Transforming a design into a high-quality product is a challenge in metal additive manufacturing due to rare events which can cause defects to form. Detecting these events in-situ could, however, reduce inspection costs, enable corrective action, and is the first step towards a future of tailored material properties. In this study a model is trained on laser input information to predict nominal laser melting conditions. An anomaly score is then calculated by taking the difference between the predictions and new observations. The model is evaluated on a dataset with known defects achieving an F1 score of 0.821. This study shows that anomaly detection methods are an important tool in developing robust defect detection methods.

This study aims to relate the time-frequency patterns of acoustic emission (AE) and other multi-modal sensor data collected in a hybrid directed energy deposition (DED) process to the pore formations at high spatial (0.5 mm) and time (< 1ms) resolutions. Adapting an explainable AI method in LIME (Local Interpretable Model-Agnostic Explanations), certain high-frequency waveform signatures of AE are to be attributed to two major pathways for pore formation in a DED process, namely, spatter events and insufficient fusion between adjacent printing tracks from low heat input. This approach opens an exciting possibility to predict, in real-time, the presence of a pore in every voxel (0.5 mm in size) as they are printed, a major leap forward compared to prior efforts. Synchronized multimodal sensor data including force, AE, vibration and temperature were gathered while an SS316L material sample was printed and subsequently machined. A deep convolution neural network classifier was used to identify the presence of pores on a voxel surface based on time-frequency patterns (spectrograms) of the sensor data collected during the process chain. The results suggest signals collected during DED were more sensitive compared to those from machining for detecting porosity in voxels (classification test accuracy of 87%). The underlying explanations drawn from LIME analysis suggests that energy captured in high frequency AE waveforms are 33% lower for porous voxels indicating a relatively lower laser-material interaction in the melt pool, and hence insufficient fusion and poor overlap between adjacent printing tracks. The porous voxels for which spatter events were prevalent during printing had about 27% higher energy contents in the high frequency AE band compared to other porous voxels. These signatures from AE signal can further the understanding of pore formation from spatter and insufficient fusion.

In the manufacturing process of heavy industrial equipment, the specific unit in the welding diagram is first manually redrawn and then the corresponding sheet metal parts are cut, which is inefficient. To this end, this paper proposes a U-net-based method for the segmentation and extraction of specific units in welding engineering drawings. This method enables the cutting device to automatically segment specific graphic units according to visual information and automatically cut out sheet metal parts of corresponding shapes according to the segmentation results. This process is more efficient than traditional human-assisted cutting. Two weaknesses in the U-net network will lead to a decrease in segmentation performance: first, the focus on global semantic feature information is weak, and second, there is a large dimensional difference between shallow encoder features and deep decoder features. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, this paper proposes a U-net jump structure model with an attention mechanism to improve the network's global semantic feature extraction ability. In addition, a U-net attention mechanism model with dual pooling convolution fusion is designed, the deep encoder's maximum pooling + convolution features and the shallow encoder's average pooling + convolution features are fused vertically to reduce the dimension difference between the shallow encoder and deep decoder. The dual-pool convolutional attention jump structure replaces the traditional U-net jump structure, which can effectively improve the specific unit segmentation performance of the welding engineering drawing. Using vgg16 as the backbone network, experiments have verified that the IoU, mAP, and Accu of our model in the welding engineering drawing dataset segmentation task are 84.72%, 86.84%, and 99.42%, respectively.

Ensoul is a framework proposed for the purpose of creating technologies that create more technologies through the combined use of networks, and nests, of energy homeostatic (enerstatic) loops and open-ended evolutionary techniques. Generative technologies developed by such an approach serve as both simple, yet insightful models of thermodynamically driven complex systems and as powerful sources of novel technologies. "Self Organizing intelligent Ultra Low power Systems" (SOULS) is a term that well describes the technologies produced by such a generative technology, as well as the generative technology itself. The term is meant to capture the abstract nature of such technologies as being independent of the substrate in which they are embedded. In other words, SOULS can be biological, artificial or hybrid in form.

