It’s a timeless manufacturing goal: producing more, higher-quality products at minimum cost. The “Smart Manufacturing” revolution is already enabling manufacturers to reach this goal more successfully than ever. And one of the main core technologies driving this new wave of innovation is Artificial Intelligence.

Data has become a highly valuable resource, and it’s cheaper than ever to capture and store. Today, more manufacturers than ever are leveraging that data to significantly improve their bottom line thanks to Artificial Intelligence – and particularly Machine Learning.

For many, this means greatly improving production efficiency and capacity, by eliminating the primary causes of production losses and other associated costs.

Of course, getting tangible business value out of Artificial Intelligence is often easier said than done. AI is a complex technology, with many different applications. How can manufacturers see through the “hype” and empty promises, to invest in Industrial AI that will truly give them a competitive edge?

The Groundbreaking Benefits of AI and Machine Learning for Manufacturing

The introduction of AI and Machine Learning to industry represents a sea change with many benefits that can result in advantages well beyond efficiency improvements, opening doors to new business opportunities.

Some of the direct benefits of Machine Learning in manufacturing include:

• Reducing common, painful process-driven losses such as yield, waste, quality and throughput
• Increased capacity by optimizing the production process
• Enabling growth and expansion of product lines at scale due to a more optimized process
• Cost reduction through Predictive Maintenance. PdM leads to less maintenance activity, which means lower labor costs and reduced inventory and materials wastage.
• Predicting Remaining Useful Life (RUL). Knowing more about the behavior of machines and equipment leads to creating conditions that improve performance while maintaining machine health. Predicting RUL does away with “unpleasant surprises” that cause unplanned downtime.
• Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow.
• Improved Quality Control with actionable insights to constantly raise product quality.
• Improved Human-Robot collaboration improving employee safety conditions and boosting overall efficiency.
• Consumer-focused manufacturing – being able to respond quickly to changes in the market demand.

Watch The Webinar ‘Building Less Wasteful, More Sustainable Production‘.

The Key To Success With AI And Machine Learning For Manufacturing

Certainly, it’s impossible to miss the rapid rise of AI technology – both in general and specifically in the context of manufacturing. It seems like every other technology-related article (and practically every single vendor vying for our attention) uses terms like AI, machine learning, digitization, automation, Industry 4.0 and Industrial Artificial Intelligence, almost as a form of punctuation.

As a result, the expectations when it comes to AI are often wildly off-base, varying from an all-encompassing solution to all your business problems to a deep skepticism any time “AI” is even uttered.

Find The Right Use Case

But, as with any technology, the truth is really somewhere in between. AI can be extremely effective – in the right context. Understanding those contexts, and the kinds of AI technology that apply to them, is the key to setting realistic business goals for AI adoption.

Artificial Intelligence is not a silver bullet. No solution will solve all, or most, of your problems. As a rule of thumb, AI works best when it is applied to solving a specific problem, or a very closely-related set of problems. “General AI” is something to be wary of: if a vendor claims to do everything, then they probably do nothing particularly well.

Which brings us back to the topic of AI in manufacturing. There are numerous potential applications for AI and Machine Learning in manufacturing, and each use case requires a unique type of Artificial Intelligence.

In the guide below, we present a simple, effective formula to select the right Industrial AI solution to address your specific manufacturing challenges and goals – focusing specifically on Machine Learning AI, since that is where the most exciting and impactful innovations are occurring.

The formula can be encapsulated in a simple diagram and methodology called “The Industrial AI Quadrant”.

Next-Generation Optimization For Manufacturers With Machine Learning

The two major use cases of Machine Learning in manufacturing are Predictive Quality & Yield, and Predictive Maintenance.

Only Do Maintenance When It’s Needed

Predictive Maintenance is the more commonly known of the two, given the significant costs maintenance issues and associated problems can incur, which is why it is now a fairly common goal amongst manufacturers.

Instead of performing maintenance according to a predetermined schedule, or using SCADA systems set up with human-coded thresholds, alert rules and configurations, predictive maintenance uses algorithms to predict the next failure of a component/machine/system. Personal can then be alerted to perform focused maintenance procedures to prevent the failure, but not too early so as to waste downtime unnecessarily.

