Genetic Algorithm

Assembly Line

The Multi Crane Scheduling Problem: A Comparison Between Genetic Algorithms and Neural Network approaches based on Simulation Modeling

Date:

Authors: Naomie Bartoli, Roberto Revetria, Emanuele Morra, Gabriele Galli, Edward Williams

Topics: Simulation, Genetic Algorithm, Multilayer Perceptron

Organizations: University of Genoa, University of Michigan-Dearborn, PMC

The internal logistics for warehouses of many industrial applications, based on the movement of heavy goods, is commonly solved by the installment of a multi-crane system. The job scheduling of a multi-crane is an interesting problem of optimization, solved in many ways in the past: this paper describes a comparison between the optimization by the use of Genetic Algorithms and the machine learning piloting driven by Neural Networks. A case-study for steel coil production is proposed as a test frame for two different simulation software tools, one based on heuristic solution and one on machine learning; performances and data achieved from reviews and simulations are compared.

Read more at PMC White Papers

Manufacturing line design configuration with optimized resource groups

Date:

Author: Takahiro Nakano

Topics: Genetic Algorithm, Manufacturing Line Commissioning

Organizations: Hitachi

Skilled line engineers spend several months designing a manufacturing line based on their experience. Optimization of the four design specifications from the viewpoint of productivity and equipment continuity is required for the line design process. However, these four design specifications are highly dependent on each other and the number of feasible combinations of the specifications is enormous and difficult to automate.

To solve these issues, our research introduces the concept of a resource group that enables a methodology to solve the four design items hierarchically and develops methods to quickly build new manufacturing lines in response to changes in product varieties and manufacturing fluctuations in a factory.

Read more at Hitachi Industrial AI Blog

Quality prediction of ultrasonically welded joints using a hybrid machine learning model

Date:

Authors: Patrick G. Mongan, Eoin P. Hinchy, Noel P. ODowd, Conor T. McCarthy

Topics: machine learning, genetic algorithm, welding

Organizations: Confirm Smart Manufacturing Research Centre, University of Limerick

Ultrasonic metal welding has advantages over other joining technologies due to its low energy consumption, rapid cycle time and the ease of process automation. The ultrasonic welding (USW) process is very sensitive to process parameters, and thus can be difficult to consistently produce strong joints. There is significant interest from the manufacturing community to understand these variable interactions. Machine learning is one such method which can be exploited to better understand the complex interactions of USW input parameters. In this paper, the lap shear strength (LSS) of USW Al 5754 joints is investigated using an off-the-shelf Branson Ultraweld L20. Firstly, a 33 full factorial parametric study using ANOVA is carried out to examine the effects of three USW input parameters (weld energy, vibration amplitude & clamping pressure) on LSS. Following this, a high-fidelity predictive hybrid GA-ANN model is then trained using the input parameters and the addition of process data recorded during welding (peak power).

Read more at ScienceDirect

Scientists Set to Use Social Media AI Technology to Optimize Parts for 3D Printing

Date:

Author: Kubi Sertoglu

Topics: 3D Printing, additive manufacturing, AI, genetic algorithm

Organizations: Department of Energy, Argonne National Laboratory

“My idea was that a material’s structure is no different than a 3D image,” he explains. ​“It makes sense that the 3D version of this neural network will do a good job of recognizing the structure’s properties — just like a neural network learns that an image is a cat or something else.”

To see if his idea would work, Messner designed a defined 3D geometry and used conventional physics-based simulations to create a set of two million data points. Each of the data points linked his geometry to ‘desired’ values of density and stiffness. Then, he fed the data points into a neural network and trained it to look for the desired properties.

Finally, Messner used a genetic algorithm – an iterative, optimization-based class of AI – together with the trained neural network to determine the structure that would result in the properties he sought. Impressively, his AI approach found the correct structure 2,760x faster than the conventional physics simulation.

Read more at 3D Printing Industry