According to a National Safety Council's (NSC) study, a workplace injury occurs every seven seconds. This staggering statistic equates to around 4.5 million injured workers a year. And while on-site supervisors can gauge the mood of their workers, supervisors cannot monitor workers at all times to prevent incidents. In this blog, we will explore how you can use the Machine Operator Monitor application of the Intel® OpenVINO™ toolkit to automatically infer a machine operator’s level of focus and mood based on video input of their facial expression. Information regarding a machine operator’s mood and level of focus can be helpful in protecting the operator from serious injury.
Figure 1 shows the pipeline for the Machine Operator Monitor deep-learning application. Let’s explore this pipeline and the activities that occur.
Figure 1: The Operator Pose and Mood Inference Pipeline diagram illustrates how a captured image moves through the deep neural networks and the OpenVINO™ toolkit to identify a machine operator’s level of focus and mood. (Source: Author)
The application uses images captured by a video camera mounted on a manufacturing station. The captured image flows through a series of three deep neural networks (based on the Convolutional Neural Network or CNN). CNNs are a popular type of deep neural network that are commonly used to process images. The first CNN identifies whether a face exists in the captured frame. If the first CNN does not detect a face, then there is no need to further process the image for pose or mood. If the first CNN detects a face with a user-configurable detection threshold, the face is passed on to the next two stages. The next CNN determines whether the operator is watching the machine. The CNN does this by detecting whether the operator is facing towards the camera. The final CNN detects the operator’s facial expression. The operator must have a particular expression for a configurable amount of time for it to be considered.
Figure 2 shows an example of the completed process of these three stages of deep neural networks.
Figure 2: The Machine Operator Monitor screen shows an example of the output produced after this application of the OpenVINO™ toolkit processes the captured image. (Source: Intel)
As shown in Figure 2, the time required to detect the face as well as infer the mood and pose is around 140ms. This speed permits a fast-response time, allowing a prompt warning to the operator to minimize the chance of an accident and injury. The sample application also illustrates how the Message Queue Telemetry Transport (MQTT) protocol communicates the information to an industrial data analytics system.
The Machine Operator Monitoring application was developed with the Intel® distribution of OpenVINO™ and 700 lines of Go—or 500 lines of C++. This code is primarily glue code with the complex work going on in the deep neural networks pre-trained for the Machine Operator Monitor task. The first network can detect a face and check to ensure that the face rectangle is completely inside the captured frame—i.e., not a partial face. The captured image is then passed through the pose network that checks to see if the head is tilted within a 45 degree angle relative to the machine. Finally, the face image is passed into the sentiment network to identify the operator’s mood. When paired with capable hardware such as one based upon the 6th generation Intel® Core™ processor or Intel’s Neural Compute Stick 2 powered by the Intel Movidius™ X VPU, the application can deliver impressive inference speeds that enable real-time analytics.
Gaze tracking is an important new technology with many applications, but an important one today is in vehicle-driver monitoring. The 2018 Trucking Fatalities Reach Highest Level in 29 Years article by Alan Adler revealed that while motor vehicle crash deaths are declining—2 percent last year—large truck crashes rose to a 29 year high of 9 percent last year. The increase in distracted driving is one factor contributing to the rising number of trucking fatalities.
Using a deep neural network to track a driver’s head pose in real time is one way to ensure that a driver is paying attention to the road. Using information to monitor a driver’s gaze can help identify risks and ensure compliance of drivers and as a result reduce the risks that a distracted driver brings to our crowded roads.
Furthermore, you can use head pose detection in conjunction with other technologies—such as heart-rate detection, body temperature measurement, and breathing monitors—to identify drowsiness. Focusing on the eyes, monitoring blinking and eye movements could be used to detect micro-sleep—where we enter a very brief state of unconsciousness even though our eyes remain open and we appear attentive.
It’s easy to think of other applications for head pose and expression detection. Using the sample code provided, you’ll just need to use the output classification for your application, including modifying for proper head tilt.
Using Closed Circuit Television (CCTV), a human can monitor areas for intrusion, but typically this data is used after-the-fact to verify incidents or for historical analysis. Given the large number of cameras deployed, it’s not possible to have a person per camera feed to monitor the area. Typically, a single security agent will monitor a large number of camera feeds in real-time.
