This paper presents a framework to develop the automated design of fixtures using the combination of design automation (DA), multidisciplinary optimization and robotic simulation. MDO necessitates the use of concurrent and parametric designs which are created by DA and knowledge-based engineering tools. This approach is designed to decrease the time and cost of the fixture design process by increasing the degree of automation. AutoFix provides methods and tools for automatically optimizing resource-intensive fixture design utilizing digital tools from different disciplines.
Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.
A real-world weather prediction system that detects and describes weather condition in image data is becoming prominent subject in machine vision . These systems are designed to address the challenge of weather classification using machine vision. Advances in the fields of Artificial Intelligence and Machine Learning enables applications to take on the image recognition capabilities to identify the input image . Deep learning is a vast field and narrow focusing a bit and takes up the challenge of solving an Image Classification process. Proposed deep learning algorithms by tensorflow or keras by classifying the image of weather reports by convolution neural network. CNN is an artificial neural network which inspires animal neural cortex and built upon it . The images are passed through the neural network which consists of multiple layers and filters and then identified and classified according to the weather type. The algorithm is inspired by the brain and so named as Convolution neural network.
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