Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.

Here is a list of white papers. Please let us know if there is a white paper you would like to see that's not on the list. Just send us an email containing details about the white paper including Name, Publication Date, Contact Telephone, Email and URL if available. This 5G Americas' white paper explores Edge Computing's role in the evolution of 5G architecture, the application of Cloud-native principles such as software defined networking (SDN) and network function virtualization (NFV), and identifies various methodologies currently being adopted for 5G applications. It covers detailed emerging use cases and outlines the stringent requirements needed to facilitate advanced mobility, compute, storage capabilities for emerging 5G wireless networks.

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn complex patterns from a sample of data. In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. When considering AI, it's important to understand what the data requirements are at the outset. The algorithm learns the patterns by being shown many examples of the parameter values in question--typically between a few thousand and several million. This data sample is a representation of the history of the factory process.

With technology rocketing forward, many industries are pulled into advancement, and manufacturing has benefited from the progression of machines and tools. Advanced manufacturing offers a wide array of job opportunities for competent mechanical engineers. Here are a handful of professions that have expanded thanks to advanced manufacturing techniques. Healthcare is and will be an important field for the foreseeable future, and this means that the design and manufacture of medical devices is more important than ever. There are opportunities for regulators and inspectors as well as engineers and designers.

One of the most seasoned and lofty innovation organizations, Indian Institute of Technology (IIT) Kharagpur has picked Hyderabad to set up its Center on Artificial Intelligence and Advanced Manufacturing. Telangana government's emphasis on developing innovations like Artificial Intelligence (AI), Cyber Security, Data Analytics is at long last getting national consideration. "IIT-Kharagpur arranged in one corner of the nation. They don't get an excessive number of visitor personnel and industry pioneers so effortlessly. So they were exploring around to set up their inside in greater city. It has better access from a tech perspective. What's more, that is the point at which they came to Telangana and picked Hyderabad to set up the inside. While the Center of Excellence (CoE) will come up in the long run in AI and Advanced Manufacturing. Before that they intend to offer a multi month program called Foundation in AI and ML which will begin in February. Their board has endorsed the choice. We will sign the formal update of comprehension (MoU) soon," said Jayesh Ranjan, vital secretary, IT Department, Telangana.

A huge confidence gap exists between the number of companies that try digital manufacturing strategies and those that successfully apply them, a new McKinsey & Co. survey found. In the 2018 Manufacturing Global Expert Survey, 92 percent of respondents think they lead or are on par with competitors in Industry 4.0 manufacturing strategies. The survey consisted of 700 companies in seven nations. Each had at least 50 employees and $10 million in annual revenue. It found that two-thirds rank digitizing the production value chain as a top priority.

Layer by metal layer, a complex component began to take shape with the help of an additive manufacturing machine -- known as a 3-D printer to most people -- and a clutch of USC engineering students at the region's newest center devoted to building better stuff and creating jobs. The part was being made for a Southern California company that was trying out an improved design but didn't have the machinery to produce something involving complicated shapes and angles. "We looked at the geometry and said'we should be able to,' and we printed it for them," said Satyandra K. Gupta, a USC professor and director of the Center for Advanced Manufacturing. The collaboration with the company, which had asked Gupta for complete secrecy to avoid tipping off competitors, was one of the first for the Center for Advanced Manufacturing. The facility opened in February as part of a $253-million Defense Department-sponsored consortium of dozens of corporations, schools, nonprofits and local governments around the country.