Industrial robots and their end effectors have demonstrated a remarkable dexterity, matching and often exceeding that of human hands. Combined with vision systems, many industrial robots can combine high-level dexterity with object recognition for pick and place applications, but most industrial systems, the things that robots manipulate are consistent in size and shape.

The food processing industry has a very different problem: individual portions of things like fish fillets are similar, but no two are alike. The technology however is rapidly improving, and modern systems can now handle complex food processing tasks that until recently have resisted automation. 

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Every manufacturing engineer considering an IIoT implementation should put considerable focus into how the systems contribute to data collection, real-time decision-making and automated control within the production environment.

Sensors are the eyes and ears of your operation. These data collection devices continuously monitor various physical or environmental parameters on the shop floor. Sensors have been developed to measure almost any condition on the shop floor. Here are some common types:

Temperature (for controlling furnaces or ovens)

Pressure (for monitoring hydraulic or pneumatic systems)

Vibration (for detecting imbalance in motors or machinery)

Humidity (for ensuring optimal conditions in certain manufacturing processes)

Proximity (for part detection on a conveyor belt or pallet)

Torque and Force (for ensuring precise assembly or machining)

These days, most sensors provide real-time data that are essential for understanding the status of machines, the health of equipment and the quality of products.

Sensors can capture data continuously or at regular intervals, feeding it back to a centralized system or edge devices. This data allows you to monitor machine performance and production quality in real-time. By continuously monitoring conditions such as temperature, vibration and pressure, sensors can help predict equipment failures before they happen—enabling predictive maintenance strategies. This minimizes downtime and unplanned repairs. Sensors can also ensure product quality by tracking parameters such as size, weight or chemical composition, ensuring products are within acceptable tolerances.

The data collected by sensors is sent to centralized cloud systems or edge devices for real-time analysis, enabling manufacturers to make informed decisions on production adjustments and process improvements.

Actuators: The Hands of Your IIoT System

Once sensors collect and transmit data, actuators play the critical role of executing actions based on the data received. Actuators are devices that respond to control signals by performing physical tasks, including:

Opening or closing a valve (to control fluid or gas flow in a pipeline)

Adjusting motor speeds (for conveyor belts or robotic arms)

Turning machines on or off (for automated start/stop of equipment)

Controlling temperature (by activating heating or cooling systems)

Moving robotic arms or equipment (for assembly, material handling or other precision tasks)

In an IIoT system, actuators are responsible for automating responses to specific conditions detected by sensors. This creates the foundation for closed-loop control systems that can operate independently of human intervention. For example, if a temperature sensor detects overheating, the actuator could activate a cooling system without manual intervention. This automation reduces human labor and the chances of errors or inefficiencies in production. It also speeds up response times to deviations, minimizing waste and downtime.

Actuators can also adjust machine settings dynamically. For example, based on real-time data, they can modify the speed or pressure of a machine, ensuring the production process adapts to the changing needs of the workflow.

In more advanced IIoT setups, edge computing and AI-driven algorithms use sensor data to make autonomous decisions, triggering actuators without human oversight. This could be as simple as adjusting a process or as complex as rerouting products based on real-time data streams.

Working together in IIoT

In a typical IIoT system, the interaction between sensors and actuators follows a continuous cycle of data collection and response, which is often referred to as closed-loop control. Here’s an example:

Sensors detect changes: A temperature sensor detects that the temperature in a furnace is rising above the set threshold.

Data is sent: The sensor transmits this information to the controller (either an edge device or cloud platform) in real-time.

Data is analyzed: The controller analyzes the data and determines that corrective action is needed (e.g., the furnace is overheating).

Actuator takes action: Based on the analysis, the controller sends a signal to an actuator that opens a valve to release cooling air or turns on a cooling system.

Process adjustment: The actuator performs the task, and the sensor continues to monitor the process, feeding back data to ensure the temperature returns to safe levels.

Benefits of sensors and actuators in manufacturing

Increased Production Efficiency:

Sensors and actuators enable real-time adjustments to processes, ensuring that machines operate within optimal parameters. This minimizes downtime and keeps production flowing smoothly.

Enhanced Predictive Maintenance:

Continuous data from sensors allows for early detection of wear and tear or impending failures, reducing the need for reactive maintenance and minimizing unexpected breakdowns. Actuators can automatically adjust processes to prevent equipment damage.

