Beverage bottling line with Andon Lights. Credit: Niculcea Florin on Unsplash
Tyson invests in AI-enabled robotics firm to boost worker productivity
Automating meat factories has long been a difficult feat because it is costly and carcasses come in varying sizes so it can be hard for robots to cut and work with all types accurately. But as the coronavirus ravaged meat plants, forcing many to temporarily shutter as thousands of workers got sick, more companies accelerated their plans for automation. Meat and poultry companies also are automating certain tasks that can be repetitious or prone to injury, such as moving or loading boxes.
Soft Robotics’ SoftAI technology uses AI and 3D vision to maneuver the company’s mGrip robotic grippers with human-like hand-eye coordination. The technology allows the automation of bulk picking for fragile and irregularly shaped proteins, produce and bakery items, according to the company. Tyson Foods is an existing user of Soft Robotics’ software.
The Autonomous Factory: Innovation through Personalized Production at Scale
Personalized products are in high demand these days. Meeting this demand is leading companies to increasingly automate their production processes and even make parts of it autonomous. However, this approach presents a trade-off: with increasing personalization comes increasing complexity. Therefore, companies need to decide on the expedient extents and levels of automation to be implemented in their factories. Two strategies that may help along the way: 1. Limited implementation in selected areas. 2. Co-creation with trusted partners.
Predictive Maintenance ROI: A $432 Billion Boost To The World’s Leading Manufacturers
By extrapolating our findings across Fortune Global 500 (FG500) industrial companies, we’ve calculated that these companies are losing 3.3 million hours in production time annually to unscheduled downtime and taking a near $1 trillion financial hit - equivalent to 8% of their annual revenues.
From the returns seen from our clients, we estimate that the widespread use of advanced, AI-driven machine-health monitoring and Predictive Maintenance could save FG500 manufacturers 1.7 million production hours a year and deliver a 4% productivity boost worth $432 billion.
BMW-led study highlights need for AI-based AM part identification
With time-to-market in the automotive industry steadily decreasing, demand for prototyping components is higher than before and the vision of large-scale production, delivering just-in-time to assembly lines, is emerging. This is not only a question of increasing output quantity and production speed but also of economic viability. The process chain of current available AM technologies still includes a high amount of labor intensive work and process steps, which lead to a high proportion of personnel costs and decreased product throughput. Also, these operations lead to bottlenecks and downtimes in the overall process chain.
Colgate-Palmolive Focuses on Machine Health to Improve Supply Chain Operations
Colgate-Palmolive is feeding this wireless sensor data into Augury’s machine health software platform. Pruitt pointed out that this enables Colgate-Palmolive’s machine data to be compared with machine data from more than 80,000 other machines connected to the Augury platform around the world.
“That massive analytical scale brings us insights on how to optimize the performance of equipment and make ever-smarter choices on how and where we deploy it,” Pruitt said. “What’s possible only gets more compelling as this AI solution harnesses more data to create better health outcomes for our machines and our business.”
Providing a specific example of how Augury’s Machine Health system has helped Colgate-Palmolive, Pruitt noted that the system’s AI detected rising temperatures in the drive of a tube maker and alerted the plant team. “Upon inspection, they discovered a problem with the motor’s water cooling system,” he said. “By getting it quickly resolved, we prevented the drive from failing due to overheating, which would’ve stopped the tube production line and incurred replacement costs. We figure the savings at 192 hours of downtime and an output of 2.8 million tubes of toothpaste, plus $12,000 for a new motor and $27,000 in variable conversion costs.”
Simplify Deep Learning Systems with Optimized Machine Vision Lighting
Deep learning cannot compensate for or replace quality lighting. This experiment’s results would hold true over a wide variety of machine vision applications. Poor lighting configurations will result in poor feature extraction and increased defect detection confusion (false positives).
Several rigorous studies show that classification accuracy reduces with image quality distortions such as blur and noise. In general, while deep neural networks perform better than or on par with humans on quality images, a network’s performance is much lower than a human’s when using distorted images. Lighting improves input data, which greatly increases the ability of deep neural network systems to compare and classify images for machine vision applications. Smart lighting — geometry, pattern, wavelength, filters, and more — will continue to drive and produce the best results for machine vision applications with traditional or deep learning systems.
Sensor-based leakage detection in vacuum bagging
A majority of aircraft components are nowadays manufactured using autoclave processing. Essential for the quality of the component is the realization of an airtight vacuum bag on top of the component to be cured. Several ways of leakage detection methods are actually used in industrial processes. They will be dealt with in this paper. A special focus is put on a new approach using flow meters for monitoring the air flow during evacuation and curing. This approach has been successfully validated in different trials, which are presented and discussed. The main benefit of the method is that in case of a leakage, a defined limit is exceeded by the volumetric flow rate whose magnitude can be directly correlated to the leakage’s size and position. In addition, the potential of this method for the localization of leakages has been investigated and is discussed.
Haschenburger, A., Menke, N. & Stüve, J. Sensor-based leakage detection in vacuum bagging. Int J Adv Manuf Technol (2021).
How SparkCognition Improved Production Efficiency for a Beverage Manufacturer
We developed seven new deep learning models to detect anomalies in resource consumption, machine status/health, and overall efficiency. (As always with a Total Plant solution, these models were tailored to the specific data, technical context, and business goals and strategies of the client.)
Once developed, the models were deployed into our AI platform for execution and KPI-driven reporting. Another key new function we delivered: predictive analysis, to anticipate problems before they occur, based on patterns detected in current and historical data, and notify the beverage manufacturer in time to take preventative action.
Finally, the results of the AI-powered analysis were delivered via a configurable dashboard that provides at-a-glance insight into the plant’s efficiency, including new KPIs reflecting water usage, water balance, power consumption, heat generation, and waste levels. This information can also now be streamed whenever, wherever, and to whomever the manufacturer requires, now or in the future.
SEM-EDS Failure Analysis in Tire Manufacturing
Tires are often considered as low-tech commodities. However, contrary to popular perception, tires are actually highly engineered structural composites. Tires contain many rubber compounds (up to 20, with several types of microstructures) that provide different levels of grip and traction. Fillers are added to the main polymer matrix to facilitate rubber reinforcement.
Tire failures often occur due to a lower or decreased material quality, and an optimal and homogenous dispersion of all the different fillers is a key factor for a higher material quality. Analytical techniques like SEM-EDS are required to understand the root cause of a failure but the material contrast obtained from a backscattered electron image is not enough to distinguish between the large variety of materials employed.
This application note demonstrates that the live quantitative elemental analysis of Axia ChemiSEM provides an efficient and easy way to characterize the different fillers, despite their similar compositional contrast.