By contrast, traditional manual and semi-manual approaches don’t take into account the more complex dynamic behavioral patterns of the machinery, or the contextual data relating to the manufacturing process at large. For example, a sensor on a production machine may pick up a sudden rise in temperature. A static rule-based system would not take into account the fact that the machine is undergoing sterilization, and would proceed to trigger a false-positive alert.

The advantages are numerous and can significantly reduce costs while eliminating the need for planned downtime in many cases.

By preempting a failure with a machine learning algorithm, systems can continue to function without unnecessary interruptions. When maintenance is needed, it’s very focused – technicians are informed of the components that need inspection, repair and replacement; which tools to use, and which methods to follow.

Predictive maintenance also leads to a longer Remaining Useful Life (RUL) of machinery and equipment since secondary damage is prevented while smaller labour forces are needed to perform maintenance procedures.

Find The Hidden Causes Of Losses

Predictive Quality and Yield – sometimes referred to as just “Predictive Quality” – is a more advanced use case of Industrial Artificial Intelligence, that reveals the hidden causes of many of the perennial process-based production losses manufacturers face on a daily basis. Examples include quality, yield, waste, throughput, energy efficiency, emissions, and more – essentially any loss caused by process inefficiencies.

Predictive Quality and Yield automatically identifies the root causes of process-driven production losses using continuous, multivariate analysis, powered by Machine Learning algorithms that are uniquely trained to intimately understand each individual production process.

Automated recommendations and alerts can then be generated to inform production teams and process engineers of an imminent problem, and seamlessly share important knowledge on how to prevent the losses before they happen.

Reducing these types of losses has always been a struggle for manufacturers of all stripes. But in today’s marketplace, this mission is more important than ever before.

On the one hand, consumers’ expectations are higher than ever before; global consumer habits are gradually “westernizing”, even as the population boom continues. According to numerous surveys, the global population will grow by 25% by 2050, equating to some 200,000 additional mouths to feed every day. On the other hand, consumers have never had so many alternatives available to them, in almost every product imaginable. Recent surveys indicate that this wealth of options means consumers are increasingly likely to permanently ditch even their favorite brands if, for example, a product isn’t available on the shelf.

Against such a backdrop, manufacturers can no longer afford to take process inefficiencies, and their associated losses, in their stride. Every loss in terms of waste, yield, quality or throughput chips away at their bottom line and hands another inch to the competition.

The challenge for many manufacturers is that they eventually hit a glass ceiling in terms of process optimization. Some inefficiencies don’t have any obvious cause, and process experts are left at a loss to explain them. That’s where Machine Learning – and particularly Automated Root Cause Analysis – can really save the day.

Two Main Categories Of Machine Learning – And How They Relate To Manufacturing

Machine Learning can be split into two main techniques: Supervised and Unsupervised.

1) Supervised Machine Learning

In manufacturing use cases, supervised machine learning is the most commonly used technique since it leads to a predefined target: we have the input data; we have the output data; and we’re looking to map the function that connects the two variables.

Supervised machine learning demands a high level of involvement – data input, data training, defining and choosing algorithms, data visualizations, and so on. The goal is to construct a mapping function with a level of accuracy that allows us to predict outputs when new input data is entered into the system.

Initially, the algorithm is fed from a training dataset, and by working through iterations, continues to improve its performance as it aims to reach the defined output. The learning process is completed when the algorithm reaches an acceptable level of accuracy.

In manufacturing, there are two most common Supervised Learning approaches:

Regression And Classification

These two approaches share the same goal: to map a relationship between the input data (from the manufacturing process) and the output data (known possible results such as quality or waste losses, part failure, overheating etc.)

Regression

Regression is used when data exists within a range (eg. temperature, weight), which is often the case when dealing with data collected from sensors.

In manufacturing, regression can be used to calculate an estimate for the Remaining Useful Life (RUL) of an asset. This is a prediction of how many days or cycles we have before the next component/machine/system failure.

For regression, the most commonly used machine learning algorithm is Linear Regression, being fairly quick and simple to implement, with output that is easy to interpret. An example of linear regression would be a system that predicts temperature, since temperature is a continuous value with an estimate that would be simple to train.

Classification

When data exists in well-defined categories, Classification can be used. An example of Classification that we’re all familiar with is the email filter algorithm that decides whether an email should be sent to our spam folder, or not. Classification is limited to a boolean value response, but can be very useful since only a small amount of data is needed to achieve a high level of accuracy.

