Early this week, tractor maker John Deere said it had signed a memorandum of understanding with the American Farm Bureau Federation, an agricultural trade group, promising to make it easier for farmers to access tools and software needed to repair their own equipment. The deal looked like a concession from the agricultural equipment maker, a major target of the right-to-repair movement, which campaigns for better access to documents and tools needed for people to repair their own gear. But right-to-repair advocates say that despite some good points, the agreement changes little, and farmers still face unfair barriers to maintaining equipment they own. Kevin O'Reilly, a director of the right-to-repair campaign run by the US Public Interest Research Group, a grassroots lobbying organization, says the timing of Deere's deal suggests the company may be trying to quash recent interest in right-to-repair laws from state legislators. In the past two years, corn belt states including Nebraska and Missouri, and also Montana, have considered giving farmers a legal right to tools needed to repair their own equipment. But no laws have been passed.

A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. Like many parts of modern life, tractors have gone high-tech, often running on advanced computer systems. But some manufacturers are tight-lipped about how these electronics work, making it difficult or nearly impossible for farmers and independent repair shops to diagnose and fix problems with the equipment. An agreement by John Deere may finally give farmers a greater hand in repairing the company's products.

Normally we do not cover topics related to cosmetics, being a website focused on the more industrial, loud, and explosive applications of additive manufacturing. But we are interested in production-grade 3D printing, as well as AI, computer vision, and computer software. And this new story from cosmetic giant Neutrogena has all of those components, so we'll cover it. Read on to find out how we let an AI pass judgment on this writer's face skin! It's CES 2023 week which means we will be seeing plenty of stories of interesting new innovations saturating tech websites.

It features an AI camera in the robot arm that can capture up to 30 frames per second. It comes equipped with RISK-V-based processors and AI accelerators to support features like real-time face recognition, voice recognition, and object detection. Other features in AI cameras include image classification, color recognition, line tracking, human segmentation, and more. The robot arm works with Wi-Fi, and the Bluetooth feature allows users to pair their smartphones with it. It features an intuitive 2.4" touchscreen display and a microphone with voice recognition.

Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given set of properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific sizes. One should, therefore, find multiple microarchitectural designs that exhibit the desired properties for a specimen with given dimensions. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture, meaning that peak stresses should be minimized as well. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, we propose a modular approach titled 'Deep-DRAM' that combines four decoupled models, including two deep learning models (DLM), a deep generative model (DGM) based on conditional variational autoencoders (CVAE), and direct finite element (FE) simulations. Deep-DRAM (deep learning for the design of random-network metamaterials) integrates these models into a unified framework capable of finding many solutions to the multi-objective inverse design problem posed here. The integrated framework first introduces the desired elastic properties to the DGM, which returns a set of candidate designs. The candidate designs, together with the target specimen dimensions are then passed to the DLM which predicts their actual elastic properties considering the specimen size. After a filtering step based on the closeness of the actual properties to the desired ones, the last step uses direct FE simulations to identify the designs with the minimum peak stresses.

Digitalization has taken over every walk of life, be it something as simple as buying a flight ticket, booking a movie, or ordering food. We are surrounded by innovations in technology like Additive Manufacturing, 3D Printing, Artificial Intelligence, IoT, Robotics, and more. Today artificial intelligence has worked wonders in arenas of problem-solving, learning, object detection, and others for household, industrial and commercial applications. One of the prime areas where AI is proving its potential for innovations and breakthroughs is electrical safety. AI can help to reduce human intervention and drastically reduce the factor of human error.

Recent developments in in-situ monitoring and process control in Additive Manufacturing (AM), also known as 3D-printing, allows the collection of large amounts of emission data during the build process of the parts being manufactured. This data can be used as input into 3D and 2D representations of the 3D-printed parts. However the analysis and use, as well as the characterization of this data still remains a manual process. The aim of this paper is to propose an adaptive human-in-the-loop approach using Machine Learning techniques that automatically inspect and annotate the emissions data generated during the AM process. More specifically, this paper will look at two scenarios: firstly, using convolutional neural networks (CNNs) to automatically inspect and classify emission data collected by in-situ monitoring and secondly, applying Active Learning techniques to the developed classification model to construct a human-in-the-loop mechanism in order to accelerate the labeling process of the emission data. The CNN-based approach relies on transfer learning and fine-tuning, which makes the approach applicable to other industrial image patterns. The adaptive nature of the approach is enabled by uncertainty sampling strategy to automatic selection of samples to be presented to human experts for annotation.

With constant advances in manufacturing, many companies are now looking to use AI to further improve their production process and enhance productivity. This software is better equipped to assist CNC machines to further streamline their processes and improve their operations. This article introduces you to the benefits of integrating artificial intelligence with online CNC machining services and provides you with typical limitations to expect when working with these devices. Artificial intelligence is already influencing the manufacturing sector with the numerous benefits it has to offer. The main benefit of using AI in a manufacturing company involves streamlining the production process, enhancing productivity, and improving efficiency. It may also be used to improve safety during parts fabrication.

In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties. Deep generative models are recently proposed to solve inverse problems, but these currently use expensive forward operators and struggle in precisely localizing the exact solutions and fully exploring the parameter spaces without missing solutions. In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent. Given a target property, the learned invertible model provides a posterior over the parameter space; we identify these posterior samples as an intelligent prior initialization which enables us to narrow down the search space. We then perform gradient descent to calibrate the inverse solutions within a local region. Meanwhile, a space-filling sampling is imposed on the latent space to better explore and capture all possible solutions. We evaluate our approach on three benchmark tasks and two created datasets with real-world applications from quantum chemistry and additive manufacturing, and find our method achieves superior performance compared to several state-of-the-art baseline methods. The iPage code is available at https://github.com/jxzhangjhu/MatDesINNe.

Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defects) detection. However, since abnormal states (with defects) occur much less frequently than normal ones (without defects) in a manufacturing process, the number of sensor data samples collected from a normal state is usually much more than that from an abnormal state. This issue causes imbalanced training data for classification analysis, thus deteriorating the performance of detecting abnormal states in the process. It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set. To achieve this goal, this paper proposes a novel data augmentation method based on a generative adversarial network (GAN) using additive manufacturing process image sensor data. The novelty of our approach is that a standard GAN and classifier are jointly optimized with techniques to stabilize the learning process of standard GAN. The diverse and high-quality generated samples provide balanced training data to the classifier. The iterative optimization between GAN and classifier provides the high-performance classifier. The effectiveness of the proposed method is validated by both open-source data and real-world case studies in polymer and metal AM processes.