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Overview
Flexible Manufacturing
Safer Operations
Cobot Sensing
Productive Insights
Quality Assurance
Enterprise Connectivity

Industry IoT

Predictive Insights from the Edge
The path to a smarter, more adaptive, more efficient, and safer manufacturing environment.
The Path to Ubiquitous Connectivity

The digital future of healthcare revolves around predictive, proactive care focused on keeping people well based on their unique needs and lifestyles.

Industry IoT

Industry IoT
Predictive Insights from the Edge

The path to a smarter, more adaptive, more efficient, and safer manufacturing environment.

Predictive Insights from the Edge
The path to a smarter, more adaptive, more efficient, and safer manufacturing environment.
Take a Closer Look at How Real-Time Connectivity Is Transforming Manufacturing
Take a Closer Look at How Real-Time Connectivity Is Transforming Manufacturing
Including Edge-to-Cloud Computing, Analytics and Measurement
On the factory floor, employees work side by side with robots that perform a wide range of tasks, from installing hard-to-reach parts to properly aligning headlights on auto assembly lines.
Many manufacturers are also integrating smart sensing capabilities, automation and artificial intelligence (AI)/machine learning into their production processes to gain further insights that enable greater efficiencies, optimize asset utilization, and improve safety.
All these innovations are leading to a digital transformation in industrial markets that helps manufacturers meet their safety, environmental, and productivity goals.
One of the keys to unlocking the full value of connected systems in manufacturing environments is an AI-driven sensing and interpretation platform that acquires, learns, and interprets unidimensional signals such as sound, vibration, and temperature at the edge in real time.
Take a Closer Look at How Real-Time Connectivity Is Transforming Manufacturing
Including: Edge-to-Cloud Computing, Analytics and Measurement
On the factory floor, employees work side by side with robots that perform a wide range of tasks, from installing hard-to-reach parts to properly aligning headlights on auto assembly lines. Many manufacturers are also integrating smart sensing capabilities, automation and artificial intelligence (AI)/machine learning into their production processes to gain further insights that enable greater efficiencies, optimize asset utilization, and improve safety. All these innovations are leading to a digital transformation in industrial markets that helps manufacturers meet their safety, environmental, and productivity goals.

One of the keys to unlocking the full value of connected systems in manufacturing environments is an AI-driven sensing and interpretation platform that acquires, learns, and interprets unidimensional signals such as sound, vibration, and temperature at the edge in real time.

Here’s a closer look at how real-time connectivity — including edge-to-cloud computing, analytics, and measurement — is transforming manufacturing.

Take a Closer Look at How Real-Time Connectivity Is Transforming Manufacturing
Including: Edge-to-Cloud Computing, Analytics and Measurement
On the factory floor, employees work side by side with robots that perform a wide range of tasks, from installing hard-to-reach parts to properly aligning headlights on auto assembly lines.
Many manufacturers are also integrating smart sensing capabilities, automation and artificial intelligence (AI)/machine learning into their production processes to gain further insights that enable greater efficiencies, optimize asset utilization, and improve safety.
All these innovations are leading to a digital transformation in industrial markets that helps manufacturers meet their safety, environmental, and productivity goals.
One of the keys to unlocking the full value of connected systems in manufacturing environments is an AI-driven sensing and interpretation platform that acquires, learns, and interprets unidimensional signals such as sound, vibration, and temperature at the edge in real time.
Flexible Manufacturing
Manufacturers need to move quickly in today’s market. Driven by e-commerce and digital technologies, such as 3D printing, demand for more customized products with shorter lead times is becoming the norm. In the healthcare industry, for instance, patients and consumers are seeking more personalized wellness products, such as wearables and disease-specific monitoring devices. This means manufacturers must contend with smaller batch sizes and reconfigurable processes, while minimizing the waste, defects, and inefficiencies, such as increased energy usage, that often result from these new business models.
Solutions

Collaborative Robots

Many manufacturers are addressing these issues by adding collaborative robots (cobots) to their facilities. Cobots don’t replace workers; they work alongside them to perform many time-consuming, repetitive tasks. In recent years, robot manufacturers have been designing smaller cobots that are easier to program, so users can teach them to perform a wider variety of tasks, enabling greater flexibility on the plant floor to handle more applications. Because of this flexibility, cobots are starting to emerge from factory floors and expand into everyday consumer life, from hospitals to grocery stores to hotels.