In machine learning, common Classification algorithms include naive Bayes, logistic regression, support vector machines and Artificial Neural Networks.

2) Unsupervised Machine Learning

With Supervised machine learning we start off by working from an expected outcome and train the algorithm accordingly. Unsupervised learning is suitable for cases where the outcome is not yet known.

Clustering

In some cases, not only will the outcome be unknown to us, but information describing the data will also be lacking (data labels). By creating clusters of input data points that share certain attributes, a Machine Learning algorithm can discover underlying patterns.

Clustering can also be used to reduce noise (irrelevant parameters within the data) when dealing with extremely large numbers of variables.

Artificial Neural Networks

In the manufacturing sector, Artificial Neural Networks are proving to be an extremely effective Unsupervised learning tool for a variety of applications including production process simulation and Predictive Quality Analytics.

The basic structure of the Artificial Neural Network is loosely based upon how the human brain processes information using its network of around 100 billion neurons, allowing for extremely complex and versatile problem solving.

This ability to process a large number of parameters through multiple layers makes Artificial Neural Networks very suitable for the variable-rich and constantly changing processes common to manufacturing. Moreover, once properly trained, an Artificial Neural Network can demonstrate a high level of accuracy when creating predictions regarding the mechanical properties of processed products, enabling cuts in the cost of raw materials.

Data Preparation: Garbage In, Garbage Out

Machine learning is all about data, so understanding some key elements about the quality and type of data needed is extremely important in ensuring accurate results.

With Predictive Quality and Yield, for example, we’re focused on process inefficiencies. Therefore, it makes sense to start by collecting historical data about the performance of the line or lines in question, as well as the losses incurred over time, in order to form predictions about future potential losses.

To get the fullest, most accurate picture possible, that data should be gathered from as many sources as possible, since manufacturing processes – especially more complex ones – are affected by a very wide range of factors that are often interdependent. This can include everything from process data, quality data, raw materials, and even external factors like weather and temperature.

Next, and just as importantly, we need to decide what question we want the Machine Learning model to answer – and whether it is possible to answer this question using the data that’s available.

After all, to get the most out of an Industrial Artificial Intelligence/Machine Learning solution, manufacturers need to know which AI solution is best suited for their own unique sets of challenges.

Read The White Paper ‘The Power of Augury’s AI’. Or to learn more about how our AI is making the jobs of manufacturers easier, get in touch today.

The post Artificial Intelligence And Machine Learning In Manufacturing: A Quick Guide To The Fundamentals appeared first on Augury.

With production optimization as the key route to increasing revenue and reducing costs, chemical manufacturers are increasingly turning to AI-driven solutions to keep assets running and streamline process.

One of the main ongoing concerns for manufacturers is how to enhance productivity. This includes analyzing, measuring, modeling and implementing specific actions to optimize their production lines.

The reason production optimization plays a huge role in manufacturing, is because it is a core means to increase revenue and reduce costs. Process failures in production cause companies to suffer from losses in quality and yield – which translate directly into revenue loss.

Let’s take a look at the chemical manufacturing industry, as the birth of the heavy chemical industry coincides with the beginning of the Industrial Revolution. The chemical industry comprises about 15% of the US manufacturing sector, manufactures more than 70,000 different products, and is responsible for 90% of our everyday products.

Challenges Chemical Manufacturers Face

Just as broad as the chemical manufacturing industry is, so are the process optimization challenges it faces.

In order for chemical manufacturers to optimize production lines, they need to address different process inefficiencies, such as the formation of undesired side products, process instabilities, losses due to impurities and more, on an ongoing basis.

Given the complexities of chemical manufacturing, it’s extremely time-consuming and difficult to understand the root causes for these process inefficiencies, let alone anticipate when they are going to happen. Often times, it is the specific behavior of the combination of multiple production parameters, or tags, that cause the inefficiency to happen.

A growing number of chemical manufacturers are turning to Industrial Artificial Intelligence solutions to identify and anticipate process inefficiencies leveraging methods of supervised and unsupervised machine learning.