Improved Quality Control:

Sensors track key quality metrics, and actuators can adjust the process instantly to ensure product quality remains consistent, reducing waste and scrap.

Operational Flexibility:

Sensors and actuators provide greater control over manufacturing systems, enabling them to respond flexibly to changes in production schedules, environmental factors, or even supply chain disruptions.

Cost Reduction:

Automation through sensors and actuators can lower labor costs and reduce human error. Moreover, optimized processes lead to less material waste, contributing to overall cost savings.

Data-Driven Decision Making:

By integrating sensors and actuators with a central data system (cloud or edge-based), manufacturers can leverage real-time analytics to gain actionable insights and make informed decisions to improve efficiency and productivity.

Common challenges

Let’s face it, maintaining a network of sensors and actuators and similar technology in a manufacturing environment can be tricky. Many environmental and workflow factors can result in degraded performance, even if they aren’t integrated into a broader IIoT implementation.

However, in IIoT manufacturing systems, several challenges are directly related to the integration of sensors and actuators into the broader industrial network. One key issue is communication latency and bandwidth limitations. IIoT systems rely heavily on real-time data transfer between sensors, actuators and control systems. Latency or insufficient bandwidth can delay data transmission or actuator responses, which is particularly troublesome in time-sensitive applications where quick reactions are essential.

Another challenge is connectivity and reliability issues. Since IIoT systems often involve wireless communication (e.g., Wi-Fi, LPWAN, or other IoT protocols), connectivity problems like signal dropouts, weak coverage or protocol incompatibility can disrupt the flow of critical data. In a networked environment, these disruptions can lead to missed sensor readings or commands not reaching actuators, causing downtime or unsafe conditions.

The sheer volume of data generated by IIoT devices can also lead to data overload and management challenges. With sensors constantly transmitting data, storage and processing systems can quickly become overwhelmed, making it difficult to extract actionable insights or react quickly to system needs. This can hinder operational efficiency, slow decision-making, and complicate data analysis.

Security vulnerabilities are another significant concern in IIoT systems. As sensors and actuators become more interconnected, they are exposed to potential cyber threats. Hackers could access the network to manipulate sensor data or control actuators, posing serious risks to both data integrity and physical safety.

Lastly, sensor and actuator compatibility can be an issue when integrating devices from different manufacturers or upgrading legacy systems. IIoT environments require seamless communication between different components, and incompatible sensors, actuators or communication protocols can lead to integration problems, system inefficiencies or even failures in real-time operations.

To address these challenges, best practices include using real-time networking protocols, implementing strong cybersecurity measures, employing edge computing to process data closer to the source, and ensuring that systems are compatible and interoperable across the IIoT network. These steps help ensure that the IIoT infrastructure operates reliably and efficiently.

The post What are the roles of sensors and actuators in IIoT? appeared first on Engineering.com.

Experienced design engineers can certainly estimate cycle times, throughput, quality and uptime. However, the complexity of processes and associated controls leave plenty of room for fine-tuning during engineering and after installation. It should be noted that optimization is not the same as continuous improvement. Optimization is refinement conducted on a current process where continuous improvement generally refers to changes to the process or systems. These can be done concurrently but it is best to optimize first and then concentrate on continuous improvement.

Simulation and modeling

System simulation after initial design can be used quite effectively before the final design is complete. Simulation time and costs should be worked into a project whenever possible as the payback can be quite significant. These tools can be used to test and validated designs. Identifying potential issues at the design stage can reduce the risk of costly mistakes that could delay commissioning or cause problems afterwards. Simulation can be used to evaluated machining and automation processes and serve as a tool to facilitate conversations through the project.

Process Analysis and Mapping: Some simulation can be detailed enough to be considered a “Digital Twin” of the system. Digital Twins allow for even more detailed simulations to take place. System behaviour can be evaluated under very specific conditions and inputs creating a map that enables continuous optimization and testing without disrupting actual operations. As well, a properly designed and updated Digital Twin can then operate simultaneously with an actual system providing some future predictability.

Current State Analysis: Models encourage designers to more thoroughly understand existing processes. Creating an accurate model involves documenting every step, input, output, and resource used within the system. The goal is to have a clear and comprehensive overview of how the system operates. This in turn sets the foundation for identifying areas of improvement.

Once the process is mapped out and simulated, designers can identify points in the process where delays or inefficiencies occur. These could be due to machine limitations, inadequate supply of materials, or other factors that slow down the process. By visualizing the flow of materials and information through the system, designers can enhance and streamline processes and eliminate waste.

