However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.

A new primary torque control concept for hydrostatics mobile machines was introduced in 2018. The mentioned concept controls the pressure in a closed circuit by changing the angle of the hydraulic pump to achieve the desired pressure based on a feedback system. Thanks to this concept, a series of advantages are expected. However, while working in a Y cycle, the primary torque-controlled wheel loader has worse performance in efficiency compared to secondary controlled earthmover due to lack of recuperation ability. Alternatively, we use deep learning algorithms to improve machines' regeneration performance. In this paper, we firstly make a potential analysis to show the benefit by utilizing the regeneration process, followed by proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely detect Y cycles. Compared to existing algorithms, the CRDNN with bi-directional LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable performance but much fewer training parameters. Based on our dataset including 119 truck loading cycles, our best neural network shows a 98.2% test accuracy. Therefore, even with a simple regeneration process, our algorithm can improve the holistic efficiency of mobile machines up to 9% during Y cycle processes if primary torque concept is used.

The amount of digital data that currently exists is now growing at a rapid pace. The number is doubling every two years and it is completely transforming our basic mode of existence. According to a paper from IBM, about 2.5 billion gigabytes of data had been generated on a daily basis in the year 2012. Another article from Forbes informs us that data is growing at a pace which is faster than ever. The same article suggests that this year, 2020, about 1.7 billion of new information will be developed per second for all the human inhabitants on this planet.

Additive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.

We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.

Every few seconds there is a small puff of smoke as a weed keels over, having been zapped with a high voltage. The device doing the zapping is a prototype weeding robot developed by the Small Robot Company, a new firm operating out of an old munitions depot near Salisbury, in south-west Britain. Such machines, called "agribots", are appearing in many shapes and sizes from a variety of companies. Muddy tracks from other prototypes lead into the Small Robot Company's workshop, where a row of 3D printers make bright orange components out of plastic. That makes parts easier to find should they fall off in a field, which is a sure sign that farmers are at work here, with roboticists and scientists.

A mummified Ancient Egyptian priest is talking from beyond the grave thanks to modern technology. Nesyamun, a priest at the time of pharaoh Ramses II the Pharaohs was mummified around 3,000 years ago. His remains are so well preserved that scientists were able to map his throat, mouth and voice box using a CT scanner at Leeds General Infirmary, and recreate it using 3D printing. The priest, who is normally on display at Leeds museum, was first unwrapped in 1824 and has'true of voice' inscribed on his coffin. Academics believe his voice would have produced a vowel-like sound -- somewhere between an'a' and'e' noise.

Robotics have a mixed history in construction. Some work, especially in prefabricated building offsite, while others have not been successful, particularly when used onsite. However, that may be changing as more equipment companies are exploring the use of robotic technology and applying it to construction. One of the technologies that has shown promise is in the use of robotic arms and gantry equipment for 3D printing of concrete walls. From one of the first, if not the first, completed buildings constructed in this method, an office building in Dubai, to the research work being done by Chinese and U.S. companies as well as others in the European Union, building onsite using what is referred to as additive manufacturing techniques is moving rapidly.

Diffractive deep neural network is an optical machine learning framework that blends deep learning with optical diffraction and light-matter interaction to engineer diffractive surfaces that collectively perform optical computation at the speed of light. A diffractive neural network is first designed in a computer using deep learning techniques, followed by the physical fabrication of the designed layers of the neural network using e.g., 3-D printing or lithography. Since the connection between the input and output planes of a diffractive neural network is established via diffraction of light through passive layers, the inference process and the associated optical computation does not consume any power except the light used to illuminate the object of interest. Developed by researchers at UCLA, diffractive optical networks provide a low power, low latency and highly-scalable machine learning platform that can find numerous applications in robotics, autonomous vehicles, defense industry, among many others. In addition to providing statistical inference and generalization to classes of data, diffractive neural networks have also been used to design deterministic optical systems such as a thin imaging system.

Most 3D-printed objects are prototypes or one-off creations, in large part because 3D printing is more finicky than traditional manufacturing. Because the process works by adding layers of material atop each other, subtle changes in temperatures or material quality can result in imperfections and hours of lost work. Inkbit, a Boston-area 3D printing company, is using machine vision and artificial intelligence to help its equipment course correct. Javier Ramos, co-founder and director of hardware at Inkbit, said Inkbit's machine vision technology instantly scans the objects it prints, relying on AI to correct for any mistakes made. He imagines a future where Inkbit's tech is used on every factory floor, printing out millions of products more cheaply -- and faster -- than traditional manufacturing processes ever could.