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Moore’s Law Could Ride EUV for 10 More Years

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Author: Alan Patterson

Topics: Extreme Ultraviolet Lithography

Vertical: Semiconductor

Organizations: ASML

ASML expects that chip makers ramping up production with the new technology initially will use 0.55 NA for a cost-saving single-expose EUV process for advanced wafer layers, while using multi-pattern 0.33 NA along with older lithography technology for more mature nodes. As the single-expose 0.55 NA technology reaches its limits, somewhere around six years from now, ASML predicts that chipmakers will once again resort to multi-patterning to reach even more advanced nodes with higher transistor densities. In the next few years, ASML’s introduction of 0.55 NA tools will help leading semiconductor foundries like TSMC overcome obstacles they are now encountering at the 3nm chip process technology node.

The Dutch company is the world’s lone supplier of EUV equipment. In 2010, ASML shipped the first prototype EUV tool to an undisclosed Asian customer. Semiconductor production today is divided into the EUV “haves” like Taiwan Semiconductor Manufacturing Co. (TSMC), Samsung and Intel, which make advanced chips for customers like Apple, MediaTek and Qualcomm. The EUV “have not” chip makers years ago threw in the towel at leading nodes, jettisoning the associated multi-billion dollar capital expenditures and focusing on improved profits from legacy production lines and products that benefit little or none from process shrinks.

Read more at EE Times

The evolution of Amazon’s inventory planning system

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Topics: demand planning, operations research, E-commerce, glocalization

Organizations: Amazon

Forecasting models developed by Amazon’s Supply Chain Optimization Technologies organization predict the demand for every product. Buying systems determine the right level of product to purchase from different suppliers, while large-scale placement systems determine the optimal location for products across the hundreds of facilities belonging to Amazon’s global fulfillment network.

“In 2016, Amazon’s supply chain network was designed for scenarios where inventory from any fulfillment center could be shipped to any customer to meet a two-day promise,” said Salal Humair, senior principal research scientist at Amazon who has been with the company for seven years. This design was inadequate for the new world in which Amazon was operating; one shaped by what Humair calls the “globalization-localization imperative.”

A new multi-echelon inventory system developed by SCOT (a project whose roots stretch back to 2016) is a significant break from the past. The heart of the model is a multi-product, multi-fulfillment center, capacity-constrained model for optimizing inventory levels for multiple delivery speeds, under a dynamic fulfillment policy. The framework then uses a Lagrangian-type decomposition framework to control and optimize inventory levels across Amazon’s network in near real-time.

Broadly speaking, decomposition is a mathematical technique that breaks a large, complex problem up into smaller and simpler ones. Each of these problems is then solved in parallel or sequentially. The Lagrangian method of decomposition factors complicated constraints into the solution, while providing a ‘cost’ for violating these constraints. This cost makes the problem easier to solve by providing an upper bound to the maximization problem, which is critical when planning for inventory levels at Amazon’s scale.

Read more at Amazon Science

Industrializing Additive Manufacturing by AI-based Quality Assurance

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Author: Axel Reitinger

Topics: additive manufacturing, quality assurance

Organizations: Siemens

At Siemens we are aiming to significantly improve quality assurance in Additive Manufacturing (AM) with industrial artificial intelligence and machine-learning to accelerate the time from prototype to industrialization as well as the efficiency in large-scale serial production.

Data of all print jobs are collected in a virtual private cloud (encrypted and secured by two-factor authentication), which facilitates the analysis and comparison across multiple print jobs and factory locations.

A profile of the severity scores of the final prototype can be used to define upper control limits for the serial production, which are then the basis for an automatic monitoring of the printing quality in the industrial phase. This could include, for example, the automatic creation of non-conformance reports (NCR).

The application calculates a severity score per printed part on the layer and additionally a severity score for the whole build plate. The severity score per part is calculated on the area of the bounding box of every single part, which helps to focus on those issues in the powder bed that can negatively impact the part’s quality. It allows a detailed monitoring of every part during the print process and is used by technical experts to evaluate if further Non-Destructive-Evaluation (NDE) of the finished part is required.

Read more at Siemens Ingenuity

Manufacturing Manakins for Medical Simulation and Training

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Author: Rehana Begg

Topics: computer-aided design, digital twin

Vertical: Medical Equipment

Organizations: Simetri

Human patient simulators may mimic the human body with varying degrees of realism—or fidelity—and can be used in almost every aspect of healthcare education. The most effective medical training devices are those that have the ability to create accurate modeling of the underlying structures of the human body and replicating them digitally and physically, noted Alban. It is why Simetri’s anatomical models and medical training aides integrate electronic, mechanical and computational components and turns to materials science for innovations in soft and skeletal tissue.

