Bi-layer metallic tube (BMT) plays an extremely crucial role in engineering applications, with rotary draw bending (RDB) the high-precision bending processing can be achieved, however, the product will further springback. Due to the complex structure of BMT and the high cost of dataset acquisi-tion, the existing methods based on mechanism research and machine learn-ing cannot meet the engineering requirements of springback prediction. Based on the preliminary mechanism analysis, a physical logic enhanced network (PE-NET) is proposed. The architecture includes ES-NET which equivalent the BMT to the single-layer tube, and SP-NET for the final predic-tion of springback with sufficient single-layer tube samples. Specifically, in the first stage, with the theory-driven pre-exploration and the data-driven pretraining, the ES-NET and SP-NET are constructed, respectively. In the second stage, under the physical logic, the PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small sample BMT dataset and composite loss function. The validity and stability of the proposed method are verified by the FE simulation dataset, the small-sample dataset BMT springback angle prediction is achieved, and the method potential in inter-pretability and engineering applications are demonstrated.

Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: 1) AutoML analysis to quickly find correlations in batch process data, and 2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements.

As data becomes the driving force of the modern world, pretty much everyone has stumbled upon such terms as data science, machine learning, artificial intelligence, deep learning, and data mining at some point. But what exactly do these terms mean? What differences and relationships exist between them? The listed terms while all interconnected can't be used interchangeably. Whether you're a specialist interested in data-driven research, a business owner eager to make the most out of modern technology, or just someone who wants to be more tech-savvy, this article will help you understand not only what studies and expertise help extract knowledge from data to make machines more intelligent but how exactly they do that. Data science, data mining, machine learning, deep learning, and artificial intelligence are the main terms with the most buzz.

However, discovering new phenomena is not the only challenge: utilizing the freshly obtained knowledge for real-world applications is crucial. With the availability of computers and large amounts of experimental/- computational data nowadays [1-4], machine learning [5-9] has proven an effective tool for this purpose. Machine learning is a class of methods that start from existing data to train a model and then predict the quantities of interest useful for a given application. For example, machine learning can predict many properties of a putative material [10-18], and moreover can understand the uncertainty in those predictions. This uncertainty can be used to design the material that is most likely to satisfy the set target criteria [19-21], avoiding the typical expensive and time-consuming cycles of trial and improvement experiments. Furthermore, the uncertainty is useful for accelerating materials discovery by guiding where new experiments should be performed in the materials space [22-24], and also for the identification of outliers and erroneous entries in materials databases [25]. While uncertainty is crucial for focusing on the most viable candidates for a given application, uncertainty itself could be a useful value for property prediction. This strategy is motivated by Wilson's Renormalization Group theory [26], in which fluctuations on all scales determine the macroscopic state of the system.

A prototype "smart bin" developed by researchers at Australia's University of Technology, Sydney (UTS) can sort recyclable materials automatically through a combination of artificial intelligence (AI), robotics, and machine vision. UTS' Xu Wang said the system can categorize different types of waste such as glass bottles, metal cans, and several varieties of plastic. "We have a camera and we're running an AI algorithm to classify different types of plastics and then we use IoT [Internet of Things] and other robotics technology to sort the waste into the bins," Wang explained. The researchers envision smart bins deployed in shopping centers, schools, cinemas, businesses, and airports.

Despite the best intentions, the sad reality is that only a fraction of the plastics we dutifully separate from the rest of our waste is ever truly recycled. And one of the biggest contributing factors to this state of affairs is that plastic recycling isn't properly sorted. According to the Australian Bureau of Statistics (ABS), almost half of the overall waste generated annually in the country is recycled. But in New South Wales alone, only 10 per cent of the state's 800,000 tonnes of plastics are recycled because they are not sorted properly, according to the Commonwealth Scientific and Industrial Research Organisation (CSIRO). "The recycling process is quite complicated. If you go to the supermarket or for the daily recycling you need to know how to properly place all the recyclable (items), like bottles or others, into the right bins. You need to know the labels, know the icons," says Dr Xu Wang, from the School of Electrical and Data Engineering at the University of Technology Sydney.

There is a great demand for the robotization of manufacturing processes fea-turing monotonous labor. Some manufacturing tasks requiring specific skills (welding, painting, etc.) suffer from a lack of workers. Robots have been used in these tasks, but their flexibility is limited since they are still difficult to program/re-program by non-experts, making them inaccessible to most companies. Robot offline programming (OLP) is reliable. However, generat-ed paths directly from CAD/CAM do not include relevant parameters repre-senting human skills such as robot end-effector orientations and velocities. This paper presents an intuitive robot programming system to capture human manufacturing skills and transform them into robot programs. Demonstra-tions from human skilled workers are recorded using a magnetic tracking system attached to the worker tools. Collected data include the orientations and velocity of the working paths. Positional data are extracted from CAD/CAM since its error when captured by the magnetic tracker, is signifi-cant. Paths poses are transformed in Cartesian space and validated in a simu-lation environment. Robot programs are generated and transferred to the real robot. Experiments on the process of glass adhesive application demonstrat-ed the intuitiveness to use and effectiveness of the proposed framework in capturing human skills and transferring them to the robot.

Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have optimal target absorption energies in the visible region as well as well-defined ground states. Machine learning (ML) accelerated discovery could overcome such challenges by enabling screening of a larger space, but is limited by the fidelity of the data used in ML model training, which is typically from a single approximate density functional. To address this limitation, we search for consensus in predictions among 23 density functional approximations across multiple rungs of Jacobs ladder. To accelerate the discovery of complexes with absorption energies in the visible region while minimizing MR character, we use 2D efficient global optimization to sample candidate low-spin chromophores from multi-million complex spaces. Despite the scarcity (i.e., approx. 0.01\%) of potential chromophores in this large chemical space, we identify candidates with high likelihood (i.e., > 10\%) of computational validation as the ML models improve during active learning, representing a 1,000-fold acceleration in discovery. Absorption spectra of promising chromophores from time-dependent density functional theory verify that 2/3 of candidates have the desired excited state properties. The observation that constituent ligands from our leads have demonstrated interesting optical properties in the literature exemplifies the effectiveness of our construction of a realistic design space and active learning approach.

Bill Gates has called for greater investment in engineered crops that can adapt to climate change and resist agricultural pests, in an effort to solve world hunger. In the latest annual Goalkeepers Report from the Bill & Melinda Gates Foundation, Gates says the global hunger crisis is so immense that food aid cannot fully address the problem. What's also needed, he argues, are innovations in farming technology that can help to reverse the crisis. Gates points in particular to a breakthrough he calls'magic seeds' - including maize that has been bred to be more resistant to hotter, drier climates, and rice that requires three fewer weeks in the field. These innovations will allow agricultural productivity to increase despite the changing climate, he argues.

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property, where design of novel drugs is an important topic in bioinformatics and chemo-informatics. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP. To improve the learning performance of prediction functions in the framework maintaining the compactness of the MILP, this paper utilizes the product of two of those descriptors as a new descriptor and then designs a method of reducing the number of descriptors. The results of our computational experiments suggest that the proposed method improved the learning performance for many chemical properties and can infer a chemical structure with up to 50 non-hydrogen atoms.