TAIPEI, Taiwan, Jan 27, 2025 – Cincoze has launched the latest addition to the rugged computing – DIAMOND product line, the entry performance & compact industrial computer DC-1300 series. It packs enhanced performance and expansion flexibility in a compact 185 x 131 x 56.5 mm chassis. The new generation DC-1300 series can be equipped with an Intel Alder Lake-N processor, supports the essential I/O interfaces for industrial applications, and provides complete wireless transmission solutions (Wi-Fi, 5G, GNSS, etc.). The DC-1300 continues the legacy of the DC series for successful integration in space-limited industrial automation applications.
4.5X Performance and Multiple Storage Options
The new generation DC-1300 series supports the latest Alder Lake-N platform Intel Core i3-N305 processor, with computing performance of 4.5 times that of the previous generation. The DC-1300 supports up to 16GB DDR5 4800MHz in a single memory slot. It offers flexible storage options, such as 2.5-inch SATA HDD/SSD or Half-Slim SSD. Additionally, it supports expansion via the M.2 slot for NVMe SSD to meet the needs of different application scenarios.
Innovative Stackable Expansion Box Design
The DC-1300 has a set of native I/O interfaces, including LAN, USB, COM, and 4K DisplayPort, and can expand COM, DIO, display, and IGN functions through Cincoze’s exclusive CMI/CFM modules. Cincoze has launched a new Stackable Expansion Box (SEB) for the DC-1300 series. The SEB uses the built-in dual M.2 B Key slots to support I/O, CANbus, and Fieldbus modules. This design enhances its capabilities for smart manufacturing, transportation, energy management, and other applications.
Rugged, Compact, and Flexible Deployment
The DC-1300 series is compact (185 x 131 x 56.5 mm) and designed for space-limited factory automation applications. It supports various installation methods, including wall mounting, side mounting, and DIN rail mounting, for convenient and flexible deployment in machines, control cabinets, or automated guided vehicles. It supports wide temperature (-40 to 70°C) and wide voltage (9-48 VDC). It also complies with EMC standards (IEC 61000-6-2/4) and the US military shock standard (MIL-STD-810H) to ensure stable operation in harsh industrial environments.
About Cincoze
Cincoze was established in 2012 and is headquartered in New Taipei City, Taiwan. It specializes in the design and manufacture of rugged embedded computing systems tailored for edge computing and AIoT applications. Their product portfolio includes rugged embedded computers, convertible embedded systems, industrial panel PCs and monitors, and embedded GPU computers. These solutions serve industries such as manufacturing, intelligent transportation, railway computing, in-vehicle computing, warehouse and logistics, energy, kiosks, and security and surveillance. Cincoze has experienced rapid and steady growth, maintaining a compound annual growth rate (CAGR) of 40% for eight consecutive years. The company has expanded its global presence with facilities in the USA, China, and Germany, and a network spanning over 40 countries with more than 50 value-added partners.
ISLANDIA, NY, Jan 21, 2025 – Festo introduces its new valve terminal, the VTUX. The VTUX can serve as I/O, remote I/O, and decentralized I/O. These durable IP65/67-rated terminals can be placed anywhere on a machine to boost performance and speed up OEM installation. VTUX modularity results in less inventory and lower overhead costs. The VTUX is set to replace Festo’s legacy terminals.
The VTUX valve terminal
VTUX is both compact and lightweight, an advantage for end-of-arm tooling and conserving space on a machine or in a control cabinet. The terminal is built from a tough polymer, allowing use in welding environments.
The key to the VTUX’s flexibility is its modular design. For high flow rates up to 670 l/min, a high flow subbase is used and for space saving needs, the compact subbase. A single valve model can be used for both subbases, which simplifies ordering, stocking, and support. High flow and compact subbases of one or four valve positions can be mixed and matched on a terminal. VTUX terminals can have up to 128 valves with up to 128 solenoid coils. The VTUX also features vacuum capability.
The electronics side of the terminal offers the same flexibility as the pneumatics side by featuring mix-and-match modules. For example, users can add multiple analog or digital I/O modules, including I/O-Link. The modular concept continues through to the method of communication between controller and terminal. The choices include the new Festo Automation Platform (AP) for backplane speed communication in all top communication protocols. All AP-based modules appear to the control engineer to be under a single IP address that simplifies commissioning and allows smaller and less expensive PLCs to be specified. Additional communication modules include IO-Link, AP-I for decentralized I/O, and multipin connector.
