Scientists in Wales are 3D-printing cartilage for people born without body parts or who have missing facial features due to facial scarring. Using human cells and plant based materials, the experts say they are able to print ears, noses and other parts to help with facial reconstruction. The technology would benefit those who have had facial scarring as a result of burns, cancer and other types of trauma. The Scar Free Foundation has launched a three-year £2.5 million programme of'regenerative research' into the technology based at Swansea University with the aim to progress to clinical trials involving humans. A three-year ££2.5 million research programme funded by the Scar Free Foundation and Health and Care Research Wales at Swansea University will aim to advance the development of 3D bioprinted facial cartilage According to the Scar Free Foundation, patients living with the loss of facial features have told researchers that existing plastic prostheses didn't feel'part of them' and would prefer their own tissue to be used for reconstruction.

Construction technology continues to evolve, including the creation of a variety of infrastructure, including three-dimensional (3D) prints. Now, the world's first 3D-printed steel structure, a'living laboratory' bridge, has been unveiled by a robot in Amsterdam. This pedestrian bridge with smart sensors will replace the old bridge under external restoration for the next two years. The 3D-printed footbridge, which is over four years in the making, is the result of a unique collaboration between MX3D, software company Autodesk, chief engineer Arup, steel giant ArcelorMittal, the City of Amsterdam, and the University of Twente, among others. MX3D made this design possible by turning welding robots with intelligent software into industrial 3D printers.

The artificial intelligence (AI) market in the agriculture industry is driven by maximizing profits in farm operations.

With time-to-market in the automotive industry steadily decreasing, the demand for additive manufactured prototyping components is higher than ever. However, in order to make larger 3D printed volumes tangible, process chains still need to be optimized and further developed in regards to output quantity, production speed, and economic viability, according to a new study by German multinational automotive firm BMW. Having identified a need to further optimize and increase the efficiency of additive manufacturing technologies and their process chains, BMW has conducted research into the complexity and economical value of Artificial Intelligence (AI) for the automated identification of 3D printed parts. The paper outlines the state-of-play of current available additive manufacturing process chains, the complexities of using AI for part recognition, and the economic viability of using AI-based platforms such as AM-VISION, an automated machine learning part recognition system from Dutch 3D printing, post-processing and automation firm AM-Flow, to further industrialize overall 3D printing process chains. The research paper, which has been compiled by authors from BMW, AM-Flow and the University of Duisburg-Essen (UDE), highlights how additive manufacturing's technological progress is enabling higher production speeds, increased choice of materials, and adjustable robust mechanical properties within parts that resemble those of conventional products.

Researchers from Baidu Research Robotics and Auto-Driving Lab (RAL) and the University of Maryland, College Park, have introduced an autonomous excavator system (AES) that can perform material loading tasks for a long duration without any human intervention while offering performance closely equivalent to that of an experienced human operator. AES is among the world's first uncrewed excavation systems to have been deployed in real-world scenarios and continuously operating for over 24 hours, bringing about industry-leading benefits in terms of enhanced safety and productivity. The researchers described their methodology in a research paper published on June 30, 2021, in Science Robotics. "This work presents an efficient, robust, and general autonomous system architecture that enables excavators of various sizes to perform material loading tasks in the real world autonomously," said Dr. Liangjun Zhang, corresponding author and the Head of Baidu Research Robotics and Auto-Driving Lab. Excavators are vital for infrastructure construction, mining, and rescue applications.

Applicable only in its native Sweden, the firm's patent covers the uploading of a patient's 3D scan to an autonomous system, which uses this data to perform cosmetic filler procedures via bioprinting-armed robotics. Given that it's invention potentially provides patients with faster and more accurate plastic surgeries, CELLINK says that with further tweaking, it could find commercial applications moving forwards. "This invention shows our innovative research & development capabilities and passion for improving patient's outcomes, whether for cosmetic or reconstructive surgery," said Dr. Héctor Martínez, CTO of CELLINK. "This granted patent can potentially offer commercial opportunities for the CELLINK Group." "We foresee that this robotic system enables cutting-edge possibilities to deliver personalized aesthetic outcomes and we will continue to explore it."

This led him to complete a master's degree in University College Dublin, followed by a PhD in machine learning in additive manufacturing (more …

Caterpillar Inc. (often shortened to CAT) is an American Fortune 100 corporation that designs, develops, engineers, manufactures, markets, and sells machinery, engines, financial products, and insurance to customers via a worldwide dealer network. It is the world's largest construction-equipment manufacturer. In 2018, Caterpillar was ranked number 65 on the Fortune 500 list and number 238 on the Global Fortune 500 list. Caterpillar stock is a component of the Dow Jones Industrial Average . CATERPILLAR INC.&tbm isch Caterpillar is the world's leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. We are a leader and proudly have the largest global presence in the industries we serve.

In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraints while maximizing one or more objective functions. The synthesis problem is typically characterized by a physical process in which many different realizations may achieve the goal. This many-to-one map presents challenges to the supervised learning of feed-forward synthesis, as the set of viable designs may have a complex structure. In addition, the non-differentiable nature of many physical simulations prevents direct optimization. We address both of these problems with a two-stage neural network architecture that we may consider to be an autoencoder. We first learn the decoder: a differentiable surrogate that approximates the many-to-one physical realization process. We then learn the encoder, which maps from goal to design, while using the fixed decoder to evaluate the quality of the realization. We evaluate the approach on two case studies: extruder path planning in additive manufacturing and constrained soft robot inverse kinematics. We compare our approach to direct optimization of design using the learned surrogate, and to supervised learning of the synthesis problem. We find that our approach produces higher quality solutions than supervised learning, while being competitive in quality with direct optimization, at a greatly reduced computational cost.

This week in a paper published in the journal Nature, researchers at Google detailed how they used AI to design the next generation of tensor processing units (TPU), the company's application-specific integrated circuits optimized for AI workloads. While the work wasn't novel -- Google's been refining the technique for the better part of years -- it gave the clearest illustration yet of AI's potential in hardware design. But the Nature paper suggests AI can at the very least augment human designers to accelerate the brainstorming process. Beyond chips, companies like U.S.- and Belgium-based Oqton are applying AI to design domains including additive manufacturing. Oqton's platform automates CNC, metal, and polymer 3D printing and hybrid additive and subtractive workflows, like creating castable jewelry wax.