Deep learning can solve this problem by automatically detecting not only if a person enters a camera’s field of view but also indicate whether the person is within a specific area in that field of view. This allows multiple restricted areas to be monitored with a real-time notification if someone violates a restricted zone. In this example of the Intel® OpenVINO™ toolkit, we’ll look at how video images can be used to identify whether a person enters a user-designated restricted area.
In prior blog posts, we’ve seen examples of face and vehicle detection using images captured by a video camera. In this application, we’ll look at a different type of detection using deep learning to identify a ‘person’ and whether they are in a restricted zone.
Figure 1 shows the pipeline for the Restricted Zone Monitor deep-learning application. Let’s explore this pipeline and the activities that occur.
Figure 1: The Restricted Zone Tracking Pipeline diagram illustrates how this application of the OpenVINO™ toolkit processes a captured image to identify whether a person enters an area and determine if that area is in a user-defined restricted zone. (Source: Author)
This image processing application uses images captured by a video camera mounted above an area that includes a restricted zone. A Convolutional Neural Network (CNN)—a type of image processing deep neural network—processes the captured images to determine if a person is violating the restricted zone. First, the CNN identifies whether a person is in the capture frame. If a person is detected, the CNN then checks to see if the person is in the restricted zone area. The user can define the restricted zone with a captured image and a mouse to create a plane in the image. Once defined, the application will generate a notification if a detected person has entered the restricted zone.
Figure 2 shows an example of the completed process of this deep neural network. Note that in this example, the CNN identified the person in under half a second, and also determined that the detected person is not in the restricted zone.
Figure 2: The Restricted Zone Monitor Output screen shows an example of this application of the OpenVINO™ toolkit identifying a person and determining that the person is not in the restricted zone. (Source: Intel)
The sample application also illustrates the use of the Message Queue Telemetry Transport (MQTT) protocol, which communicates the zone information to an industrial data analytics system.
The Restricted Zone Monitor application was developed with the Intel® distribution of OpenVINO™ and ~450 lines of Go (or 400 lines of C++). Traditional video monitoring requires a human to watch a number of monitors, which can be tedious and error prone. Removing the human from this monitoring role reduces the probability that a mistake is made and helps to ensure compliance in the workplace. Given these mistakes could result in life-threatening injuries, this is a great use of a cool technology. When paired with capable hardware such as one based upon the 6th generation Intel® Core™ processor or Intel’s Neural Compute Stick 2 powered by the Intel Movidius™ X VPU, impressive inference speeds can be attained that enable real-time analytics.
Perimeter security is an obvious use case for this technology. Detecting people in or around an area is useful as part of a physical security process, but the technology could be applied in other ways. This deep learning network is pre-trained to detect people, but it could also be trained to detect animals. For example, has a bear or other wild animal wandered into a suburban area with the potential to do harm?
Detecting people in a city could also be useful—in particular when it comes to the flow of pedestrians and traffic. Pedestrian crossings can detect when a person is waiting to cross, but stopping a busy road for one person can be less beneficial than stopping for a large group. Applying person detection to manage the flows of vehicular and pedestrian traffic could ensure the most optimal flow of people.
Video surveillance using digital cameras is a growing trend, some of which is driven by the growth of the Internet of Things (IoT). In 2016, there were an estimated 350 million surveillance cameras operating worldwide—with about 65 percent of those operating in Asia.
But these cameras can do more than just passively record video when movement is detected in the frame. The video can also be used in real-time for analysis. In this blog, we will explore how you can use the Parking Lot Monitor application of the Intel® OpenVINO™ toolkit to automatically identify parking spot availability based on cars entering or exiting a lot.
In past blog posts, we’ve explored applications of face and expression detection using images from a camera. In this application, we’ll explore a different use of deep learning to track vehicles based on direction and identify whether they are entering the lot or exiting.
Figure 1 shows the Parking Lot Vehicle Tracking Pipeline. Let’s take a closer look at what occurs in this deep-learning application.