Integration of Sensing

Another key to enabling flexible manufacturing is the integration of sensing, connectivity, and AI software capabilities in machines. For example, machines typically have their own proprietary software control, which requires user configuration to synchronize the machine with the system. New digital platforms provide centralized control with a single environment for motion, vision, safety and robots, which allow a system or machine to connect using open networks such as Ethernet IP. These systems often include sensors that can detect anomalies based on sound, temperature, current and/or vibration interpretation for every unit produced.

Software Configurable Systems

In addition, software-configurable systems are enabling industrial OEMs to deliver unprecedented levels of flexibility to the factory floor while simultaneously reducing their own product complexity. New software configurable input/output (SWIO) capabilities allow any industrial I/O function to be accessed on any pin, allowing channels to be configured at any time. This means customization can happen right at the time of installation, resulting in faster time to market, fewer design resources, and universal products that can be implemented broadly across projects and customers.
Safer & More Productive Operations
Safer & More Productive Operations
Operational efficiency is one of the more common reasons manufacturers adopt connected technologies. But industrial IoT has wide-ranging benefits both inside the factory floor and beyond. For instance, Swiss food processing company Bühler uses industrial IoT and edge computing to help prevent toxins from getting into the food supply. A collaboration between Microsoft and Bühler led to the creation of a grain-sorting machine that uses cloud-based data analysis and UV light sensors to detect and remove grains with a carcinogenic fungus so they don’t enter the food chain.

Another part of industrial safety is smoke/gas detection. Edge sensing allows for detecting dangerous smoke versus a false alarm-inducing nuisance. For toxic gases and chemicals, intelligent sensing can also alert workers to dangerous levels of certain gases.


In fact, safety is another primary reason manufacturers adopt industrial IoT technologies, according to a survey of 3,000 professionals by Hypothesis Group and Microsoft. Some other key drivers for adoption include quality, productivity, supply chain management, asset tracking, and condition-based maintenance.

Reasons for IoT Adoption

Safety Is a Primary Reason Manufacturers Adopt IoT Technologies

Operations Optimizations

Operations Optimizations

Employee Productivity

Employee Productivity

Safety & Security

Safety & Security

Supply Chain Management

Supply Chain Management

Quality Assurance

Quality Assurance

Asset Tracking

Asset Tracking

Sales Enablement

Sales Enablement

Energy Management

Energy Enablement

Conditioned-Based Maintenance

Condition-Based Maintenance

Health & Wellness

Health & Wellness

Conditioned-Based Maintenance

Health & Wellness

Condition-Based Maintenance

Source: Hypothesis Group/Microsoft
Inside the plant, industrial IoT technologies are enabling safer conditions for employees. Cobots automate tasks which were previously only possible with humans, freeing up workers to focus on the creative and cognitive work only humans can do. Because robots have great power and ability, and they work so closely with humans, it’s imperative that they maintain control and avoid collisions with workers or equipment.
Cobots may also include integrated safety features that enable a safer work environment because they can perform repetitive tasks that often carry an increased risk for ergonomic injuries. A 58-year-old worker at Kay Manufacturing’s Calumet City, Illinois, plant told the Chicago Tribune that a cobot trained to perform manual inspections of auto parts using optical sensors has freed him from manual tasks that aggravated his arthritis.
At another one of Kay’s plants, the company is saving $150,000 annually by shifting packing duties away from the machine operators. Now, workers can focus on more value-added activities and earn more money as productivity increases.
Cobots also can perform some heavy lifting that may cause back strain or injury among workers. In addition, they’ve become a key tool for keeping workers safe during the COVID-19 pandemic, allowing for more social distancing and broader safety precautions on the plant floor.

Inside the plant, industrial IoT technologies are enabling safer conditions for employees. Cobots automate tasks which were previously only possible with humans, freeing up workers to focus on the creative and cognitive work only humans can do. Because robots have great power and ability, and they work so closely with humans, it’s imperative that they maintain control and avoid collisions with workers or equipment.

Cobots may also include integrated safety features that enable a safer work environment because they can perform repetitive tasks that often carry an increased risk for ergonomic injuries. A 58-year-old worker at Kay Manufacturing’s Calumet City, Illinois, plant told the Chicago Tribune that a cobot trained to perform manual inspections of auto parts using optical sensors has freed him from manual tasks that aggravated his arthritis.

At another one of Kay’s plants, the company is saving $150,000 annually by shifting packing duties away from the machine operators. Now, workers can focus on more value-added activities and earn more money as productivity increases.


