 20 per cent reduction in downtime and a 25 per cent reduction in inspection costs. Machine learning is a significant player in this positive impact of artificial intelligence. In traditional predictive maintenance, engineers program the thresholds for a component’s normal operation into a supervisory control and data acquisition (SCADA) system. When the component deviates from normal operation, the system alerts an engineer of the developing fault. The problem with this approach is the lack of flexibility. It does not take into consideration variations in plant activity or the context of manufacturing processes. For example, a system may detect a sudden increase in a component’s operating temperature and interpret this as a developing fault, when in fact it is due to the machine being sterilised. Machine learning technology means predictive maintenance systems do not have to be programmed with normal operating thresholds. They use data from the factory floor and IT systems to monitor operational patterns and make informed decisions about what is normal and abnormal activity. Quality assurance There are two main ways machine learning can improve quality assurance (QA). Firstly, it enables assembly robots to continuously monitor and optimise their processes. Secondly, machine learning increases the capabilities of machine vision systems. Like with predictive maintenance, traditional machine vision systems for QA lack flexibility. For example, […]

The word globalisation is out of date. This is what should replace it

The word globalisation has lost its relevance and lustre with the emergence of the new global economy of the 21st century. In fact, it’s become an anachronism. Its deficiency is that it’s not a new concept which creates nuances of confusion. Globalisation describes the international outreach of countries for the purpose of economic, social, political and cultural liaisons. Global linkages between countries through military conquest, colonisation, multilateral free trade agreements and cultural exchange existed in an uninterrupted continuum in the evolving history of humankind. Historically, however, the process of globalisation has taken different forms, and its meaning has changed and mutated since it was first used in English in the 1930s. For lack of a better word, the term received an extension to its lifeline until the end of the 20th century despite the profound structural changes and technological advances that began to take place in the 1980s. Therefore, globalisation has come to be used to represent “modern” 20th century economics and social processes, including the instant exchange of money enabled by internet technologies. Yet because of its varied historical use, it does not truly reflect the electronic and digital empowerment that has been unleashed by the information technology revolution. Therefore, globalisation is not an accurate descriptor of the 21st century and the internet-driven transformational change sweeping the international economic landscape. And so I have coined the term internetisation which I believe should replace the concept of globalisation. Internetisation: Time, geography are irrelevant Internetisation is the contemporary face of globalisation. It includes the modern tools of electronic globalization and embraces the digital connectivity and empowerment of the internet and the World Wide Web. Although we could widen the term globalisation to include internetisation, I believe it is better to replace the word globalisation altogether because the long duration of its […]

Working across the supply chain

Industry 4.0 is changing the way we work across the supply chain. Using AI, sensors, and Internet of Things (IoT) technology, a smart and data-driven distribution center can be developed. For example, by cross-referencing enterprise resource planning (ERP) systems with consumer trends data, AI technology can automatically order the correct amount of raw materials to fulfil orders, reducing waste and increasing profit. As the complex web of distribution is opened to the benefits of AI, the supply chain could have a bigger economic benefit than any other application of AI in manufacturing. Using this technology, distributors will no longer need to predict demand for products through guesswork but will instead merge datasets to make accurate predictions about the future, enabling them to make well-informed business decisions. Inventory level efficiency With insight into future demand, AI can also help with forecasting the demand of your suppliers, based on previous orders. This means crucial decisions can be made to optimize stock levels. For example, if your AI software lets a distributor know that many other distributors will want the same equipment in 12 months’ time, you would be sure to jump the queue and get ahead by ordering it much sooner than this. Cost of goods sold Why does it matter if inventory levels aren’t optimised? Well, it’s related to inventory level efficiency. Your cost of goods sold (COGS) will reduce since you don’t incur costs of holding inventory beyond its use. In 2015, the cost of over-stocking was $470 billion, and of under-stocking was $630 billion worldwide, according to IHL Group. Freeing up cash and storage space creates the potential for savings. Lead times As Industry 4.0 empowers your supply chain to manage different orders faster, lead times for customers will shorten. However, this increases the pressure to deliver on time, […]

Tethered drone and sensors operation unlimited

Sky Sapience’s UAV platform, HoverMast- Lite (HM-L1 and HM-L3.) is designed to function as smaller, light-weight versions of their flagship HoverMast platform. The HM-L systems will be used in tandem with small robotic vehicles participating in the RCV/SMET programs. The HM-L systems maintain the impressive capabilities of the original HoverMast product line; such as 100m hovering, 24/7/365, autonomous, on the move, bad weather operations, fibre optic communication, cyber immunity and operation in GPS denied environments, while also meeting the size, weight and electric power requirements of the RCV and SMET programs. The unique design concept behind the HoverMast-Lite enables the tethered drone and sensors operation without limiting the vehicle’s continuous work. Moreover, the HM-L does not occupy the vehicle’s bed, allowing it to remain available for additional equipment and sensors. The HM-L concept is internationally patent protected. The HoverMast-Lite has the ability to incorporate most payloads weighing up to 6.0 kg; including CCD/IR cameras, radars, lasers, cellular and mesh communication devices, hyperspectral sensors and cyber systems. HM-Lite and its payload are both easily and intuitively controlled and monitored from a single ruggedised Human- Machine Interface (UHMI.)