Figure 1: The Parking Lot Vehicle Tracking Pipeline diagram illustrates how this application of the OpenVINO™ toolkit performs vehicle detection from a captured image and then counts the centroids (movement of detected vehicles) to determine the ingress and egress of vehicles. (Source: Author)
The application operates using images captured by a video camera mounted above the entry and exit to the parking lot. From a captured image, the deep neural network identifies the vehicles in the frame using a Convolutional Neural Network (CNN) trained and optimized for vehicle identification. CNNs are a popular type of deep neural network that are commonly used to process images. The CNN identifies vehicles in the captured frame, and then vehicle rectangles are used to calculate centroids to represent the vehicle. These centroids are then stored. When a new frame is captured and vehicles are detected, the new centroids are checked against the old, and the nearest old centroid indicates the vehicle (given the high speed of detection and the slow speed of the vehicle). These two samples can then indicate which direction the vehicle is travelling, and can be used to determine whether the vehicle is entering or exiting the parking lot.
Figure 2 shows the result of this deep neural network. Note that the green overlays in the image are the car centroids with their coordinates (used for tracking and correlation).
Figure 2: The Parking Lot Counter output screen shows centroids as green circles to determine if a vehicle is entering or exiting a parking lot. (Source: Intel)
The sample application also illustrates the use of the Message Queue Telemetry Transport (MQTT) protocol, which communicates the parking lot information to a data analytics system.
This application was developed with the Intel® distribution of OpenVINO™ and ~800 lines of Go (or 700 lines of C++). The complex part of this application is performed through the pre-trained deep neural network, which is accompanied by some glue code that implements simple calculations for vehicle tracking and correlation between frames (by tracking centroids representing the vehicles). Based on the size of the rectangle detected, the application can discard objects (such as pedestrians that wander into the frame). When paired with capable hardware such as one based upon the 6th generation Intel® Core™ processor or Intel’s Neural Compute Stick 2 powered by the Intel Movidius™ X VPU, impressive inference speeds can be attained that enable real-time analytics.
Many use cases exist for an application that can identify vehicles in a captured frame and then track them. Consider a scenario in which road safety engineers track vehicles in a troublesome intersection looking for potential issues (such as vehicles not honoring a stop sign, or near miss accidents at a blind spot intersection). The road safety engineers could use the statistics (centroid locations and speeds through the intersection) gathered by this application to propose changes (such as installing a light or additional stop signs).
Another use would be tracking the number of people standing in a given area. A camera installed above a pedestrian crossing or outside of an elevator could help to determine when to change the light—for example, if traffic is light, road safety engineers could optimize the traffic flow for pedestrians—or which floor to change to as a way to optimize the flow of people in and out of a building.
In industrial manufacturing, the reliability of machines and equipment is paramount. Achieving high levels of machine flexibility, optimizing production processes, and ensuring remote serviceability are key performance parameters. However, accessing and interpreting data from sensors and actuators, which are essential for machine operation, can be challenging. This difficulty often leads to unexpected equipment failures and costly downtime, making the need for efficient and reliable power supplies a key focus for those in the industrial sector.
Efficiency, productivity, and reliability in manufacturing are gauged through various metrics. These include machine downtime and maintenance analytics, which are vital for operational efficiency and equipment effectiveness. Tracking and improving these metrics are essential for reducing machine downtime and maintaining a competitive edge.
Machine downtime refers to the timeframe during which a machine is not operational due to malfunctions, maintenance, or other issues. Reducing machine downtime is critical as it directly affects production capacity and efficiency. Similarly, machine maintenance and repair activities employ additional metrics to help monitor the status and effectiveness of the maintenance efforts. These metrics are critical for predictive and preventive maintenance strategies.
By tracking and analyzing these metrics, manufacturers can obtain valuable insights into the operational aspects of their machinery and equipment, enabling proactive maintenance, informed decision-making, and continuous improvement in production processes. But achieving this is easier said than done; oftentimes, the siloed rigidity of manufacturing machines makes it practically impossible to adhere to the planned production schedule due to unscheduled downtime.
Well, that no longer must be the case, thanks to IO-Link.