Data monitoring and collection

An often-neglected step in the improvement process is proper and accurate data monitoring and collection. A logical and systematic approach is required with emphasis on using the proper tools for the job. If good data is not collected, the subsequent analysis will be flawed. It is extremely important to try and understand what data is needed and how accurate that data must be. A camera system, for example, intended for use as image collection or shape recognition may not be able to measure dimensional attributes for quality purposes.

Sensor Integration: Integrating sensors into machinery and processes allows for real-time data collection of various parameters such as temperature, pressure, speed, and more. This data is crucial for determining system performance and possibly identifying areas for improvement. Forward planning will help reduce costs by designing in connections and associated hardware during equipment build.

Analysis: Once the data is collected, advanced analytics can be used to help identify patterns, trends, and anomalies. Deviations from expected performance metrics may indicate issues that need addressing. Analysis can be performed on-site or even remotely by a third parts that specializes in big data collection and analysis. Modern AI learning algorithms can now proactively predict potential equipment failures before they occur minimizing downtime and even extending the lifespan of machinery. Monitoring robot joint motor performance, for example, can be used to trigger a preventative maintenance activity before a problem leads to a significant breakdown.

PLC systems

Coding: Writing code is not necessarily difficult, however, writing efficient, modular, and well-documented PLC code takes time, planning and experience. Good coding techniques is crucial to make the code easier to maintain and modify, reducing the likelihood of errors and enhancing system reliability.

Error Handling and Debugging: Robust error handling routines are essential for quickly identifying and resolving issues by operator and maintenance personal. This must be specified early as a great deal of time and effort is required. The payback is reduced downtime and smooth systems operation.

Human-Machine Interface (HMI): Designing intuitive and user-friendly interfaces makes it easier for operators to control and monitor systems. This can reduce the likelihood of operator errors and improve overall system efficiency. Providing real-time feedback and alerts to operators allows for quick responses to issues. This can include notifications about performance deviations, maintenance needs, or system faults.

Automation and robotics optimization

Path Optimization: It is important that path creation is done by experts. However, in many robotic systems, there will still be room for improvement. Optimizing the movement paths can significantly reduce cycle times and energy consumption. This involves programming robots to take the most efficient routes. Using joint moves can be faster then calculated linear or curved routes. However, creating intermediate points can sometimes force a robot to behave less erratically.

Cycle Time Reduction: Streamlining operations to reduce the time taken for each cycle of operation increases overall throughput. This can involve optimizing tool changes, reducing setup times, and eliminating redundant steps. The goal is to minimize unnecessary movements and dwells times to reduce non-value added motion.

Continuous improvement and lean methodologies

Many companies follow specific techniques to refine processes. Regardless of the methodology employed, most can be used for both optimization and continuous improvement. It should be noted that these are only tool to effectively create positive change but specific expertise in the specific method is necessary. A culture of improvement is a tremendous benefit and should not be discounted. Some examples are:

Kaizen: Implementing a culture of continuous improvement, known as Kaizen, encourages regular evaluation and enhancement of processes. This approach focuses on making small, incremental changes that collectively lead to significant improvements. The process is allowed to stabilize before moving onto the next development opportunity. Since this approach represents a culture, it does not matter if the target is quality, maintenance, cycle time, operation or some other enhancement.

Six Sigma: Utilizing Six Sigma methodologies helps reduce process variation and eliminate defects. This is a data driven focused process using a statistical approach in decision-making to improve process quality and efficiency. Although this method is mainly targeted to process improvements that effect quality, a thorough analysis of data can lead to discoveries in many areas that can be a benefit.

By advance planning and the careful implementation of some of these strategies, organizations can achieve significant improvements in the performance, efficiency, and reliability of their automation systems and associated processes. A holistic approach, involuting multiple tools and a variety of personnel can enhance productivity and minimize waste.

The post Optimization of processes and automation systems appeared first on Engineering.com.

According to Stanford University’s Artificial Intelligence Index Report 2023, in 2017, only 2.8% of all newly installed industrial robots were collaborative. By 2021, that number had increased to 7.5%. The global trend toward robots working with humans to support a range of more flexible applications continues to fuel the impressive growth of the cobot industry as part of the digital transformation of the manufacturing industry.

Part of this growth can be attributed to ongoing workforce challenges in the North American manufacturing industries. The 2024 Deloitte and the Manufacturing Institute Talent Study reported that attracting and retaining talent has remained a primary business challenge for manufacturers since before the pandemic, including in skilled positions.