Machine learning (ML), including deep learning, has recently gained tremendous popularity in a wide range of applications. However, like traditional software, ML applications are not immune to the bugs that result from programming errors. Explicit programming errors usually manifest through error messages and stack traces. These stack traces describe the chain of function calls that lead to an anomalous situation, or exception. Indeed, these exceptions may cross the entire software stack (including applications and libraries). Thus, studying the patterns in stack traces can help practitioners and researchers understand the causes of exceptions in ML applications and the challenges faced by ML developers. To that end, we mine Stack Overflow (SO) and study 11,449 stack traces related to seven popular Python ML libraries. First, we observe that ML questions that contain stack traces gain more popularity than questions without stack traces; however, they are less likely to get accepted answers. Second, we observe that recurrent patterns exists in ML stack traces, even across different ML libraries, with a small portion of patterns covering many stack traces. Third, we derive five high-level categories and 25 low-level types from the stack trace patterns: most patterns are related to python basic syntax, model training, parallelization, data transformation, and subprocess invocation. Furthermore, the patterns related to subprocess invocation, external module execution, and remote API call are among the least likely to get accepted answers on SO. Our findings provide insights for researchers, ML library providers, and ML application developers to improve the quality of ML libraries and their applications.

Application of soft and compliant joints in grasping mechanisms received an increasing attention during recent years. This article suggests the design and development of a novel bio-inspired compliant finger which is composed of a 3D printed rigid endoskeleton covered by a soft matter. The overall integrated system resembles a biological structure in which a finger presents an anthropomorphic look. The mechanical properties of such structure are enhanced through optimization of the repetitive geometrical structures that constructs a flexure bearing as a joint for the fingers. The endoskeleton is formed by additive manufacturing of such geometries with rigid materials. The geometry of the endoskeleton was studied by finite element analysis (FEA) to obtain the desired properties: high stiffness against lateral deflection and twisting, and low stiffness in the desired bending axis of the fingers. Results are validated by experimental analysis.

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Generative AI models are all the rage these days thanks in large part to OpenAI and their latest gpt-3 and gpt-4 models. Seemingly everyone has heard of their now famous and shockingly human-like chatgpt interface. Even my grandmother has tried it out and she still has a corded home phone and sends me emails from her @hotmail.com The rapid pace of development around these large language models (LLMs) has been nothing short of incredible. ChatGPT recently broke the record as the fastest-growing consumer application in history, hitting 100 million users in its first two months.

With RunwayML, you can create and experiment with generative models in a matter of minutes, without having to write a single line of code" (RunwayML Website). Demand for innovation: Generative AI has opened up new possibilities and opportunities for innovation in various fields and industries. Generative AI can help to generate new ideas, designs, products, services, etc. that can solve problems or meet needs. In manufacturing, Autodesk and Creo use generative AI to design physical objects. In some cases, they also create those objects through 3D printing or computer-controlled machining and additive manufacturing. NVIDIA's set an example through GauGAN, an AI-powered tool that can transform rough sketches into photorealistic images in real-time. "GauGAN represents a major breakthrough in AI-powered image creation, opening up new possibilities for artists, designers, and creatives.

Recently renamed Meta, Facebook is one of the undisputed giants of emerging technologies, whether in the fields of virtual reality, augmented reality, artificial intelligence or even additive manufacturing. Indeed, the group is progressively advancing in this market, slowly but surely. Notably, the American giant already announced the acquisition of Luxexcel a few months ago. This time, Meta has decided to combine all this expertise to announce a new and somewhat… surprising project. Mark Zuckerberg, founder and CEO of the former Facebook, declared in an official press release that the company has 3D printed a robot equipped with OpenAI's GPT-5 to sit on the board of directors and support it in its strategic decisions.