IO-Link™ (IEC 61131-9) is a standardized global input/output technology that is part of the IEC 61131 family of standards. IO-Link is not a fieldbus nor a network protocol in the traditional sense; instead, IO-Link specifically focuses on single-drop digital communication interface (SDCI) for small sensors and actuators. IO-Link extends the traditional tried and tested three-wire digital input and digital output interface used by machine sensors and actuators into a low-level point-to-point communication link, allowing the transfer of parameters to sensors and actuators and the delivery of diagnostic information from those devices to the automation system.
IO-Link technology plays a pivotal role in improving equipment repairs and reducing maintenance costs, thanks in large part to its capability to provide detailed diagnostics that contribute to an increase in OEE. IO-Link facilitates the communication of crucial machine process data, including analog values and switching states. It also handles the transmission of configuration data, such as function activation and deactivation, along with identification data, like device manufacturer IDs.
Moreover, IO-Link allows for easy access to machine parameters, including sensitivity, switching thresholds, and diagnostics. This feature is particularly beneficial for maintenance personnel, enabling them to receive early warnings, such as when a power supply is at risk of tripping due to a voltage overload. Machine operators can proactively adjust the voltage setting while the machine is operational, ensuring that production continues without interruption. This capability not only guarantees greater equipment uptime but also enhances the overall utilization of the machine, making IO-Link a valuable tool in industrial settings.
Industrial systems require powerful and robust power supplies that offer not only reliable power outputs, but also maximum functionality features like adaptability and preventative functionality.
The Phoenix Contact QUINT POWER 4th generation power supplies with IO-Link are engineered with adaptability, power, preventive awareness, and robustness (Figure 1).
Figure 1: The Phoenix Contact QUINT POWER 4th generation power supplies with IO-Link are three-phase input devices with screw connection, DIN rail mounting, and an output of 24VDC / 20A (p/n 1151048) and 40A (p/n 1151047). (Source: Mouser Electronics)
These fourth-generation industrial power supplies offer maximum functionality and superior system availability through IO-Link and Selective Fuse Breaking (SFB) technology. QUINT power supplies feature the unique combination of preventive function monitoring and a robust power reserve in a compact size.
These powerfully communicative QUINT power supplies are ideally suited for ensuring the maximum availability of industrial and building automation systems and can be quickly and easily integrated or parameterized in your control environment using fieldbus or IO-Link.
The QUINT POWER supplies from Phoenix Contact, equipped with IO-Link and SFB technology, significantly enhance manufacturing efficiency, productivity, and equipment reliability. By offering advanced features like adaptability, powerful output characteristics, preventive monitoring, and robust construction, these power supplies greatly contribute to Overall Equipment Effectiveness, ensuring minimal downtime and optimal performance in industrial settings.
Arnold, Nils. 2023. “Understanding Machine Downtime: A Key Factor in Manufacturing Efficiency.” ADTANCE, March 7, 2023. https://www.adtance.com/en/blog/2023/understanding-machine-downtime
In recent years, there has been a significant shift towards electrification in various industry sectors, most notably in automotive. But significant changes are also happening in electrically powered heavy machinery and equipment, replacing traditional alternatives powered by fossil fuels. These advancements are fueled by a combination of regulatory pressures, environmental concerns, and the quest for improved operational efficiency1. Amidst these changes, the TE PowerTube Connectors, a new addition to TE’s HIVONEX portfolio, are emerging as a vanguard technology ensuring safe and reliable electrification.
Companies like Caterpillar (Cat), a prominent name in the construction and mining machinery world, have been increasingly investing in electrification and report that recent efforts to electrify heavy machines are delivering significant gains in efficiency and significant reductions in exhaust emissions.
Cat’s focus on electrification includes their hybrid diesel-electric products, where they are seeing 30 percent to 49 percent fuel efficiencies2 with their mining and truck loading applications. Additionally, their large dozer electrification projects are in progress.
Cat is also committed to fully electric heavy machines that rely on a single non-engine power source and produce zero exhaust emissions. These fully electric machines are capable of regenerating power that can be reused or delivered back to the power grid.
And in an ingenious approach, Cat has developed a power assist attachment for Cat trucks that allows the use of external electric power during certain sections of the haul road, for instance, when climbing the steep road out of a mining pit. When connected to the externally powered trolley system, the truck’s propulsion system is powered purely with electricity from the power grid—reducing fuel burn by up to 90 percent while on that section of haul road.