Cobots also can perform some heavy lifting that may cause back strain or injury among workers. In addition, they’ve become a key tool for keeping workers safe during the COVID-19 pandemic, allowing for more social distancing and broader safety precautions on the plant floor.
Cobot Sensing Capabilities
To work safely and efficiently, cobots require various types of integrated sensing technologies to detect and prevent possible collisions. This includes:
Temperature and Humidity
Measuring temperature and humidity of components, including the AI motherboards and components.
Vision
  • Time of flight sensors to measure distance
  • LIDAR for navigational sensing
Vibration
MEMS sensors and AI sensing interpretation for condition monitoring and predictive maintenance.
Ultrasonic and Radio Waves
To operate in dark conditions and measure the velocity of objects.
Magnetic Angle Sensors
Enable more robust, affordable medium resolution encoder solutions suited to human-assist tasks.
One of the issues manufacturers may encounter with cobots is interoperability. Many control systems and plant floor machinery can’t communicate with each other. However, IoT technology providers have introduced solutions that address data translation and deliverability issues.
For instance, Analog Devices (ADI) has made several advancements in robotic sensing capabilities that combine necessary connectivity, control, and data collection functions. This includes the servo drive for the core motor and motion control, SmartMesh™ wireless networks, machine health monitoring and battery management as well as multiprotocol flexibility.
Productive Insights Cut Downtime
Productive Insights Cut Downtime
Toyota Motor Corp.’s largest supplier, Denso, discovered that a fan in one of its brazing ovens would fail in exactly 58 hours if the company didn’t take action to replace it. The story, related by lean manufacturing guru Jeffrey Liker in the revised edition of “The Toyota Way,” demonstrates how connected systems are transforming plants from reactive to predictive environments. As Liker explains:
Toyota Motor Corp.’s largest supplier, Denso, discovered that a fan in one of its brazing ovens would fail in exactly 58 hours if the company didn’t take action to replace it. The story, related by lean manufacturing guru Jeffrey Liker in the revised edition of “The Toyota Way,” demonstrates how connected systems are transforming plants from reactive to predictive environments. As Liker explains:
“The ovens, which make aluminum heat exchangers, require 12 expensive fans ‘the size of a table.’ If a fan stops, the Denso team faces a time-consuming task of cooling down the oven, replacing the fan and bringing it back into service — each task taking 12 hours to complete.”
— Jeffrey Liker, The Toyota Way

Most manufacturers can’t wait 12 hours...