• While traditional industrial robots require complex installation and dedicated, restricted cells separated from workers, cobots offer many flexibility benefits that make them more attractive to manufacturing leaders looking to make smaller, more manageable investments in automation. Some flexibility features of cobots include:
  User-friendly, no-code programming and control interfaces
• Built-in safety features that allow humans to work inside the robot envelope during operation
• Designed to be redeployable, such as being mounted on a cart and moved to different task areas
• Built-in force sensing, making certain tasks simpler without the need to configure third-party sensors
• As cobots typically don’t replace workers, cobots can have a positive effect on employees’ perception of automation and the changes automation may bring in the workplace

With so many benefits for flexible, bite-sized automation, cobots can be an ideal entry point to the world of automation for manufacturers addressing skills gap challenges, or a solution for highly automated, smart factories looking for the next value add in niche applications such as screw assembly or buffing and grinding.

Latest cobot product announcements

Schneider Electric Lexium

Image: Schneider Electric

Unveiled in April 2024 at MODEX, the two new Lexium cobots offer payloads of 3 to 18 kg, with positioning accuracy of +/- 0.02 mm (+/- 0.00079 in.) and operating radius up to 1073mm. The robots use Schneider’s EcoStruxure architecture, which connects smart devices, controls, software and services for collaborative data flow and shop-floor to top-floor machine control.

Doosan Robotics Prime-Series Cobots

Image: Doosan Robotics

The Doosan P-Series is, according to the company, the longest-reaching cobot available, with a reach of 2030mm. The P-series has a payload of 30 kg and is primarily designed for palletizing applications. Features of the P-Series cobot include lower power consumption compared to similar payload cobots by applying its built-in gravity compensation mechanism, inherent wrist-singularity free, and a 5 degree-of-freedom movement with the 4th axis removed and 6th axis speed increased to 360 degrees/second. The P-Series also includes PL (e) and Cat 4 safety ratings.

Kawasaki Robotics CL Series Cobots

Image: Kawasaki Robotics

Kawasaki Robotics’ CL Series are powered with NEURA Robotics’ robot assistance technology and feature speed of 200°/s and repeatability of ± 0.02 mm with payloads and reaches of 3kg/590mm, 5kg/800mm, 8kg/1300mm, and 10 kg/1000mm. They offer free mounting orientations, extremely small footprints and IP66 classification. Applications for the CL Series robots include finishing, parcel sorting and palletizing/depalletizing.

FANUC CRX-10ia/L Paint

Image: Fanuc

The latest addition to FANUC’s CRX line of cobots, the 10ia/L, has a payload of 10kg, reach of 1418mm and is the first collaborative paint robot to comply with explosion-proof safety standards (including IECEx, ATEX, U.S., Canada, Japan, Korea, China, Taiwan and Brazil). Meant for high-mix, low volume paint applications, even for operators with little to no robotics experience. Its “easy-teach” features including drag-and-drop programming and lead-to-teach (which may be considered a standard feature on most cobots.) In addition to painting, the robot can also be deployed for powder and liquid coating applications.

Universal Robots UR30

Image: Universal Robots

Universal Robots developed the very first collaborative robot and continues to expand its product line with the UR30, offering a 30 kg payload and 1300mm reach. According to the company, the design of the UR30 is smaller and more compact than comparable cobots, because of the importance of flexibility in collaborative robot applications. The UR30 is part of the company’s growing portfolio of products, joining the UR3e, UR5e, UR10e, UR16 and UR20

Techman Robot TM30

Image: Techman Robot

Techman Robot’s TM30 has a payload of 35 kg and reach of 1702mm. With this high reach-to-weight ratio, the TM30 is ideal for palletizing applications. According to the announcement, an ideal application is the semiconductor backend process, which includes significant manual labor for lifting and loading wafer boxes up to 35 kg. Techman robots integrate proprietary AI Vision technology, providing a series of add-on software tools for safety, incoming part positioning, barcode reading, dimension measurement and visual inspection.

Delta D-Bot Series

Image: Delta

At Hanover Messe 2024, Delta, a leader in power management and a provider of IoT-based smart green solutions, announced a new line of six collaborative robots, with payloads ranging from 6 to 30 kg and reach ranging from 800 to 1800mm. These six-axis robots offer speeds up to 200 degrees per second and accuracy within ±0.02mm. The robots offer “plug-and play” installation and a user-friendly interface designed for non-technical personnel. Applications include palletizing, pick-and-place and welding.