Caterpillar is one of many heavy equipment OEMs fully vested and committed to the electrification of their major product lines. Komatsu, another giant in the construction and mining sectors, has released electric mining trucks and has showcased plans for further electrification of their heavy machinery line-up. Volvo Construction Equipment (Volvo CE) announced its commitment to launch a range of electric compact excavators and wheel loaders and stop new diesel engine-based development of these models. John Deere, known for its agriculture machinery, has been working on electrification and introduced electric concepts for tractors that are making strides in hybrid technology. Hitachi, another giant in construction equipment, has released several hybrid machines that combine conventional engines with electric motors. Liebherr, a global leader in construction machinery, has also ventured into the electrification space. Furthermore, JCB and Bobcat have introduced the first all-electric mini excavator and all-electric skid-steer loader, respectively, demonstrating their commitment to the electrification movement.
These companies, among others, are all committed to shaping the future of electrification in the heavy machinery sector.
The road to electrification for OEMs of heavy machinery and equipment can be an arduous one. OEMs will need to make a conscious strategic choice on EV-product offerings and development and determine which product lines or applications to target first.
On the one hand, they understand the need to wean themselves from traditional fossil fuels to power their equipment and comply with regulatory pressures, environmental concerns, and improved operational efficiencies. On the other hand, these companies understand the need and value their equipment delivers to their global customer base and how any interruption to their operations can be detrimental to their bottom line. Fundamental requirements OEMs must consider with their strategic electrification choices include the following:
Lastly, advancements in the electrification of heavy machinery hinge on battery technology. High-capacity, rapid-charge batteries have paved the way for the viability of electric heavy machinery. Modern batteries can store more energy, deliver higher power, and charge faster than their predecessors. Battery regenerative systems are now being incorporated into heavy equipment. Regenerative systems capture and store energy during operations like braking, further enhancing their efficiency. Advancements in intelligent software and control mechanisms ensure optimal energy use, predictive maintenance, and enhanced operational efficiency.
This week’s New Tech Tuesday features TE Connectivity's HIVONEX PowerTube Connectors.
The TE PowerTube Connectors, the newest addition to TE’s HIVONEX portfolio, encapsulate all the requirements for the electrification of heavy machines and equipment and represent such advancements in the industry. PowerTube Connectors are designed specifically for challenging environments. These connectors offer superior resilience against factors like moisture, dust, and temperature variations. These connectors are also optimized for high current applications, ensuring minimal energy loss while catering to the demanding power needs of heavy machinery. The PowerTube connectors come with features that ensure protection against short circuits, misconnections, and electric shocks. Their design prioritizes user safety, making it a preferred choice for operators and technicians alike. Lastly, given the variety of machinery and their varied power needs, these connectors are designed to be versatile, fitting a range of applications without the need for a variety of different components.
Expanding TE’s HIVONEX portfolio further, the PowerTube connector series is a powerful solution that enables the future of e-mobility in trucks, buses, construction, agricultural vehicles, and other heavy equipment. These connectors are built to handle large electrical loads, with a maximum continuous current of up to 580A—depending on wire size and system temperature—and a voltage rating of 1000V and include multiple wire sizes between 35mm² to 150mm². The connectors are modular and scalable, offering options for different orientations and multiple positions per connector. This makes them suitable for a variety of vehicle subsystems, such as e-motors, inverters, and battery packs. The circular design of the connectors simplifies cable routing and assembly, thereby reducing the complexity and cost of installation. Additionally, the PowerTube connectors system provides reliable, safe connectivity thanks to IP6K9K dust and water ingress protection, Connector Position Assurance (CPA), High Voltage Interlock Loop (HVIL) safety features in each pin, and a robust design to withstand engine-level vibration.
As the world shifts towards a more sustainable future, electrification in heavy machinery and equipment is no longer just a vision—it's an ongoing revolution. Ensuring that this transformation is efficient, safe, and reliable requires top-tier components that can withstand the demands of the industry. The TE PowerTube Connectors are a stellar addition to TE’s HIVONEX portfolio and are perfectly poised to lead this charge. With their combination of robustness, safety, and versatility, they encapsulate the future of electrification in the heavy machinery sector, driving the industry towards a greener, more efficient future.
