Machine learning (ML) is a branch of artificial intelligence that helps computers analyze large datasets and make informed decisions about their environment. When it comes to materials, businesses can leverage the technology to automate the screening process, simulate the performance of different materials and identify the best option.
ML feeds data through various algorithms that give businesses the necessary insights to optimize everything from employee scheduling to resource allocation. The algorithms—computational models of varying complexities—allow computers to recognize patterns, forecast trends and make objective judgments without human supervision.
“By utilizing machine learning algorithms, industry professionals can analyze vast amounts of data related to material properties, performance and manufacturing processes,” explains Kevin Ameche, president of RealSteel, a Pasadena, Texas-based metalworking shop. “This enables them to make informed decisions about material selection, optimizing the overall manufacturing process.”
Genetic algorithms and simulated annealing are particularly useful for material selection. Genetic algorithms effectively put items through a natural selection process to determine the fittest option. Simulated annealing mimics the physical annealing of metal by performing random stress tests until they reach a desired structure.
Another effective algorithm is particle swarm optimization (PSO), which simulates the behavior of different materials nonlinearly. Rather than putting everything through the same simulation, PSO algorithms use other measurements and conditions to identify each one’s ideal parameters within the various environments.
A 2021 study by Aachen University’s Laboratory for Machine Tool and Production Engineering produced a PSO cutting simulation algorithm that calculated material model parameters within less than 40 iterations considering different process variables. Each one remembers its best solution, making PSO highly efficient when companies select materials for cutting or manufacturing applications.
Machine learning doesn’t just help identify the best choice. It can also be used to analyze each material’s structure via generative models, reinforcement learning and inverse design. “This allows businesses to select materials that meet desired criteria such as strength, durability and cost-effectiveness,” Ameche says.
“(And) machine learning can assist in identifying the optimal cutting parameters and manufacturing techniques for different materials, leading to improved efficiency and quality,” he notes. “Businesses can optimize these parameters through regression, classification, feature selection and dimensionality reduction.”
A company can use a feature selection algorithm to compare hundreds of material compositions and discover the ideal alloy for cutting. A classification algorithm can rank different materials during product development, while the engineering team uses regression models to trade off other qualities such as strength, flexibility and estimated maintenance costs.
All these methods play a similar role in a slightly different format—comparing a material’s properties, such as corrosion and temperature resistance, in different scenarios to help businesses identify the ideal cutting process. ML makes recommendations through organized reports, handling the time-intensive analysis.
“Overall, machine learning enhances the material selection process by providing data-driven insights, enabling businesses to make informed decisions, optimize manufacturing processes and deliver high-quality products,” Ameche asserts.
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