The roadmap to digitization for Simetri, said Alban, started first on the mechanical side, when mechanical models started to go from sketches to using SolidWorks and 3D models, and then embedding sensors to capture data before writing the related software and then advancing the software development capability.

In another development, software can monitor when skin has been cut, and when and if the correct fascia (connective tissue encasing the muscle) has been cut. That data is transmitted digitally to the manakin, and the physiology model of that manakin is updated as a result of that new data and, therefore, displays new vital signs. “If you will have done it the right way, you will lose pulse at the foot, but if you do this procedure correctly, you will gain back pulse at the foot because you’re allowing circulation to flow through,” explained Alban.

Read more at Machine Design

Machine Shop Creates Robot Machining Cell Before There was Work for It

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Author: John Koetsier

Topics: industrial robot, robotic arm

Organizations: Custom Tool, Fanuc, Hardinge, Telit

This machine shop’s self-integrated robot was purchased without a project in mind. However, when a particular part order came in, the robot paired with the proper machine tool was an optimal fit for the job, offering consistency and an increase in throughput.

The M-10 is a six-axis robot that is designed specifically for small work cells and can lift up to 12 kg. Young purchased the robot with a force sensor, which he highly recommends. Force sensors enable robots to detect force and torque applied to the end effector. This provides it with an almost human sense of touch. Surprising to Young and his team, the force sensor was not difficult to set up and use.

After the robot purchase and the order came in, it was time to search for the right machine tool for the job. The Hardinge Bridgeport V480 APC VMC was attractive to Young because of its pallet changing system that maximizes spindle uptime.

Custom Tool’s automated data collection and reporting system developed by company president, Gillen Young, uses a web-based, Industrial Internet of Things (IIoT) platform to pull data from machines that have agents for the open-source MTConnect communication protocol as well as the company’s JobBoss enterprise resource planning (ERP) software. The platform is Devicewise for Factory from Telit, a company that offers IIoT modules, software and connectivity services and software.

Read more at Production Machining

Material World: A Greener and Smarter Future for Textile Production

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Author: Abigail Saltmarsh

Topics: sustainability, recycling

Vertical: Textile

Organizations: Verteego, SoftWear Automation

The environmental impact of textile production is well documented, with the industry as a whole ranking second only to oil in terms of global pollution levels. Massive energy and water use, together with sky-high levels of discarded chemicals and landfill waste are all key drivers in the calls for closed-loop production.

“3D design packages help designers optimize materials and design for minimal or zero waste, for example through lay efficiencies when laying pattern pieces out, or through calculating how to knit a garment in one piece without any yarn waste. Smart processes can also influence sourcing and supply strategies, for example through using computer algorithms to predicts waste or production inefficiencies, or fabric performance issues.”

Read more at DirectIndustry

Quality prediction of ultrasonically welded joints using a hybrid machine learning model

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Authors: Patrick G. Mongan, Eoin P. Hinchy, Noel P. ODowd, Conor T. McCarthy

Topics: machine learning, genetic algorithm, welding

Organizations: Confirm Smart Manufacturing Research Centre, University of Limerick

Ultrasonic metal welding has advantages over other joining technologies due to its low energy consumption, rapid cycle time and the ease of process automation. The ultrasonic welding (USW) process is very sensitive to process parameters, and thus can be difficult to consistently produce strong joints. There is significant interest from the manufacturing community to understand these variable interactions. Machine learning is one such method which can be exploited to better understand the complex interactions of USW input parameters. In this paper, the lap shear strength (LSS) of USW Al 5754 joints is investigated using an off-the-shelf Branson Ultraweld L20. Firstly, a 33 full factorial parametric study using ANOVA is carried out to examine the effects of three USW input parameters (weld energy, vibration amplitude & clamping pressure) on LSS. Following this, a high-fidelity predictive hybrid GA-ANN model is then trained using the input parameters and the addition of process data recorded during welding (peak power).

Read more at ScienceDirect

Surge Demand

In a big week for robots, they learn to navigate virtual obstacle courses, find your lost keys and erect boxes. The Robot Report has a comprehensive recap of PackExpo 2021.