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SoftBank Group's $200 million investment in Swiss startup Gravis Robotics marks another major robotics bet as founder Masayoshi Son seeks to make the company a pivotal player in the global AI race. SoftBank Group has invested $200 million in Gravis Robotics, a Swiss startup that makes autonomous software for construction machinery. Zurich-based Gravis announced the Series A investment in a statement on Monday but didn't share a valuation. It was reported in July that SoftBank was considering an acquisition of Gravis that might be completed in several stages, which includes injecting fresh capital over time. The deal marks another splashy investment into robotics from SoftBank, whose founder, Masayoshi Son, is trying to make his firm a pivotal player in the global artificial intelligence race. The Japanese investor, which by October is set to own a 13% stake in OpenAI, has backed a range of robotics businesses, including with the acquisition of ABB's industrial unit last year.

G-code (Geometric code) or RS-274 is the most widely used computer numerical control (CNC) and 3D printing programming language. G-code provides machine instructions for the movement of the 3D printer, especially for the nozzle, stage, and extrusion of material for extrusion-based additive manufacturing. Currently, there does not exist a large repository of curated CAD models along with their corresponding G-code files for additive manufacturing. To address this issue, we present Slice-100K, a first-of-its-kind dataset of over 100,000 G-code files, along with their tessellated CAD model, LVIS (Large Vocabulary Instance Segmentation) categories, geometric properties, and renderings. We build our dataset from triangulated meshes derived from Objaverse-XL and Thingi10K datasets. We demonstrate the utility of this dataset by finetuning GPT-2 on a subset of the dataset for G-code translation from a legacy G-code format (Sailfish) to a more modern, widely used format (Marlin). Our dataset can be found here. Slice-100K will be the first step in developing a multimodal foundation model for digital manufacturing.

Joe Hogan, Align Technology's plastics-nerd CEO, says you shouldn't eat with your aligners and that you don't need to wear your retainers every night. Joe Hogan sees a lot of smiles. When people ask him where he works, he responds with "Align Technology," which inevitably prompts the follow up, "What's that?" After months, sometimes years, the discrete rival to braces promises to give people smiles they will want to show off. Hogan gets a look at them all. And he's eager to see more. Align is embarking on its biggest manufacturing overhaul since it was founded by two Stanford Graduate School of Business classmates 29 years ago. The company is preparing to begin directly 3D printing the aligners at the core of its business, ditching what Hogan describes as a longer, more wasteful process that involves making molds. A successful transition could lower costs and make treatment more affordable in the long run, bringing Invisalign to more customers and boosting Align's profits. It also, according to Hogan, would entrench Align as the world's biggest user of 3D printers .

V astTrack consists of a few attractive properties: (1) V ast Object Category . In particular, it covers targets from 2,115 categories, significantly surpassing object classes of existing popular benchmarks ( e.g ., GOT -10k with 563 classes and LaSOT with 70 categories). Through providing such vast object classes, we expect to learn more general object tracking.

We build our dataset from triangu-lated meshes derived from Objaverse-XL and Thingi10K datasets.

Technology Engineering Man solves ceiling fans' most annoying problem His 3D-printed device finally shows a ceiling fans' speed. Breakthroughs, discoveries, and DIY tips sent six days a week. Anyone who's used an overhead ceiling fan knows it can be a pain to work. Yanking its chain gets the motor running, but there's no easy visual indication of what speed setting the fan is on. The blades can also take a frustratingly long time to reach their full speed.

Luke and Mike Bell's Peregrine 4 achieved the milestone barely a month after it was taken from them. Breakthroughs, discoveries, and DIY tips sent six days a week. A YouTuber and his father have once again reclaimed the Guinness World Record for fastest quadcopter drone . Soaring through the air at an average speed of 408 miles per hour, Luke and Mike Bell's Peregrine 4 highlights the latest intersection between engineering, creativity, and 3D-printing technology. The Bells' achievement arrives barely a month after Australian aerospace engineer Ben Biggs and his Blackbird drone set the now-previous world record at 389 mph.

The world's first 3D-printed microscope costs only $60 to build

Part qualification is crucial in additive manufacturing (AM) because it ensures that additively manufactured parts can be consistently produced and reliably used in critical applications. Part qualification aims at verifying that an additively manufactured part meets performance requirements; therefore, predicting the complex stress-strain behaviors of additively manufactured parts is critical. We develop a dynamic time warping (DTW)-transfer learning (TL) framework for additive manufacturing part qualification by transferring knowledge of the stress-strain behaviors of additively manufactured low-cost polymers to metals. Specifically, the framework employs DTW to select a polymer dataset as the source domain that is the most relevant to the target metal dataset. Using a long short-term memory (LSTM) model, four source polymers (i.e., Nylon, PLA, CF-ABS, and Resin) and three target metals (i.e., AlSi10Mg, Ti6Al4V, and carbon steel) that are fabricated by different AM techniques are utilized to demonstrate the effectiveness of the DTW-TL framework. Experimental results show that the DTW-TL framework identifies the closest match between polymers and metals to select one single polymer dataset as the source domain. The DTW-TL model achieves the lowest mean absolute percentage error of 12.41% and highest coefficient of determination of 0.96 when three metals are used as the target domain, respectively, outperforming the vanilla LSTM model without TL as well as the TL model pre-trained on four polymer datasets as the source domain.