What’s Next for Cobots?

Robotics-as-a-Service (RaaS), which mimics the popular subscription model transforming the software and cloud service industries, should continue to grow as more small companies dive into automation. Another key technology poised to deeply impact industrial robotics is AI prompt engineering. As robot control software continues to trend away from code and toward user-friendly interfaces, the idea of prompting an AI to teach or program the robot to perform an operation is not far off.

No matter what the future holds, collaborative robots remain a solution for manufacturers looking to automate dull, dirty and dangerous tasks, without taking on a large traditional robotic automation project.

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Executives and decision makers know it’s not easy to automate industrial processes, but what they may not understand is why. The challenges arise from the fact that most facilities are made up of bespoke machines used to make specific parts, products or assemblies. Hence, there is rarely a one-size-fits-all solution.

Obvious examples of this phenomena can be seen with grippers, effectors and tools that physically interact with products or parts. This equipment must be tailored to hold, move and manipulate an object with specific geometry. But specialization doesn’t end there. An often-overlooked piece of customized equipment are industrial automation control panels. Afterall, if the control panel operates custom machinery, it makes sense that it also needs to be customized.

But these modifications aren’t easy. A search of Jameco, a supplier of industrial automation parts, for products only from MEAN WELL, which is just one of their manufacturing partners, comes up with almost 6,400 results. So, how does anyone make sense of it all?

To understand control panels and how one would go about customizing them for a particular application, engineering.com sat down with Gil Orozco, vice president of Product Management at Jameco and Harland Chen, field application engineer at MEAN WELL.

What is an industrial automation control panel?

Industrial automation control panels act as a central hub for all the components and tools used to monitor, instruct and integrate machinery. “Industrial automation control panels are the backbone of automation,” says Chen. “Panels enhance the efficiency, productivity, safety and quality of the system.”

Just like the machines they operate; panel parts need to be uniquely selected to meet particular needs. “The specific components used will depend on the intended function and complexity of the control panel,” confirms Orozco. “Customer applications are endless. [Selecting the right components] depends on what the customer requires.”

Even though contents can vary, control panels typically consist of:

• Circuit breakers and fuses, which cut the power supply in the event of excess current or faults in the system. This is done to protect other circuitry.
• Switches and/or buttons, which make up parts of the human machine interface (HMI) that enables human operators to manually control or preset operations.
• Indicators, which contain LED lights, computer monitors and gauges. These HMI parts are used to keep human operators informed of the status of the facility’s equipment.
• Power supplies, which includes the electrical batteries, generators and/or grid connections needed to ensure components operate.
• Control relays, which help control high-power devices or circuits with low power signals.
• Terminal blocks, which provide access points to connect and secure wires and cables.
• Programmable logic controllers (PLCs), which are advanced automation and control circuits used to manage equipment and systems based on measured inputs and code.

With the rise of Industry 4.0, many of these control panel components have become smarter. They can communicate with digital systems, connect to the Industrial Internet of Things (IIoT) and even digest data, predict performance or make decisions on how to operate. “The components are really in some respect endless,” says Orozco. “In some [instances], you have very smart components [and others] where you have some very basic analog components. So, it really starts with the customer’s application. How we make sense of all that depends on what the customer needs and how can we support them.”

In other words, each of the above parts must be optimized to the task being controlled by the panel. And since there are hundreds, maybe thousands of options for each part, engineering expertise is needed to ensure the panel is optimized to its needs.

What role do control panels play in industrial automation?

Control panels act as the brain and central nervous system of an automated facility. They regulate and manage systems using hardware, software and input data from HMIs, sensors, cameras and more. A control panel need not be fully automated. Some require human interactions, others can be autonomous, and many fall somewhere in between.

Chen explains, “By integrating the programming logic controls, the human machine interface … and various sensors and alternators, the control panels enable the real-time data acquisition and a precise control of the industrial operation.”

So, the benefits of the fully automated systems are that they offer consistent, precise and accurate control. In contrast, systems with human interactions may involve industrial operations that can be more unpredictable, requiring the oversight of human operators who can quickly adapt to a situation.