VTUX terminals are assembled and tested at the Festo Regional Service Center in Mason, Ohio. They arrive ready for installation. As a key Festo product, VTUX parts are stocked worldwide to ensure quick replacements and reduce downtime. The modular design simplifies inventory, shortens training, and speeds up troubleshooting and repairs.
The moving assembly line has certainly proven its worth over the 100-plus years since Henry Ford introduced the industrial time-and-motion breakthrough in his Michigan automobile factory in 1913. The addition of robots (by GM in 1962) alongside human workers has further increased efficiency.
A Heitec engineer monitoring a robotic CT scan and analysis system as it inspects a manufactured component.
The success of the assembly line is tied to the quality of the components fed to it from production. As the complexity of modern vehicles has risen dramatically in recent decades, particularly in the case of electric vehicles (EVs), the value of pre-assembly quality checks at key stages during active production has become increasingly apparent. In addition to various methods of surface scanning, some of the leading automotive manufacturers (along with others in aerospace, energy and electronics) are using computed tomography (CT) to inspect inside components.
Examples of CT-scan-data analysis of an automotive part.
Germany’s luxury automaker, BMW, is doing CT scans of every engine housing in their iX EVs.
The iX is based on an independent platform, unlike the company’s other electric vehicles, However, it it uses the same drive unit (motor, inverter, transmission) known as HEAT (Highly-Integrated Electric Drive Train) that is employed in the other BMW models. Ensuring the consistency and quality of this valuable drivetrain is clearly a priority for the automaker.
How In-line CT Quality Inspection Works
During quality testing with in-line computed tomography (CT), an X-ray source emits radiation that penetrates objects and is attenuated depending on the material and geometry of the workpiece to be tested. A detector attached at the opposite side of the source creates a shadow image of the workpiece from the remaining radiation; the varying grey tones of the shadow images represent the differences in the nature of the test specimen. Control software then generates a 3D model of the object from the 2D X-ray image stacks. Finally, holistic analysis and visualization software is used to view and analyze the digital model from a variety of specific perspectives to either detect potential faults or to certify acceptable quality.
Robots and Scanners and Software—On the Line
At its Landshut, Germany, facility, BMW is using a combination of in-line robotics, CT scanning and detection, and analysis and visualization software, to inspect their HEAT e-engine housing during active production. The entire system was installed and integrated into their production line by Heitec, a global engineering solutions provider for a broad range of industries.
Heitec’s installation delivers a continuous-flow process in which a robot plucks an aluminum housing off the production line, deposits it on a platform that feeds it into a HeiDetect Inline CT scanner, then shifts to the other side of the scanner to retrieve an already-scanned component and moves that one back to the line. Each scan takes place in 50 seconds.
Post-scan, Heitec’s proprietary software quickly generates a digital model of each housing from the CT data, then transfers the model to Hexagon’s VGinLINE software for rapid segmentation and analysis. Viewable onscreen by engineers, graphic-and-colored alerts are automatically generated to call out any significant porosity, cracks, geometric inconsistencies, or other production or material flaws that deviate from BMW’s quality specifications.
Delivering Production Insights—at Speed
Christian Abt of Heitec
“Doing all this in the cycle time of the production line is the challenge,” says Heitec co-founder Christian Abt. “You have to speed everything up to stay within that time—or even several seconds less than that.”
Achieving this kind of fine-tuned industrial automation has become second nature to Heitec, which has been installing increasingly “smart” factory systems worldwide for more than 26 years. After completing his engineering degree, Abt first worked at the Fraunhofer Institute in Germany (“for a young engineer, a fantastic playground for robotics”). He left to co-found Heitec as robots were becoming a commodity, deciding to strengthen his young company’s automation offerings with the addition of CT scanning.
“Prior to that time, most CT was being used in the laboratory,” he says. “We saw an opportunity to bring these machines into real-world production, supporting the guys in blue jackets on the factory floor with our x-ray technology, our robots, and our knowledge of automation.”
“Manufacturing companies that are now coming to us for in-line CT have usually checked out all other possibilities but found no other solution that provides the level of quality assurance they are looking for,” says Abt. “They are demanding answers that work for them.”