Proven Success Story

According to recent research by Accenture, companies that have implemented Industrial AI in the chemical sector are seeing big benefits—a whopping 72 percent report a minimum 2x improvement in some process KPIs, and 37 percent a 5x improvement. For example, a manufacturer of Ethylene Dichloride implemented process-based Industrial AI to solve a number of process inefficiencies, and by doing so increased yield by €1.7M in less than 12 months.

With the capabilities Industrial AI has to offer, chemical manufacturers can utilize their data, improve their processes, and continually adapt them.

Revolutionizing the Chemical Manufacturing Industry

Chemical manufacturers need to identify and avoid process inefficiencies to improve chemical process control.

A production disturbance is any unintentional event in the chemical production process that leads to process inefficiencies, unplanned stoppages, rework, or scrap

By implementing Industrial AI solutions to chemical production lines, manufacturers have the ability to leverage different AI technologies that are critical to identifying production disturbances and optimizing production:

• Real-time data connectivity and capture – manufacturers use industrial IoT connectivity to securely connect to the production line assets and capture data in a central time-series repository
• Process-based machine learning – manufacturers use process-based AI to get visibility into the full manufacturing process in detail, and holistically, and to discover and surface process issues that need attending.
• Digital Twin visualization – manufacturers use a digital twin, which is a virtual representation that matches the attributes and operational metrics of a “physical” production line through the captured production-line data. This enables production teams to quickly pinpoint performance anomalies and their root cause, providing them with actionable insights, and presenting them in the context of the production line. This eliminates the need for data scientists.

How to Use AI for Predictive & Prescriptive Process Optimization

Let’s dive a bit deeper into how specific Industrial AI technologies can be used to identify, anticipate, and prevent chemical process inefficiencies:

• Implement Digital Twin Visualization

The first step manufacturers should take to identify specific process inefficiencies is implementing digital twin visualization. This allows them to easily track their main KPIs and receive actionable insights into process anomalies.

• Perform Automated Root Cause Analysis

Automated Root Cause Analysis can then be performed to gain fast and accurate insights into process inefficiencies. This approach enriches historical and real-time asset data and applies machine learning algorithms to automatically trace the causal chain of events leading to production failures.

• Translate Data into Insights with Industrial Predictive Analytics

Once process inefficiencies have been identified using the analyzed data, it’s important to translate this data into actionable insights. Industrial Predictive Analytics can achieve this.

Machine learning algorithms can be implemented to identify relevant events and predict their outcomes.

By having the ability to prevent specific inefficiencies and production disturbances, process teams can increase production yield while preventing failures at the same time.

Bottom Line: Save Time And Money

By using process-based machine learning, manufacturers get focused and contextual predictive alerts. This is a huge opportunity for chemical manufacturers, since operational technology (OT) data is already well organized and captured within data historians.

Leveraging this data with process-based AI means being able to pinpoint the root cause of process disturbances with extreme accuracy, and predict process instabilities and failures before they have the chance to affect production.

So, with Industrial AI, chemical manufacturers can reduce quality and production losses, saving them great amounts of time and money.

Ready to get started with process optimization, driven by data and machine learning? Reach out.

The post The Role of Industrial AI Solutions in Chemical Manufacturing Digitization appeared first on Augury.

While still an essential metric for manufacturers, Overall Equipment Effectiveness (OEE) is limited in its ability to take in the ‘big picture’. Now, AI can be applied to also measuring Overall Line Efficiency (OLE) for a fuller view of the whole manufacturing process – backed by actionable insights.

Overall Equipment Effectiveness (OEE) is a widely accepted and utilized manufacturing evaluation method. However, Industry 4.0 is raising the standards of production, and OEE is limited in its ability to take into account more complex systems. 

To respond to this need for a better-suited metric, a technique known as Overall Line Efficiency (OLE) is being used. It is much better equipped to describe multiple production lines and the interaction of a number of various sub-processes within a larger production process. 

Ideally, a complete performance evaluation approach will incorporate both OEE and OLE methods with appropriate modifications to suit the operation.

Efficiency Vs. Effectiveness

To differentiate between OEE and OLE, it helps to start by clarifying the difference between effectiveness (in OEE) and efficiency (in OLE), two terms often misused in the context of manufacturing.

The simple diagram above demonstrates that effectiveness focuses on performing the right tasks and aiming for the right goals, while efficiency is about performing tasks in an optimal way.