Nintex unveils major enhancements to Workflow Cloud

Nintex, the global standard for process management and automation, announced significant enhancements to its cloud-first process automation solution, Nintex Workflow Cloud, including a market first offering – the ability for customers to quickly and easily create workflows that include robotic process automation (RPA) bots. This new feature within Nintex Workflow Cloud called Nintex Gateway, provides two-way, drag-and-drop interaction between workflows and bots. In addition, Nintex subscription-based workflow customers now have access to RPA bots included with their current service. With Nintex Gateway, customers utilise bots to achieve end-to-end automation across a much broader set of process scenarios. Process workflows can also delegate tasks to Nintex RPA bots that mimic human keystrokes and mouse clicks to interact with apps, systems, services, and websites through their existing screens and interfaces. This results in less work being assigned back to the user and helps organisations leverage cloud workflow capabilities while protecting their on-premises investments. Nintex Workflow Cloud enhancements accelerate configuration, results and compliance Additional new features within Nintex Workflow Cloud include easy testing of workflows as they are built. This helps workflow designers get quick feedback to ensure their workflows are correctly configured before they are published. Workflow designers can also activate new “task authentication” features to require the person assigned to execute a task to first log into Nintex Workflow Cloud, ensuring that only authorised users may take that action. Existing Nintex Workflow Enterprise customers will now have full access to attended and unattended RPA bots within their current workflow subscriptions to enhance the value of their automation with Nintex RPA bots. Nintex customers can create and train RPA bots to execute specific automation tasks – called botflows – just like they create workflows today with Nintex. This means that Nintex customers can allocate existing workflows towards deploying botflows, providing enormous automation […]

New subscription model offered with FARO CAM2 software

Latest release provides performance enhancements and new features including an updated statistical process control tool. FARO Technologies Inc. a global leader in 3D measurement and imaging solutions, announces the latest iteration of its CAM2 2020 Software. The release includes a variety of performance and user interface improvements, new features and a new subscription licensing option. Users can now achieve greater control over their full manufacturing process at a lower up-front cost in this latest iteration of the metrology software platform. The new subscription model empowers users to benefit from CAM2 with a lower initial investment. It offers scalability through a flexible licensing model and ensures users always have access to the latest and most up to date version of CAM2. FARO CAM2 is a powerful, intuitive and application-focused 3D measurement platform designed to help users efficiently fulfil their quality assurance and inspection tasks. FARO CAM2 2020 is helping users get the most from their manufacturing processes, with an intuitive, streamlined and application-focused platform. Through a continuous improvement process, user feedback and requirements are continually collected, integrated and deployed. FARO CAM2 2020 is the culmination of these efforts, which lead to a variety of new features, including an enhanced measurement experience and an updated statistical process control tool that assists users in identifying production data trends that may indicate when a process is moving out of a specified parameter. Being able to predict this kind of error reduces wasted time, scrap and rework, and helps keep production capacity at full strength. As part of an established line-up of smart features, this release represents a fully realized solution for the everyday production tasks of the customer.

How Omron keeps your production flowing

Flow sensors have been used throughout industry for many years. However, Omron’s new E8FC series introduces dual parameter sensing, where a single meter measures the temperature of a liquid, as well as its flow. The simultaneous monitoring temperatures means many more abnormalities can be detected than when only flow is measured. Typical applications are measuring cooling water used in moulding and welding. The water must not only flow, it must also be below a certain temperature to be effective. Faulty or substandard products could result if either is incorrect. Three different models are available, analogue only or analogue + IO Link (both with COM2 and COM3 baud rates). All models have a digital output for external alarm detection, which allows for easy replacement of existing units. The meter uses its IO Link channel to return the flow rate, temperature and a host of configurable alarm bits, set by threshold values. This allows users to keep a close eye on proceedings and even perform preventative maintenance functions if required. A four-digit high luminosity 7-segment display on the meter shows flow rate, temperature or percentages of what’s expected. It also has a large indicator that changes colour to alert users of potential flow and temperature problems. As the meter often needs to be mounted into a small or awkward space, it can be rotated up to 330° on its mounting. The display also has an “upsidedown” mode for when the meter needs to be mounted this way.