Most manufacturers can’t wait 12 hours...
When it’s an emergency, those 12 hours of maintenance have to happen immediately, cutting into regularly scheduled production time. In addition to increasing downtime—a big problem for manufacturers as it affects customer satisfaction and overall costs—most manufacturers can’t wait 12 hours. The impact to productivity and customer satisfaction can lead to significant losses.
Denso responded by implementing tiny sensors on each fan. The sensors feed information to a computer that displays the condition of the fans and notifies maintenance about any degradation long before a shutdown. Managers at Denso don’t view IoT as an opportunity to replace people. Rather, the company views it as tool to help workers solve problems. Leaders at Denso refer to this as “collaborative creation and growth of human, things and equipment,” according to Liker.
Industrial IoT is about increasing visibility in all aspects of plant operations, so workers perform their jobs safely and more efficiently with minimal disruptions. According to a Deloitte study, predictive maintenance offers potential savings of:
20% to 50% ►
In Maintenance Planning Time
10% to 20% ►
In Increased Equipment Uptime & Availability
5% to 10% ►
In Operations & MRO Material Spend
5% to 10% ►
In Overall Maintenance Costs
Industrial IoT is about increasing visibility in all aspects of plant operations, so workers perform their jobs safely and more efficiently with minimal disruptions. According to a Deloitte study, predictive maintenance offers potential savings of:
Another example is the use of AI-driven condition monitoring to reduce machine testing time. Analog Devices’ sensing interpretation platform, ADI OtoSense™, can connect to a PLC to collect critical information needed to test pump performance, including speed and pressure. ADI OtoSense automatically triggers the anomaly detection models corresponding to each pump. In addition to reduced electricity use, this process can reduce testing time by at least 25%, and potentially as much as 50%.
Real-Time Quality Assurance
A medical device manufacturer in Puerto Rico applies machine learning to detect any defects that could cause a product to fail before it leaves the plant floor. The implications are significant when you consider what a single failure could mean for patients who depend on that equipment for their health. The machine learning program conducts predictive analytics using both historical and current data to “identify discrepancies, variances and the smallest combination of weakness,” according to Medical Device and Diagnostic Industry magazine.
Quality testing is traditionally a manual process that involves the eye of a skilled operator to recognize product defects or abnormalities. These skills often require significant training, which creates additional challenges with a lack of qualified operators on the market. But many of the sensing capabilities that lead to improved equipment maintenance and reliability also help to increase product quality. AI algorithms notify operations teams about production faults that may cause product quality issues.
This may include deviations from standard operating procedures, machine abnormalities, and changes in raw materials. AI-enabled quality assurance also allows manufacturers to collect field data about the use and performance of their products. AI-based quality assurance can increase productivity by up to 50% and improve defect detection by 90% compared with a human inspection.
A medical device manufacturer in Puerto Rico applies machine learning to detect any defects that could cause a product to fail before it leaves the plant floor. The implications are significant when you consider what a single failure could mean for patients who depend on that equipment for their health. The machine learning program conducts predictive analytics using both historical and current data to “identify discrepancies, variances and the smallest combination of weakness,” according to Medical Device and Diagnostic Industry magazine.
Quality testing is traditionally a manual process that involves the eye of a skilled operator to recognize product defects or abnormalities. These skills often require significant training, which creates additional challenges with a lack of qualified operators on the market. But many of the sensing capabilities that lead to improved equipment maintenance and reliability also help to increase product quality. AI algorithms notify operations teams about production faults that may cause product quality issues.
This may include deviations from standard operating procedures, machine abnormalities, and changes in raw materials. AI-enabled quality assurance also allows manufacturers to collect field data about the use and performance of their products. AI-based quality assurance can increase productivity by up to 50% and improve defect detection by 90% compared with a human inspection.
ADI OtoSense accomplishes this by sensing abnormalities at any given step within a manufacturing process. For example, a common problem in metal punching is “slug pulling.” This is when punched scrap sticks to the part, which may lead to costly parts defects and harm the machine tool at the next stage in the production line. Detecting and preventing this issue can make a material difference in line performance.
ADI OtoSense accomplishes this by sensing abnormalities at any given step within a manufacturing process. For example, a common problem in metal punching is “slug pulling.” This is when punched scrap sticks to the part, which may lead to costly parts defects and harm the machine tool at the next stage in the production line. Detecting and preventing this issue can make a material difference in line performance
This may include deviations from standard operating procedures, machine abnormalities, and changes in raw materials. AI-enabled quality assurance also allows manufacturers to collect field data about the use and performance of their products. AI-based quality assurance can increase productivity by up to 50% and improve defect detection by 90% compared with human inspection.
This may include deviations from standard operating procedures, machine abnormalities and changes in raw materials. AI-enabled quality assurance also allows manufacturers to collect field data about use and performance of their products. AI-based quality assurance can increase productivity by up to 50% and improve defect detection by 90% compared with human inspection.
ADI OtoSense accomplishes this by sensing abnormalities at any given step within a manufacturing process. For example, a common problem in metal punching is “slug pulling.” This is when punched scrap sticks to the part, which may lead to costly parts defects and harm the machine tool at the next stage in the production line. Detecting and preventing this issue can make a material difference in line performance. 
In addition, lead times to acquire replacement parts for these tools is lengthy, leading to potential production delays. ADI OtoSense can verify that the scrap metal correctly detached from the metal to reduce the potential for defective parts.
ADI OtoSense accomplishes this by sensing abnormalities at any given step within a manufacturing process. For example, a common problem in metal punching is “slug pulling.” This is when punched scrap sticks to the part, which may lead to costly parts defects and harm the machine tool at the next stage in the production line. Detecting and preventing this issue can make a material difference in line performance.
In addition, lead times to acquire replacement parts for these tools is lengthy, leading to potential production delays. ADI OtoSense can verify that the scrap metal correctly detached from the metal to reduce the potential for defective parts.
Edge-to-Enterprise Connectivity
Edge-to-Enterprise Connectivity
The digital transformation requires solutions that bridge the gap between IT and operational technologies (OT). Many manufacturers struggle to achieve their digital transformation goals because their OT systems cannot communicate seamlessly with their enterprise solutions.
ADI addresses this through a suite of solutions that includes its SmartMesh wireless sensor network, industrial ethernet, and 5G technologies to help manufacturers deliver information from connected devices to back-office systems so they can make smarter business decisions.
As the needs of manufacturers evolve along with the technologies that support them, the best strategies will need to be flexible, forward-compatible and with a focus on continuous improvement driven by data and insights.

Additional Resources

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Sources

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