Traditional methods of defect detection faced a number of challenges that reduced the quality of the process. Applying deep learning algorithms to captured video information increases the speed and accuracy of identifying objects that do not meet a predefined standard. Though deep learning is a relatively new solution for defect detection, it can expand the scope of the solution from simple detection of a defect to classification of the type of defect. Training deep learning networks to identify types of defects makes it possible to automatically route objects based upon their severity—such as the size of the flaw. In this example of the Intel® OpenVINO™ toolkit, we will look at a simple example of how video images can be used to determine whether an object is defective based upon its surface area.
In prior blog posts, we’ve seen examples of face and vehicle detection using images captured by a video camera. In this application, we’ll look at a different type of detection using deep learning to identify an object on a conveyor belt, measure its surface area, and check for defects.
Figure 1 shows the Object Size Detection pipeline. Let’s explore this pipeline and the activities that occur.
Figure 1: The Object Size Detection Pipeline diagram illustrates how this application of the OpenVINO™ toolkit processes an image to determine whether an object has a defect based on its surface area. (Source: Author)
This image processing application uses images captured by a video camera mounted above a conveyor belt. A Convolutional Neural Network (CNN)—a type of image processing deep neural network—processes the captured images to determine if an object is present. First, the CNN identifies whether an object is in the capture frame. If an object is present, the CNN draws a bounding box and calculates the area that object occupies. Then, this area is checked against the predefined acceptable constraint. If the object is larger or smaller than expected, then a defect indication is communicated.
Figure 2 shows the output of the Object Size Detection application of the OpenVINO™ toolkit. Note that in this example, the CNN found the object and bounded it in order to calculate its area.
Figure 2: The Object Size Detector output screen shows an example of the calculated area of a detected object. (Source: Intel)
Defect inspection is a monotonous task and prone to error based upon the inspector. Using deep learning to inspect parts frees up people to do more useful and creative work while increasing the efficiency of defect classification. In this simple example, the area of the part is used to determine if a defect exists, but deep learning can be applied in more advanced models to inspect for various types of defects and classify them accordingly—for example, rework or salvage. When paired with capable hardware such as one based upon the 6th generation Intel® Core™ processor or Intel’s Neural Compute Stick 2 powered by the Intel Movidius™ X VPU, impressive inference speeds can be attained that enable real-time analytics.
Calculating the area of an object on a conveyor belt can be useful in a variety of environments. Take for an example the process of sorting fruits and vegetables. Traditional methods of sorting fruits and vegetables can lead to bruising. Therefore great care needs to be employed when handling these items. By adapting this example to the produce industry, fruits and vegetables could be inspected and routed based upon their size (area) and color. Deep learning can also expand on existing methods by looking at a greater number of features for grading.
A circuit board created by a desktop milling machine. (Source: Green Shoe Garage)
In recent years, the demand for digital engineering, rapid prototyping, short-run production, and the DIY electronics movement has driven significant innovations in desktop circuit board manufacturing machines. These machines enable hobbyists, researchers, and small-scale producers to design, iterate, and fabricate circuit boards in-house, drastically reducing the time from concept to working prototype. While the speed of board turnaround is the chief advantage of desktop manufacturing machines, there are many other considerations when choosing such a machine. Cost is a significant driver in that choice, and the machines available for desktop circuit board manufacturing can run from the low thousands to tens of thousands of dollars. Obviously, the cost variances are driven by available features and the underlying technologies of the different classes of machines. In this article, we highlight some of the differences between desktop board manufacturing and traditional manufacturing techniques. We will also explore the different machine categories and digital tools available on the market for desktop circuit board manufacturing.
There are numerous advantages to locally manufacturing circuit boards using desktop machines. There are also drawbacks, of course. Your board layouts will likely need significant changes if they were initially created for traditional circuit board manufacturing infrastructure. Let’s begin by looking at some of the benefits of using desktop machines for circuit board manufacturing.