Automation control panel safety, compliance and regulations

Strict safety, compliance and regulation standards exist to prevent control panels from causing electrical shocks, fires and damage to people or property. “The control panel must adhere to this compliance and regulation to ensure safe operations,” Chen explains. Control panels require “electrical safety, proper grounding and protection against flash cases. Compliance with standards like UL 508A in the U.S., or ‘CE markings’ in Europe and the CSA certification in Canada are essential.”

He also notes the importance of ensuring the electronics operate at safe temperatures, meet environmental safety requirements and have ingress protection (IP) ratings — which measures how well an electrical device is protected from water or dust.

Since so much customization comes into play when finding the right automation control panel, ensuring that it meets safety, compliance and regulation standards is not easy. So once again, engineering expertise is required to guarantee success.

Engineering expertise for industrial control panels

Jameco offers almost 60 different DIN rail terminal blocks from MEAN WELL alone. When other manufacturing partners are included in the search, the number increases by a factor of three.  So, how does anyone know which control panel parts are needed for their particular setup?

Chen and Orozco suggest contacting Jameco and MEAN WELL directly. “It boils down to the customer’s needs,” says Orozco. “Applications and components are endless and there are many different brands and options … We need to understand the [given] application to provide a solution to the customer. And that’s where Jameco and MEAN WELL come in … We take an approach to understanding the customer’s requirements to show what total solutions we can offer them.”

Chen used the example of sizing a power supply. “The power supply we evaluate is based on the necessary functionalities of the [given] control panel. We consider the components, space [and] installation of the power supply.”

With the help of Jameco and MEAN WELL, manufacturers can make sense of all the available options, components and customizations they can add to their control panels. Instead of being lost in a forest of part numbers and compliance documentation, they will see a path to the right solution for a given situation.

“We evaluate based on the region, power and customer,” adds Chen. “If the customer needs to meet a specialized safety standard, our factories in China and Taiwan offer the certification needed for the specific safety and power supply standard.”

For more information on automation control panels solutions, read more about industrial power components.

The post Customizing automation control panels is challenging, but skipping it is worse appeared first on Engineering.com.

(Image: ARM Institute)

When the ARM Institute launched its Robotics Manufacturing Hub about a year ago, it quickly realized that manufacturers weren’t looking at robotics and automation because they weren’t interested in robotics, but rather the barriers to automation loomed so large that it was impossible for small and medium sized firms to know where to start. When the ARM Institute announced its no-cost Robotics Manufacturing Hub for manufacturers in the Pittsburgh region, its pipeline of interested manufacturers rapidly filled. With the ARM Institute offering a pathway to minimize the risks they associate with robotics and automation, manufacturers were, and still are, eager to explore the possibilities.

Larger manufacturing firms can more easily navigate the process of implementing automation. With greater general resources, in-house R&D, financing to invest in the upfront costs, and more time to explore solutions, they’ve more successfully been able to see the process through from start to finish. Small and medium-sized firms have to navigate more risk. They need to spend more time understanding how the changes will impact their operations, they often lack in-house robotics expertise, and they need solutions that will dynamically meet their needs without requiring constant upkeep when, in many cases, their workforce is already strained.

The ARM Institute’s Robotics Manufacturing Hub is a free resource that helps manufacturers navigate these barriers and others by identifying the best business cases for robotic solutions, testing the solutions within the manufacturer’s budget, and offering a path to implementation. Part of this solution includes the ability for small and medium sized manufacturers in the Southwestern Pennsylvania region to work directly with the ARM Institute’s team of robotics engineers and get hands-on with advanced technologies in the institute’s Pittsburgh facility.

Select Case Studies

Since the Robotics Manufacturing Hub’s creation around one year ago, the ARM Institute has worked with several manufacturers in the Pittsburgh region to explore their challenges and help them understand where robotics can address these challenges.

For example, the ARM Institute worked with a manufacturer of castings and forgings to automate its manual quality inspection process. Partnering with FARO and NEFF Automation through the Robotics Manufacturing Hub, the ARM Institute performed a proof-of-concept of a Universal Robot controlling a FARO laser scanner. The manufacturer plans to pursue implementation.

The ARM Institute also worked with a company that needed to package heavy iron and steel parts into shipping containers, creating an ergonomically uncomfortable task for a human worker. In this situation, requirements for the robotic end effector are highly specific and it’s critical to calculate the correct pick place on the parts and speed limitations of the robot to move heavy parts and prevent failure or injury. The ARM Institute is working with its member CapSen Robotics on a solution.

