EP-4742119-A1 - OPTIMIZED PRODUCT MATERIAL SELECTION FOR CIRCULAR EFFICIENCY

EP4742119A1EP 4742119 A1EP4742119 A1EP 4742119A1EP-4742119-A1

Abstract

A materials selection optimization system (MSOS) (102), a computer implemented method (300) and a computer program product for selecting materials associated with a product deployable in an industrial environment, are provided. The MSOS generates obtains product data and a plurality of optimization objectives associated with the product, identifies from a materials database, material alternatives to the materials used in the product based on one or more material parameters, iteratively determines, using a multi-objective genetic algorithm, from the material alternatives optimal material combinations until each of the optimization objectives are met, and renders the optimal material combinations associated with the product, wherein each combination is associated with a design scenario associated with the product.

Inventors

PILLAI, Unnikrishna
PRADEEP CHANDRAN, Rohith Krishnan

Assignees

Siemens Aktiengesellschaft

Dates

Publication Date: 20260513
Application Date: 20241111

Claims (10)

A computer implemented method (300) for selecting materials associated with a product deployable in an industrial environment, the computer implemented method characterized by : - obtaining (301), by a processor (201) of a materials selection optimization system (102), product data and a plurality of optimization objectives associated with the product; - identifying (303), by the processor (201) of the materials selection optimization system (102), from a materials database, material alternatives to the materials used in the product based on one or more material parameters; - iteratively determining (304) from the material alternatives, by the processor (201) of the materials selection optimization system (102), a plurality of optimal material combinations until each of the optimization objectives are met; and - rendering (305), by the processor (201) of the materials selection optimization system (102) on a graphical user interface of the materials selection optimization system (102), the optimal material combinations associated with the product, wherein each combination is associated with a design scenario associated with the product.
The computer implemented method (300) according to the claim 1, wherein the optimization objectives comprise at least a technical objective, an economical objective, and an environmental objective associated with the product.
The computer implemented method (300) according to the claim 1, wherein the material parameters comprise one or more of material suppliers, material lifespan, material efficiency, material process, material end of life, and material circularity.
The computer implemented method (300) according to the claim 1, wherein iteratively determining the plurality of optimal material combinations comprises employing a multi-objective genetic algorithm.
The computer implemented method (300) according to any one of the claims 1 and 4, wherein iteratively determining the plurality of optimal material combinations comprises performing: - defining (304A) a search space based on the optimization objectives; - generating (304B) a set of possible material combinations in the defined search space; - evaluating (304C) each of the material combinations based on the optimization objectives; - sorting (304D) the material combinations based on the optimization objectives; - identifying (304E) a reference point for the sorted material combinations based on the optimization objectives; - modifying (304F) the sorted material combinations by employing the multi-objective genetic algorithm; and - iteratively selecting (304G) an optimal material combination from the sorted material combinations.
The computer implemented method (300) according to claim 5, wherein the steps of generating a set of possible material combinations; evaluating the optimization objectives for each of the material combinations, sorting the material combinations, identifying the reference point; modifying the sorted material combinations; and selecting the optimal material combination are performed iteratively until a predefined convergence criterion is met.
The computer implemented method (300) according to any one of the claims 5 and 6, wherein modifying (304F) the sorted material combinations by employing the multi-objective genetic algorithm comprises performing: - determining from the sorted material combinations, based on the reference point, two or more parent combinations; - generating an offspring combination by combining the two or more parent combinations; and - mutating the offspring combination based on the product data.
The computer implemented method (300) according to the claim 1, wherein the optimal material combinations associated with the product are stored in the materials database.
A materials selection optimization system (102) for selecting materials associated with a product deployable in an industrial environment, characterized by : - a non-transitory computer readable storage medium storing computer program instructions defined by the materials selection optimization system (102); - at least one processor (201) communicatively coupled to the non-transitory computer readable storage medium, wherein the at least one processor (201) is configured to execute the computer program instructions, thereby performing the method according to the claims 1 to 8.
A computer-program product having machine-readable instructions stored therein, which when executed by one or more processors (201), cause the processors (201) to perform the method according to the claims 1 to 8.

Description

The present disclosure relates to a system and a method for automated selection of materials. More particularly, the present disclosure relates to optimization of material selection process during design of a product using multi-objective genetic algorithm for increasing sustainability of the product. Ecodesign of a product considers environmental aspects at each stage of the product development process, thereby resulting in products which have lowest possible environmental impact throughout the product life cycle. Conventionally material selection phase in ecodesign of a product is based on a combination of expert knowledge, simplified lifecycle assessments, and basic decision-making tools including checklists, matrices, or basic software tools that score materials based on various criteria, including their environmental impact. However, these traditional methods have limitations as they often require substantial manual input and expert analysis, which can be time-consuming and prone to biases or oversights. Moreover, the rapid evolution of materials science, coupled with increasing regulatory pressures and market demands for sustainability, has made the material selection process even more complex especially amidst evolving sustainability regulations, standards and circularity performances. Traditionally, product designers are known to select materials for a product based on multitude of factors, majority of which are associated with technical performance of the material and overall cost effectiveness. However, the evolving sustainable product regulations such as the Ecodesign directive rolled out by European Commission and other such initiatives across the globe towards adoption of circular economy principles demand industries to factor in environmental aspects into various stages of their product design and manufacturing. This complicates the decision making of product designers as they need to consider aspects like lifecycle assessment impacts, resource depletion, recyclability, etc. Moreover, the product designers face difficulties adhering to the aforementioned directives due to trade-offs between material properties, cost and environmental impacts. In such complex design and manufacturing scenario, manual selection approach of product designers based on their experience and expertise becomes increasingly time-consuming and leads to suboptimal choices being made. Furthermore, there is a dearth of optimization models that can provide best possible circularity performance and practices. Accordingly, it is an object of the present disclosure to address the technical challenge in developing an automated way of material selection which considers circularity performance and environmental aspects along with other factors to meet the directives. Moreover, there is a need for tools that can handle a multitude of variables in an integrated, data-driven and automated approach to provide a robust, multi-dimensional analysis that aligns with principles of the circular economy whilst performing material selection. The present disclosure achieves the aforementioned object by providing a materials selection optimization system and a computer implemented method for selecting materials associated with a product deployable in an industrial environment by employing a multi-objective genetic algorithm that ensures scalable and optimized selection of materials whilst minimizing the time and resources required in the material selection process. As used herein, the term industrial environment refers to any environment that has industrial automation products deployable therein. For example, factories, buildings, etc., having industrial automation products such as circuit breakers, programmable logic controllers, etc., deployed therein. According to one aspect of the present disclosure, the materials selection optimization system for selecting materials associated with a product deployable in an industrial environment disclosed herein comprises a non-transitory computer readable storage medium storing computer program instructions defined by module(s) of the materials selection optimization system, and at least one processor communicatively coupled to the non-transitory computer readable storage medium, wherein the at least one processor is configured to execute the computer program instructions, thereby performing the computer implemented method for selecting materials required in product design and/or manufacturing in an industrial environment. As used herein, "non-transitory computer readable storage medium" refers to all computer readable media, for example, non-volatile media, volatile media, and transmission media except for a transitory, propagating signal. According to an embodiment, the materials selection optimization system is installable on accessible by a user of the materials selection optimization system via his/her user device, for example, a personal computing device, a workstation, a client device, a network