How CT Data Analysis is helping TE Connectivity Reach Their Ultimate Goal of End-to-End Quality Inspection
As impressive as interconnected digital-platform benefits are for traditional CAD/CAE/CAM disciplines, Computed Tomography (CT)-data analysis for quality inspection has greatly expanded its reach and purpose within today’s growing digital landscape. Its new influence on the central tools of design, simulation and manufacturing are significant; CT data analysis software is making these tools, normally used well upstream of it, even better in their roles.
“We are on target for a ten-day turnaround for design, simulation, scanning and producing a digital metrology report,” says Stokowski. “So much is now going on upfront. We want as much done as possible before we hold a physical part.” Volume Graphics has helped TE Connectivity in this frontloading and looping of data with their Adaptive Measurement Templates. The templates can classify, localize and segment defects using AI and Machine Learning. They also automate much of the scan analysis and then capture information for metrology reports. The goal at TE Connectivity is to lower analysis time from 10 days to five.
Large-Format Multitasking Simplifies Tube Manufacturing
For decades, the shop relied on two three-axis machines to produce the necks and threads for its tubes, but the machines relied on manual programming for each part, a time-consuming process that also required a wealth of experience for smooth operation. Loading and part flipping also proved difficult for these machines — after all, the tubes can range from seven to 42 feet long — with both operations requiring the use of a large crane. The difficulty of finding people to operate its machines and the lengthy downtime waiting for the crane became bottlenecks for FIBA. Amid increasing demand from companies in the space exploration, alternative fuels and electronics industries, the shop decided to invest in an additional machine that could bypass some of these issues. It found a suitable solution in the Soraluce FLP 14000 multitasking machine.
Electronic Bore Gage Automates Bore Data Acquisition
Manually writing down bore gage measurements and inputting them into a statistical process control (SPC) system is slow and risks transcription errors even when working on parts with standardized requirements — this process grows unacceptably riskier with custom precision parts and bearings, such as those made by Kamatics Corporation. When the high-mix, low-volume operation started searching for ways to reliably increase productivity and profitability, moving to a digital gaging approach was high on the list.
The company teamed up with Sunnen to produce a digital gage, using the analog PG-800 gage as a starting point and rapidly prototyping the experimental system. Phil Hanna, product manager at Sunnen, says the PG-800 was used as the starting point because it was a well-proven system that Kamatics was already using. The PG-800 can cover diameters from 0.370 inch to 3.00 inches (with optimal extension fingers) and is typically used with a Sunnen PG-400E or PG-500E setting fixture to eliminate the need for master rings.
50 not out: UK manufacturing success story, Renishaw, reaches a half century
Renishaw began its manufacturing journey back in 1973 when now Executive Chairman, Sir David McMurtry, worked as an Assistant Chief of Engine Design for Rolls-Royce. At the time the company was experiencing a dimensional measurement issue in relation to the manufacture of the Olympus engines that powered the supersonic Concorde aircraft. McMurtry had a reputation as a great innovator and troubleshooter and his ingenuity brought about the creation of a prototype touch-trigger probe for co-ordinate measuring machines (CMMs).
Although today, a significant amount of Renishaw’s business is still derived from contact and non-contact measurement systems for CMMs and machine tools, the company now supplies a wide range of metrology systems for calibration, position feedback and gauging, plus associated accessories including styli and fixturing. The company has also applied its core expertise in measurement, manufacturing and process control to develop systems for non-destructive testing using Raman spectroscopy, robots and drug delivery systems for brain surgery, and is also a technology leader in the field of metal additive manufacturing (3D printing).
As metrology equipment has been rolled out on the shop floor, and integrated into automation, process control has become increasingly close knit. The data that results can be utilised to keep processes running and for validation. However, it can also provide visualisation into what’s going on in the factory. The company has recently launched Renishaw Central, a data collection system that allows data consolidation from Renishaw and third-party devices. The system provides all the process history of the part as it’s gone through the factory.
The cost of defect failures is starting to spiral out of control, and the cheapest insurance against this is more Metrology and Inspection. One of the changes the industry is adopting is advanced packaging as the primary driver to increasing semiconductor performance. The push to advanced packaging has an entire set of consequences, namely newer packaging technology and a new vector of failure.
Additionally, Metrology and Inspection spending tends to ramp before the rest of the tools, and that is why they should continue to grow so robustly in 2022 given that large fabs are just starting to come online. Metrology and inspection ramps are likely happening currently for the N3 and N5 nodes at TSMC and Intel.
Fingerprinting liquids for composites
Collo uses electromagnetic sensors and edge analytics to optimize resin degassing, mixing, infusion, polymerization and cure as well as monitoring drift from benchmarked process parameters and enabling in-situ process control.
“So, the solution we are offering is real-time, inline measurement directly from the process,” says Järveläinen. “Our system then converts that data into physical quantities that are understandable and actionable, like rheological viscosity, and it helps to ensure high-quality liquid processes and products. It also allows optimizing the processes. For example, you can shorten mixing time because you can clearly see when mixing is complete. So, you can improve productivity, save energy and reduce scrap versus less optimized processing.”
Additive Manufacturing: New Frontiers for Production and Validation
Additive manufacturing (AM) is a uniquely disruptive technology; 25-30 years ago, it changed the manufacturing paradigm by altering the way that manufacturers produced prototypes. Today, it is disrupting the way that manufacturers produce end-use parts and components, and is increasingly seen as a truly viable production technique. Now, the conversation among manufacturers is around the most judicious use of AM for production, its advantages, the sweet spot is in terms of production volumes, key opportunities, and barriers to entry. Many of these barriers relate to precision quality control of AM parts, which challenge traditional methods of surface metrology.
Optimized quality control data keep the automotive supply chain flowing
“What the FARO ScanArm allowed me to do was protect my company by proving to the customer that the issue started with their engineering print. With this particular issue, I provided a full layout to the customer with all of the profile call outs from the engineering drawing that showed where the issues were.”
Without FARO solutions and the more accurate data they provided, Taylor Metal Products might have been held financially responsible for these “no build conditions.” Thanks to the fact that the ScanArm was being used, however, Jason was able to “quickly address and correct these severe issues.”
“CAD is your perfect master; it can’t be refuted,” Jason explained. “The great thing about the FARO scans is that I can use color maps. One of the overseas manufacturers is really big about pulling those color maps because with the nature of our product, you’re taking a piece of metal and you’re bending it in different directions. The natural tendency of steel is to conform back to its original state. So, the stamping world is not like the machining world where you’re dealing with really tight tolerances, cutting and threading a hole, or boring out a hole. In the stamping world, you’re pushing metal. So that’s where the scans really come into play. The color maps show any deviation from CAD throughout the entire part. You can scan a profile with a fixed CMM, but it is a linear format, not 3D — and the CMM has to be programed to do this. With the FARO ScanArm after the CAD is locked in, it’s just one click to produce the color map. And the Japanese automotive manufacturers are big on using this technology.”
Aiming for the Top in Industrial AI, SK’s First AI Company Gauss Labs
Gauss Labs has been developing AI solutions aimed at maximizing production efficiency using the massive amount of data generated at SK hynix’s production sites. SK hynix wishes to make the overall semiconductor production process more intelligent and optimized across all procedures including process management, yield prediction, equipment repair and maintenance, materials measurement, and defect testing and prevention.
AI In Inspection, Metrology, And Test
“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”
That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”