Heitec customizes every installation to the specific industrial customer, sets up the robot(s) and CT scanner(s)—and then must deliver the promised rapid-inspection results. “If you’re familiar with using industrial CT in the past, you may remember hours for scanning and gigabytes of data to sift through for defects,” Abt says. “That’s not the case here in production—there’s no time for that kind of delay.”
The Secret’s in the Scan—and the Software
The solution to the time crunch? Employing lower-resolution industrial CT scans that need significantly less time to capture an image—plus Heitec’s scanner-integrated software designed to process this “less-heavy” information and reconstruct the component’s volume data into a 3D computer model. This data is then loaded into holistic analysis and visualization software that queries the digital model, employing sophisticated algorithms and, most recently, deep learning—to reliably identify, interpret and report potential quality issues despite the noisy images—within a much shorter time window.
“For the kind of advanced data processing software that would deliver the metrology results our customers were asking for, we found our way to Hexagon’s VGSTUDIO MAX and VGinLINE,” says Abt. “We had evolved from software developed for research to the situation where industry was asking for more standard and pre-qualified digital tools. Everyone was aware of VG software; it was, and is, the standard in CT software.
“The complete infrastructure of VGinLINE is a ready-to-use software package that allows us to easily integrate our own modules and functions, enabling us to combine accepted standards with flexibility. We just drop out a volume and VG starts to analyze it. This process is also easy to parallelize to more than one computer to speed things up in case the time for analyzing the data is slightly longer than the time to scan the part.”
The VG CT-data analysis and visualization software has undergone over a quarter century of evolution and development, with process automation and, most recently, deep- and machine-learning, increasingly being integrated into its offerings. Says Hexagon’s general manager for VG products, Dr. Daniela Handl, “The huge quantities of design and engineering data that can be captured by CT needs a holistic approach, coupled with robust computational capabilities, to be most valuable. Rapid results delivery is critical for engineers looking for real-time insights into production-line quality as well as how manufacturing-parameter variations might be affecting outcomes.”
Key to determining which data is significant in the scan of a component is the process of segmentation—the extraction of regions of interest (ROIs) from the 3D image data. Abrupt discontinuities in voxel grey-values typically indicate edges that define a region. By partitioning a scan into discrete regions, the software can more rapidly focus on and catalogue the characteristics of the voxels in each segment. Segmentation thus enables the processing of only the important portions of a dataset—which takes a lot less time.
Customizing Proprietary Solutions with Machine Learning
But how do you determine which segments are the important ones? Especially since, in a lower-resolution CT scan obtained on a production line, the less-crisp image slices can be more challenging to understand. This is where deep- and machine-learning, forms of artificial intelligence (AI), comes into the picture: using trainable algorithms, AI can be harnessed to process and make decisions based on noisy CT data as accurately as on hi-resolution data.
Advanced CT analysis software enables grey-value-based, shape-based, or machine-learning-based segmentation of 3D data. These defined regions of interest (ROIs) can be used to perform further analyses.
“Trainable” is the key word here. The algorithms compare what they see against an existing database of all known identifiable defects that have been customized to each manufacturer’s product geometries (which is where the “training” comes in). The algorithms “learn” to identify specific manufacturing flaws by interpreting what the less-crisp voxels show.
Over time, deep learning gets even better at what it does; by comparing what it sees against real-world product data for which the solution is known, it learns to recognize patterns and features and call out deviations from the norm. In this way, it can provide highly accurate snapshots of what’s actively happening on a particular production line—supporting confident decision-making about whether to accept or reject a part. This in turn informs production-variable changes, the effects of which can be captured, collated, and statistically examined. EV batteries are another area in which machine learning can be used in this way.
How to optimize this kind of AI methodology is what BMW is now exploring—as are forward-thinking manufacturers in other industries. Working closely under NDAs, with innovators such as Heitec and Hexagon, these companies are creating and curating their own internal data sets with which to train in-house deep learning systems.
“Training a deep learning system to one’s proprietary data understandably takes some time,” says Handl. “But it pays off by saving highly valuable time and resources on the production line.” Actionable insights into what’s happening on one’s own factory floor are contributing to the further evolution of smart manufacturing, enabling companies across many industries to identify ways to improve quality while making their products more competitive.
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