3-Finger electric gripper handles cylindrical objects

OnRobot’s 3FG15 three-finger gripper with a 150mm stroke is a powerful alternative to bulkier and less-flexible three-finger grippers currently on the market – a unique solution. The new gripper excels at heavy-payload machine-tending applications that require high precision and flexible handling. The 3FG15 makes previously hard-to-automate precision handling of cylindrical parts easy to program and deploy and provides flexibility for a wide range of part sizes. It was developed as a response to existing pneumatic three-finger grippers that are bulkier and less flexible. OnRobot has long defined the market for electric parallel grippers with the RG2 and RG6 series, and this three-finger gripper allows users to deploy applications faster with highly accurate, fixed positioning. The 3FG15 gripper has a maximum stroke of 150mm that can easily handle multiple processes. The innovative three-finger design with a 15 kg (33 lb) payload provides a strong, stable grip for both form fit (internal) or friction fit (external) gripping, adding flexibility to any implementation. Ideal for CNC machine tending The gripper’s design, specifically developed for machine-tending tasks, automatically centres workpieces, resulting in a strong, stable grip and precise placement in machine chucks. With a gripping force from 10 N to 240 N, the 3FG15 competes with much less flexible finger grippers. The gripper is also ideal for packaging and palletising applications, and is seamlessly compatible with any major collaborative or light industrial robot arm through OnRobot’s new award-winning One System Solution, the platform that provides a unified mechanical and electrical interface between the robot arms and any OnRobot end-of-arm tooling (EoAT). 3FG15 Features: Precise stable grip with automatic centring Large 150mm stroke for parts from 20mm to 150mm Form fit (internal) and friction fit (external) gripping 15kg payload Weight 1.15kg, gripping force 10-240 N Fast, flexible deployment The EoAT market in Asia Pacific, excluding […]

Ingenious: The Unintended Consequences of Human Innovation

By Sir Peter Gluckman and Mark Hanson As humans evolved, we developed technologies to modify our environment, yet these innovations are increasingly affecting our behaviour, biology, and society. Now we must figure out how to function in the world we’ve created. Over thousands of years, humans have invented ingenious ways to gain mastery over our environment. The ability to communicate, accumulate knowledge collectively, and build on previous innovations has enabled us to change nature. Innovation has allowed us to thrive. The trouble with innovation is that we can seldom go back and undo it. We invent, embrace, and exploit new technologies to modify our environment. Then we modify those technologies to cope with the resulting impacts. Gluckman and Hanson explore what happens when we innovate in a way that leads nature to bite back. To provide nourishment for a growing population, humans developed methods to process and preserve food; but easy access to these energy-dense foods results in obesity. To protect ourselves from dangerous pathogens we embraced cleanliness and invented antibiotics, which has led to rising rates of autoimmune diseases and antibiotic-resistant bacteria. More recently, our growing dependence on the internet and social media has been linked to mental health concerns and declining social cohesion. And we are only at the beginning of the digital transformation that will influence every part of our existence. Our ingenuity has not only changed our world–it has changed us. Focusing on immediate benefits, we rarely pause to consider the longer-term costs of innovation. Yet we are now starting to see how our choices affect the way our brains develop and our bodies function. The implications are profound. Ingenious opens our eyes to the dangers we face and offers solutions we cannot ignore.

AI can’t solve everything

The hysteria about the future of artificial intelligence (AI) is everywhere. There seems to be no shortage of sensationalist news about how AI could cure diseases, accelerate human innovation and improve human creativity. Just looking at the media headlines, you might think that we are already living in a future where AI has infiltrated every aspect of society. While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as “AI solutionism”. This is the philosophy that, given enough data, machine learning algorithms can solve all of humanity’s problems. But there’s a big problem with this idea. Instead of supporting AI progress, it jeopardises the value of machine intelligence by disregarding important AI safety principles and setting unrealistic expectations about what AI can really do for humanity. AI solutionism In only a few years, AI solutionism has made its way from the technology evangelists’ mouths in Silicon Valley to the minds of government officials and policymakers around the world. The pendulum has swung from the dystopian notion that AI will destroy humanity to the utopian belief that our algorithmic saviour is here. We are now seeing governments pledge support to national AI initiatives and compete in a technological and rhetorical arms race to dominate the burgeoning machine learning sector. For example, the UK government has vowed to invest £300m in AI research to position itself as a leader in the field. Enamoured with the transformative potential of AI, the French president Emmanuel Macron committed to turn France into a global AI hub. Meanwhile, the Chinese government is increasing its AI prowess with a national plan to create a Chinese AI industry worth US$150 billion by 2030. AI solutionism is on the rise and it is here to stay. Neural networks – easier said than done While many political manifestos […]

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