Overall Equipment Effectiveness (OEE)

Overall Equipment Effectiveness is a fundamental KPI that is used to improve manufacturing processes by using benchmarking and analysis to pinpoint inefficiencies and to categorize them. 

OEE seeks to describe the overall utilization of materials, equipment, and time in a production process. It is calculated according to the below equation, although there are a number of ways of defining the 3 contributing parameters…

OEE = Availability X Performance X Quality

For production lines consisting of a number of unbalanced (unpaced) machines, OEE is not ideal since it’s better suited to evaluate individual assets. 

Overall Line Efficiency (OLE)

Overall Line Efficiency is a relatively new metric in manufacturing and builds off of OEE to compare the current performance of a production line with how well it could be performing. 

OLE also takes into account the personnel involved in the various processes. In this way, OLE seeks to optimize the synchronization between the output rates of machines and the use of human resources.  

OLE   =   OEE of Machine A + OEE of Machine B + OEE of Machine C
3

 The above calculation assumes that the importance of Machine A, B, and C are the same – i.e. have the same “weight”. In most manufacturing scenarios this will not be the case, with different processing stages having different weights, resulting in a more complex OLE calculation.

Overall Line Efficiency can be expanded further to include a calculation for each production line (taking into account the bottleneck for each), and to formulate a calculation that incorporates a number of production lines.

New Methods for Calculating OEE and OLE

The use of artificial intelligence is steadily growing within the manufacturing sector and can be applied to both OEE and OLE calculation. AI’s advantage here is its ability to adapt to different manufacturing scenarios thanks to the algorithms’ flexibility.

In other words, an AI algorithm used to calculate OLE isn’t affected by whether the operation is in the aerospace or food processing sector. Any meaningful differences between those sectors can be reflected in the algorithm by setting specific weight values for critical parameters.

Using Artificial Neural Networks For Overall Line Efficiency

Artificial Neural Networks (ANNs) can easily handle the complexity of OLE calculations, and lead to far more accurate results than those achievable through more traditional calculation methods. 

Implementing ANNs to calculate Overall Line Efficiency is not an immediate process – the algorithm needs to be trained. This is done by feeding the ANN existing historical data categorized as input (OEE) or output (OLE) along with other relevant data from the machines and production floor. ANNs can also be fed data from observations made by operators, and thereby enhancing the training through additional information layers.  

Ideally, a complete performance evaluation approach will incorporate both OEE and OLE methods with appropriate modifications to suit the operation.

Better Manufacturing Management with Overall Line Efficiency

Using a combination of OEE and OLE calculations to monitor the performance of a manufacturing operation can be extremely useful for management. Due to the high number of variables involved, artificial intelligence in the form of Artificial Neural Networks and other techniques, is very well suited to this field and can offer actionable insights for better management decisions and greater impact.

Also read: ‘How to Improve OEE in Industry 4.0‘. Or reach out direct to find out more about how you can apply OLE for your own particular production lines. 

The post Why Overall Line Efficiency Is A Necessary Metric For Industry 4.0 appeared first on Augury.

There’s an abundance of people in the world, and that requires an abundance of resources. Approximately 8 billion people need to eat, drink, wear clothing, and live in homes. And more people are interested in cars, computers, toys, and other goods that require production and manufacturing. This demand will only grow with time, and unfortunately, that means more pollution as well.

This article first appeared in Global Trade.

Carbon dioxide emissions released by fossil fuel combustion and industrial processes rose by roughly 35% globally between 2000 and 2020. As climate change’s impact continues to unfold, governments, communities, and concerned citizens worldwide will likely expect meaningful change from businesses in every sector, especially manufacturing.

Sustainable manufacturing means creating goods in a way that minimizes the environmental impact, including the use of energy and natural resources. If we don’t take actionable steps to create sustainable manufacturing companies, the problem will only worsen. Thankfully, the technology of today is ready to combat tomorrow’s increased consumption. Through predictive maintenance tools and prioritizing both machine health and process health, manufacturers can journey forward to sustainability in manufacturing.

Obstacles to Sustainable Manufacturing

Much of the problem for manufacturing leadership lies in thin profit margins. A recent study found the average manufacturer loses 12-15% on energy consumption due to inefficient machinery.