Inside the Physical Robotics Manufacturing Hub Facility

Much of this work is completed using the ARM Institute’s Pittsburgh facility as a neutral ground for exploration and prototyping, giving manufacturers access to equipment before they commit to installing any system.

This facility is modular, adaptable, and multi-use with OEM diversity to directly meet each manufacturer’s individual needs. ARM Institute engineers work directly in the lab and interface between suppliers and manufacturers to act in the manufacturer’s best interest and ensure that the work will address the specific challenges the manufacturer is facing.

Below is a brief overview of the equipment available through the Robotics Manufacturing Hub and application areas that can be addressed using this equipment: 

Collaborative Robots (cobot) Equipment:

• Universal Robots (UR) 5e
• Yaskawa HC10
• Fanuc CRX-10 Ai/L
• Fanuc CRX-20 Ai/L

The collaborative robots can be configured for the following applications:

• Small part handling
• Pick and place
• Vision guided grasping for pick and place applications
• Machine tending
• Process tasks including glueing and dispensing
• Inspection with Faro ARM Quantum with Laser line probe and CMM
• Inspection with Cognex 2D imaging
• Inspection with Cognex 3D imaging

Industrial Robots

• Epson VT6L
• Yaskawa GP-88
• Yaskawa GP-180
• Yaskawa Weld Cell with positioner

The industrial robots can be configured for the following applications

• Large part handling
• Large part palletizing
• Large part pick and place
• Force controlled grinding and polishing
• Welding

How to Get Involved

Small and medium sized manufacturers in the Pittsburgh region can get a free automation assessment and leverage the Robotics Manufacturing Hub at no-cost thanks to funding from the Southwestern Pennsylvania Region’s Build Back Better Regional Challenge Award. Now is a great time to get started with the Robotics Manufacturing Hub as the ARM Institute is looking to work with more manufacturers. In the future, the ARM Institute hopes to expand these services to manufacturers beyond this region and encourages those with interest in using or housing these services to reach out here. Additionally, the ARM Institute’s member organization ecosystem can leverage the Robotics Manufacturing Hub as a benefit of membership.

U.S. manufacturing resiliency is the cornerstone of our national security. The ARM Institute’s Robotics Manufacturing Hub addresses a critical need in helping to provide small and medium sized manufacturers with the resources that they need to explore and implement automation, enhancing their competitiveness and benefiting the full manufacturing ecosystem.

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Everyone wants the universal household robot. According to Jim Anderton, for widespread adoption, they are going to have to have a price point that allows monthly financing or lease payments that are roughly similar to a car, suggesting that manufacturers will need to retail units in the neighbourhood of $ 40,000 to get widescale uptake. 

The post To Get Robots in Every Home, It’s About Actuators, Not AI appeared first on Engineering.com.

The 20th century was defined by engineering. Mass production of consumer goods, atomic energy, and the development of computer data processing built the world we know today. 

In the 21st century, three technologies will define the future: controlled nuclear fusion, artificial intelligence, and a specific class of robot: humanoid, general purpose, electrically actuated robots that operate without code, and function the way humans do. 

The impact of these technologies is impossible to predict with certainty, but the latter two innovations, AI and humanoid robotics, will change the nature of work in ways that current makers of industrial SCARA robots can’t imagine.

Access all episodes of End of the Line on Engineering TV along with all of our other series.

The post Only Three Technologies Matter Now. Here’s Why. appeared first on Engineering.com.

Intrinsic, a software and AI robotics company at Google’s parent company Alphabet, has announced a collaboration with NVIDIA to leverage its AI and Isaac platform technologies to make the complex field of autonomous robotic manipulation accessible to everyone.

The announcement came at the Automate 2024 trade show in Chicago, where Intrinsic is showcasing its advances in robotic grasping and industrial scalability assisted by foundation models enabled by NVIDIA’s Isaac Manipulator.

The Isaac Manipulator is a collection of foundation models and modular GPU-accelerated libraries that help industrial automation companies build scalable and repeatable workflows for dynamic manipulation tasks by accelerating AI model training and task reprogramming.

Foundation models are based on a transformer deep learning architecture that allows a neural network to learn by tracking relationships in data. They’re trained on huge datasets to process and understand sensor and robot information similar to the way ChatGPT does for text.

This enables enhanced robot perception and decision-making and facilitates zero-shot learning—the ability to perform tasks without prior examples—and the potential for a universally applicable robotic-grasping protocol to work across grippers, environments and objects.

