PrintSyst.ai has launched its latest proprietary artificial intelligence (AI) engine which aims to improve the consistency and reliability of 3D printed parts. The 3DP AI-Perfecter is a pre-printing evaluation tool and has been designed to allow companies in the aerospace, defence and automotive industries to produce additively manufactured parts with greater repeatability and reduced labour, time and cost. It believes analysis of parts before the physical 3D printing to be crucial and a process that requires highly-skilled engineers to carry out, while also baring significant risks to a company's reputation should errors be made. PrintSyst has therefore spent the last couple of years focusing on artificial intelligence and leveraging the technology to create a platform that, the company claims, has enabled instant, automatic and accurate pre-printing part analysis that can save up to 99% of the preparation time and cost. "It is a scalable tool and using it is extremely user friendly and simple," commented Itamar Yona, PrintSyst's CEO. "We support multiple 3D printing technologies and our customers enjoy automatic AI-based printing recommendations.

A research team from the NYU Tandon School of Engineering has published a study that uncovers vulnerabilities in the production of carbon fiber reinforced 3D printed parts. The vulnerability is not related to the strength of the parts, but rather in protecting their toolpaths and preventing counterfeit parts. The ability to 3D print carbon fiber reinforced polymers is creating numerous exciting applications across the aerospace and industrial sectors, among others. The materials are advantageous for many reasons, but their strength-to-weight ratios and durability are most notable. However, the process of 3D printing these materials, and specifically the extrusion-based process, can actually reveal the construction of the part and its design.

Over the past 30 years, the use of glass and carbon-fiber reinforced composites in aerospace and other high-performance applications has soared along with the broad industrial adoption of composite materials. Key to the strength and versatility of these hybrid, layered materials in high-performance applications is the orientation of fibers in each layer. Recent innovations in additive manufacturing (3-D printing) have made it possible to finetune this factor, thanks to the ability to include within the CAD file discrete printer-head orientation instructions for each layer of the component being printed, thereby optimizing strength, flexibility, and durability for specific uses of the part. These 3-D-printing toolpaths (a series of coordinated locations a tool will follow) in CAD file instructions are therefore a valuable trade secret for the manufacturers. However, a team of researchers from NYU Tandon School of Engineering led by Nikhil Gupta, a professor in the Department of Mechanical and Aerospace Engineering showed that these toolpaths are also easy to reproduce--and therefore steal--with machine learning (ML) tools applied to the microstructures of the part obtained by a CT scan.

Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure high operational availability. We note that most complex machinery has a well-documented and readily accessible underlying structure capturing the inter-dependencies between sub-systems or modules. Deep learning models such as those based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) fail to explicitly leverage this potentially rich source of domain-knowledge into the learning procedure. In this work, we propose to capture the structure of a complex equipment in the form of a graph, and use graph neural networks (GNNs) to model multi-sensor time-series data. Using remaining useful life estimation as an application task, we evaluate the advantage of incorporating the graph structure via GNNs on the publicly available turbofan engine benchmark dataset. We observe that the proposed GNN-based RUL estimation model compares favorably to several strong baselines from literature such as those based on RNNs and CNNs. Additionally, we observe that the learned network is able to focus on the module (node) with impending failure through a simple attention mechanism, potentially paving the way for actionable diagnosis.

Source: AvularSince its founding six years ago, Eindhoven, Netherlands-based Avular has built aerial drones for industrial and agricultural clients. Business was good, but for the first several years, there was a frustrating limitation: "We started to get a lot of customers asking, 'Can I do this with a drone? Can I do that with a drone?'" said Albert Maas, co-founder and CEO of Avular. "We often had to say, 'I'm sorry, it's too complicated to take this very niche, dedicated system and build something else with it.'" Two years ago, Avular decided to flip that script.

Both additive manufacturing and artificial intelligence are integral to its growth strategy. "When it came to additive manufacturing, we were initially …

The DIY 3D printing community has passion and dedication for making solid objects from digital models. Recently, we have noticed electronics projects integrated with 3D printed enclosures, brackets, and sculptures, so each Thursday we celebrate and highlight these bold pioneers! Have you considered building a 3D project around an Arduino or other microcontroller? How about printing a bracket to mount your Raspberry Pi to the back of your HD monitor? And don't forget the countless LED projects that are possible when you are modeling your projects in 3D!

It just got a little easier to create soft robots that adapt to the world around them. Rice University researchers have developed a 3D printing technique (they call it "4D") for material that automatically changes to an alternate shape when subjected to an electric current, changes in temperature or simple stress. The team produced a liquid crystal polymer'ink' with two exclusive sets of molecular links -- one with the originally printed shape, and another by manipulating the material. In this case, scientists just had to heat or cool the material to flip it between a flat surface and a bumpy one, among other changes. The challenge was to craft a polymer mix that could be printed in a catalyst bath without losing its shape, Rice said.

Here at Crendon Insurance Ltd we often cover topics on 3D printing and artificial intelligence. Reporting on the progress of the 3D printing industry and how it is modernising many sectors including manufacturing, construction and automotive has taken our interest for some years now. Whilst in addition, over more recent months, we have begun highlighting the expansion of AI (artificial intelligence) and how it too, is changing the way in which humans will engage with products of the future. Over the last few years, 3D printing has demonstrated as real'gamechanger' in the world of manufacturing. Offering the ability to produce several copies of the same component at a much lower cost, cutting out the middle man to save transportation cost and time and allowing new and innovative entrepreneurs to realise their designs more independently by installing much lower cost 3D printers on-site, has provided just some of the benefits to the growth of the 3D printing industry.

Industry 4.0, or the fourth industrial revolution, has called for a merger between automated solutions and smarter, more effective operations through the application of real-time data collection. Essentially, IoT and data-based technologies will feed real-time content into an AI platform, which will then use machine learning algorithms to analyze and extract actionable insights. Beyond that, the data solutions may support additional power systems by controlling robots or informing various processes to influence output. In other words, machine learning and AI allow for a degree of autonomy in the field like never seen before. While they are incredibly promising technologies, they're still relatively new to the industry, which means manufacturers are looking for fresh and innovative ways to apply them.