This waste compounds the obvious pollution problem. When calculating how much CO2 is produced to manufacture beverages, for example, we need to account for not just the completed production, but also the defective products. The most sustainable option would be to close up shop — but that’s obviously not an option. People rely on manufactured goods, which means manufacturers need to fight the uphill battle to create more sustainable manufacturing.

You must invest in multiple areas — equipment, knowledge, and training, among others — to improve sustainability. That leaves manufacturers stuck between a rock and a hard place. On the one hand, it’s expensive to upgrade to sustainable manufacturing, but on the other, manufacturers can’t remain ethical and competitive otherwise.

The reality is that we need this industry. That’s why agencies like the EPA are focused on helping manufacturers do their part in rebuilding a strong, sustainable infrastructure post-pandemic. But how are sustainable manufacturing companies staying profitable?

Achieving Sustainability in Manufacturing While Making a Profit

Manufacturing involves a lot of complicated machinery that needs to be perfectly calibrated to perform simple commands in a production line. This allows them to achieve complex, dynamic things at a scale limited only by human effort. Of course, this is where artificial intelligence is really changing the game — evolving data insights past human limitations.

The right AI, when built specifically for manufacturing, can provide both a bird’s-eye view of the production lines and a deep dive into its inner workings. Done properly, this AI can act as a decision-making tool that looks at all your processes and constantly learn how to improve them. By applying purpose-built AI to production lines, manufacturers gain access to thousands of complex calculations every minute.

And with the right algorithms to help digest it all, manufacturers gain powerful insights into important operational questions: When are machines most likely to fail? Which parts are most critical to the machine? What’s the optimal way to run the production line? Having these answers makes the entire production line more efficient while also improving production health and safety. By adopting AI solutions for manufacturing, you gain a cheat code to resolve all the problems that are too complex for manufacturing teams to handle, no matter how much experience is under their belt.

Not only does AI aid in design for sustainable manufacturing, but it can proactively monitor machines to enable predictive maintenance. Machine health can be continuously monitored through a series of sensors that provide real-time insights into how each cog in the machine is performing, and process health allows you to optimize quality, yield, waste, and every other manufacturing metric while minimizing downtime. As factories optimize, the amount of waste (and pollution) will naturally decrease.

With AI in place, production health becomes achievable. As machines run better, profit margins grow, allowing for scalable investment in sustainability. And there are three steps that can make it happen now.

1) Evolve Past Traditional Mindsets

Imagine you’re given an impossible task, such as being both sustainable and profitable without using AI. Often, the solution is to ignore sustainability, as it’s a long-term issue with delayed consequences. Over time, the problem never goes away; it only continues to become an obstacle for the business.

Burying our heads in the sand isn’t running a business; it’s an avoidance tactic. Our mindset must evolve alongside technology. AI-enabled machine health and process health always find the perfect balance between sustainability and profitability. While this scale is a moving target, these AI insights mean there’s no need to compromise.

2) Create Change Management Processes

Once manufacturing executives open their eyes to what’s possible, they need to plan a roadmap for the next 20 years. The next generation of your team will work in completely different ways; change is the only guarantee in business. This means every manufacturer needs a solid, well-documented change management process.

AI insights work faster than we can, which means changes can come at an incredibly fast pace. This can be overwhelming for a team, so make sure everyone feels prepared. A solid change management process will help the team adapt more quickly, meaning less time is spent getting the team on board and more time focusing on real innovations.

3) Leverage Existing Tools

Production changes are inevitable, and AI can also help in this transition. A solid platform can track assets to optimize resources. It’s not uncommon for things to get overlooked as a company expands, and some companies may not be aware of all the tools they have available, leading to costly repeats.

Sustainability in manufacturing isn’t a new concept, and the right software solutions can optimize any equipment still in use. We often talk about AI and machine learning as the future, but the reality is that it’s already on the market and in use. Not only that, but it’s much more sophisticated than it was a decade ago.

This means the transition to sustainable manufacturing is best addressed sooner rather than later. European regulations are already tightening, and it’s only a matter of time before the United States follows suit. In fact, regulations throughout the world are quickly changing in the pandemic’s wake, and CO2 emissions are always at the top of legislative lists.

Starting (or continuing) the transition to sustainability in manufacturing may be the most important thing you do today.

To learn more about how Augury is meeting today’s challenges, reach out! Meanwhile, you can also read more about sustainability and ESG here.

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