“For the broader industry, our work with NVIDIA shows how foundation models can have a profound impact, including making today’s processing challenges easier to manage at scale, creating previously infeasible applications, reducing development costs, and increasing flexibility for end users,” said Wendy Tan White, CEO at Intrinsic, in a blog post announcing the collaboration with NVIDIA.

Developing Better Robot Grip

Grasping is the most sought after robotics functionality, but it’s time-consuming, expensive to program and difficult to scale. Because of this, many companies still use workers to handle repetitive pick-and-place tasks.

Simulation is changing that. Using NVIDIA Isaac Sim on the NVIDIA Omniverse platform, Intrinsic generated synthetic data for vacuum grasping using computer-aided design models of sheet metal and suction grippers, creating a prototype for Trumpf Machine Tools, an industrial machine tools manufacturer.

The prototype uses Intrinsic Flowstate, a developer environment for AI-based robotics solutions, for visualizing processes, associated perception and motion planning. With a workflow that includes Isaac Manipulator, a user can generate grasp poses and Compute Unified Device Architecture (CUDA)-accelerated robot motions, which can first be evaluated in simulation with Isaac Sim before deployment in the real world with the Intrinsic platform.

This grasping skill, trained with fully synthetic data generated by NVIDIA Isaac Sim, can be used to build sophisticated solutions that perform adaptive and versatile object grasping tasks in simulations and the real world. Instead of hard-coding specific grippers to grasp specific objects in a certain way, efficient code for a particular gripper and object is auto-generated to complete the task using the foundation model and synthetic training data.

“With the latest AI foundation models, companies can program a variety of robot configurations that are able to generalize and interact with diverse objects inside real-world environments,” said Deepu Talla, Vice President of Robotics and Edge Computing at NVIDIA.

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The threat of cyberattacks on manufacturing operational technology (OT) is becoming a serious concern for companies throughout the industrial sector.

Leaning on its experience in technical development and operational implementation design, Siemens developed SIBERprotect for critical infrastructure and OT systems at industrial companies, including power plants, water treatment facilities, discrete manufacturing enterprises, military depots, data centers and control stations.

Siemens says its new SIBERprotect brings the Security, Orchestration, Automation, Response (SOAR) concept to cyber-physical systems with an OT-friendly and OT-managed methodology.

SIBERprotect responds to limit the impact of a cyber attack within milliseconds. It identifies the infected production equipment groups or plant networks and enables full visibility and a fast initial response at the automation system level.

Siemens says this quick response can result in resumption of normal operations in less than a day. 

Working in conjunction with Siemens SCALANCE S industrial security appliances, SIBERprotect places OT into a safe, isolated condition.

It determines the credible identification of a cyber-attack through threat detection technology that includes intrusion detection systems, next generation firewalls, endpoint solutions, threat/risk intelligence and other attack or intrusion detection platforms, often enhanced with AI and machine learning capabilities.

The system then initiates a rule-based notification, network isolation and equipment management sequence to protect the selected equipment.  Rapid assessment and remediation can then be performed, vastly limiting the risk of additional malware contamination.  Work cells and equipment clusters that aren’t infected can continue operation, while SIBERprotect prevents recontamination during remediation. 

The system provides detailed situational awareness, alerting operators to the exact nature of the threat, where it was detected in the network and the criticality level so the response team can execute emergency measures to prevent worst-case scenarios.

Unlike a conventional system that merely sends messages to an SOC (Security Operations Center), the SIBERprotect system is linked directly to network firewalls, automation hardware and a prioritized system of alarms to facilitate isolation of equipment and jumpstart the cyber incident response.

Other key features include automatically activating emergency backup equipment, interfacing with legacy technology such as Ethernet hubs, recovering one segment or “restore all” functionality and isolation from the site IT network to prevent further attack.

“SIBERprotect represents the reimagining of how to do SOAR, where an alert was typically sent to an SOC, then reviewed by a security analyst and addressed 30 minutes to hours after initial detection.  Meanwhile, a virus could spread throughout a line or the entire plant,” said As Chuck Tommey, a digital connectivity executive with Siemens. “SIBERprotect is sending the alerts directly to a PLC for instant action, based upon a predetermined priority of status and threat levels. The PLC parses the messages for its criticality level and instantly responds.”

SIBERprotect is part of the overall “Defense in Depth” suite offered by Siemens in compliance with IEC 62443, the international standard for industrial cybersecurity. 

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