Of course, every engineering decision comes with both positive and negative consequences. One should be aware of numerous differences and potential negatives when deciding to leverage desktop circuit board manufacturing machines. These include:
Several types of engineering tools are suitable for desktop circuit board manufacturing. Understanding their nuances is necessary to pick a machine that meets your needs and financial capability. Let’s take a look at some of the most common digital engineering tools used for desktop circuit board manufacturing.
Milling machines use rotary cutting bits to remove material from a PCB blank to produce circuit tracks. Some of the most popular PCB milling machines for desktop use include the Bantam Tools (formerly OtherMill) desktop CNC milling machine and the Makera Carvera. These are fast and reliable machines ideal for producing circuit boards, and these two particular manufacturers make user-friendly machines that can mill PCBs down to very fine traces, making them suitable for SMD components. Typically, copper-clad FR-1 and FR-4 boards are used for milling operations. Negatively, milling consumes more material due to the subtractive nature of the process. Milling can also be noisy and produce debris.
In contrast to the subtractive nature of milling machines, circuit board printers adopt an additive approach, depositing conductive ink onto a substrate to create circuit patterns. Popular machines on the market include the Voltera V-One, which not only prints PCBs but can also dispense solder paste and reflow components, making it a comprehensive desktop PCB production solution. The BotFactory Squink is another innovative PCB printer that, in addition to printing circuits, can also place components and cure the ink, enabling complete board production on the desktop. The main benefit of PCB printers is they are fairly clean, with very little debris created during production. The particular machines mentioned here offer additional features to make them nearly one-stop shops for low-volume PCB manufacturing. One of the shortcomings of PCB printers is the fact that the conductive inks might not be as robust as traditional, etched copper. Also, traces and pads might be limited to larger sizes due to the resolution of the print head, which can make printers ill-suited for very fine pitch components.
Another class of CNC machines includes laser etchers and cutters. While laser cutters may not be purpose-built for PCB manufacturing, they can be used to either etch away unwanted copper from a PCB or cut the PCB material itself. Recent advancements in laser technologies are pushing down costs, and combined with their highly detailed precision, lasers are gaining popularity for PCB production. Laser cutters are increasingly finding their way onto workshop benches and can create very fine details that are perfect for making circuit boards. Companies such as LPKF Laser & Electronics and Glowforge offer a range of laser-based PCB prototyping machines suitable for various applications, from simple PCBs to RF circuits. The chief advantage of laser technology is the high precision with clean edges. This is fantastic for very fine-pitched components and traces. Additionally, lasers are suitable for both etching and cutting tasks. Countering the positive aspects of laser cutters are the obvious safety concerns that arise when using a laser. It requires HEPA-grade filters to quickly and safely extract fumes from the cutting volume. Laser cutters are susceptible to the smoke generated by the etching process and can have a negative impact on quality. Lastly, laser cutters, especially high-wattage cutters, can be more expensive compared to other methods.
Making PCBs is just one part of the battle. Electronic components must be attached to the circuit board to realize a working system, resulting in an assembled printed circuit board (PCBA). In addition to the PCB manufacturing hardware, reflow ovens and pick-and-place machines are additional equipment required to go from PCB to PCBA. Some companies, such as Neoden, offer machines with a comprehensive PCBA solution that integrates multiple stages of the circuit board production process. Nedoen offers a family of desktop products for low-volume yet professional PCBA manufacturing. The Neoden FP2636 stencil printers provide a reliable and repeatable mechanism to apply solder to PCBs. The Neoden YY1 is a desktop pick-and-place for moving components from reels to the PCB. Lastly, the Neoden IN6 offers reflow soldering. Combined, these machines are ideal for those who want to go beyond just producing PCBs and move into complete board assembly. While these machines are one-stop solutions that streamline the manufacturing process, they are not entirely out of the realm of professional and serious makers, as they cost only a few thousand dollars. However, they do stretch the definition of desktop manufacturing since they tend to require a larger footprint both physically and electrically.
The rise in desktop circuit board manufacturing machines has democratized access to PCB prototyping and production. Depending on the specific requirements—be it precision, speed, versatility, or the nature of production (subtractive vs. additive)—there's likely a machine tailored to those needs. As technology advances, we can anticipate further improvements in speed, affordability, and capabilities, ensuring that in-house PCB production remains accessible to all.
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