This image shows 3D-printed crystalline lattice structures with air-filled channels, known as "fluidic sensors," embedded into the structures (the indents on the middle of lattices are the outlet holes of the sensors.) These air channels let the researchers measure how much force the lattices experience when they are compressed or flattened. MIT researchers have developed a method for 3D printing materials with tunable mechanical properties, that sense how they are moving and interacting with the environment. The researchers create these sensing structures using just one material and a single run on a 3D printer. To accomplish this, the researchers began with 3D-printed lattice materials and incorporated networks of air-filled channels into the structure during the printing process.

Industrial robots have become more and more popular in manufacturing settings over the years. You can now apply a robot to a vast range of different applications, helping improve the efficiency, consistency, and productivity of your entire manufacturing and logistics process. It's important that you are familiar with this range of applications so you can get the most from robot automation. In this article, we explore 17 of the most common industrial robot applications that manufacturers are using to stay ahead. Assembly involves combining parts to create a whole completed product.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error – possibly making thousands of prints – to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real time.

MIT researchers have developed a method for 3D printing materials with tunable mechanical properties, that sense how they are moving and interacting with the environment. The researchers create these sensing structures using just one material and a single run on a 3D printer. To accomplish this, the researchers began with 3D-printed lattice materials and incorporated networks of air-filled channels into the structure during the printing process. By measuring how the pressure changes within these channels when the structure is squeezed, bent, or stretched, engineers can receive feedback on how the material is moving. The method opens opportunities for embedding sensors within architected materials, a class of materials whose mechanical properties are programmed through form and composition.

Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooling surfaces need to be covered efficiently. When joining the heatsink, previously dispensed TIM spreads over the cooling surface. Recommendations on the dispensing pattern exist only for simple surface geometries such as rectangles. For more complex geometries, Computational Fluid Dynamics (CFD) simulations are used in combination with manual experiments. While CFD simulations offer a high accuracy, they involve simulation experts and are rather expensive to set up. We propose a lightweight heuristic to model the spreading behavior of TIM. We further speed up the calculation by training an Artificial Neural Network (ANN) on data from this model. This offers rapid computation times and further supplies gradient information. This ANN can not only be used to aid manual pattern design of TIM, but also enables an automated pattern optimization. We compare this approach against the state-of-the-art and use real product samples for validation.

We are used to 20th-century machines that work well for us under normal conditions. We turn on the autopilot once the plane is airborne, but when we suspect an engine problem, we scramble to take manual control. More generally speaking, we are used to machines performing well autonomously, but when a malfunction arises, we rely on human intervention to fix things. The greatest paradigm shift for 21st-century machines may be relying on machines to successfully handle such challenging situations even better than humans. In this series of articles on AI for additive manufacturing, I am covering various aspects of real-world applications of AI.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.

Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time. They used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.

To reduce carbon emissions, electrical power systems are Outside of the power systems community, novel machine increasingly incorporating renewable generation resources into learning techniques for partial differential equations (PDEs) the energy mix. These resources are often dependent on have been used to efficiently learn evolution equations for weather inputs and, as a result, they behave stochastically PDFs of system states. We refer to such equations as PDF in the short and long terms, posing planning and operational equations, and unlike the FPE [9], many are unclosed.