CN-121986307-A - Transporting microparticles to a target location using 4-dimensional (4D) objects

Abstract

According to one method, a computer-implemented method includes sending one or more instructions to apply an initial influencing factor to smart material of a 4D object. Further, the 4D object is configured to deliver one or more microparticles from the starting location to the target location along the delivery path in response to the initial influencing factor being applied to the smart material. One or more instructions are also sent to monitor movement of the 4D object along the delivery path in response to applying the initial influencing factor to the smart material. In response to determining that the 4D object has deviated from the delivery path, one or more instructions are further sent to dynamically weight initial influencing factors applied to the smart material using one or more machine learning models.

Inventors

• LIU SU
• T. Agraval
• V. Vallecha
• S. Lacset

Assignees

• 国际商业机器公司

Dates

Publication Date

20260505

Application Date

20240731

Priority Date

20230828

Claims (20)

1. 1. A computer-implemented method, comprising: transmitting one or more instructions to apply an initial influencing factor to smart material of a 4-dimensional 4D object, wherein the 4D object is configured to deliver one or more microparticles from a starting location to a target location along a delivery path in response to the initial influencing factor being applied to the smart material; transmitting one or more instructions to monitor movement of the 4D object along the delivery path in response to applying the initial influencing factor to the smart material, and In response to determining that the 4D object has deviated from the delivery path, one or more instructions are sent to dynamically weight the initial influencing factors applied to the smart material using one or more machine learning models.
2. 2. The computer-implemented method of claim 1, wherein sending one or more instructions to dynamically weight the initial influencing factors using the one or more machine learning models comprises: Determining an amount of force generated by the 4D object in response to the initial influencing factor being applied to the smart material; Comparing the amount of force generated by the 4D object with movement of the 4D object along the delivery path in response to application of the initial influencing factor to the smart material, and A weight value is generated, the weight value configured to adjust movement of the 4D object back along the delivery path in response to applying the weight value to the initial influencing factor.
3. 3. The computer-implemented method of claim 1, wherein the 4D object comprises the smart material and a static material, wherein the smart material is configured to physically deform in response to the initial influencing factor being applied thereto.
4. 4. The computer-implemented method of claim 3, wherein the smart material is configured to generate a force capable of physically moving the 4D object as a result of the physical deformation.
5. 5. The computer-implemented method of claim 3, wherein the one or more machine learning models are trained using feature data corresponding to different influencing factors and a repository of how they influence physical deformations of different smart materials.
6. 6. The computer-implemented method of claim 5, wherein the repository includes characteristic data corresponding to different ambient environments and how they affect physical deformation of the respective smart materials in the repository.
7. 7. The computer-implemented method of claim 1, wherein the influencing factor is selected from the group consisting of light, heat, magnetic field, sound, and electricity.
8. 8. The computer-implemented method of claim 1, further comprising: In response to determining that the 4D object has not deviated from the delivery path, sending one or more instructions to maintain the initial influencing factors applied to the smart material, and In response to determining that the 4D object has reached the target location, one or more instructions are sent to remove the initial influencing factor from being applied to the smart material.
9. 9. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being readable by a processor, executable by the processor, or both, to cause the processor to: transmitting one or more instructions to apply an initial influencing factor to smart material of a 4-dimensional 4D object, wherein the 4D object is configured to deliver one or more microparticles from a starting location to a target location along a delivery path in response to the initial influencing factor being applied to the smart material; transmitting one or more instructions to monitor movement of the 4D object along the delivery path in response to applying the initial influencing factor to the smart material, and In response to determining that the 4D object has deviated from the delivery path, one or more instructions are sent to dynamically weight the initial influencing factors applied to the smart material using one or more machine learning models.
10. 10. The computer program product of claim 9, wherein sending one or more instructions to dynamically weight the initial influencing factors using the one or more machine learning models comprises: Determining an amount of force generated by the 4D object in response to the initial influencing factor being applied to the smart material; Comparing the amount of force generated by the 4D object with movement of the 4D object along the delivery path in response to application of the initial influencing factor to the smart material, and A weight value is generated, the weight value configured to adjust movement of the 4D object back along the delivery path in response to applying the weight value to the initial influencing factor.
11. 11. The computer program product of claim 9, wherein the 4D object comprises the smart material and a static material, wherein the smart material is configured to physically deform in response to the initial influencing factor being applied thereto.
12. 12. The computer program product of claim 11, wherein the smart material is configured to generate a force capable of physically moving the 4D object as a result of the physical deformation.
13. 13. The computer program product of claim 11, wherein the one or more machine learning models are trained using feature data corresponding to different influencing factors and a repository of how they influence physical deformations of different smart materials.
14. 14. The computer program product according to claim 13, wherein the repository comprises characteristic data corresponding to different surrounding environments and how they affect the physical deformation of the respective smart materials in the repository.
15. 15. The computer program product of claim 9, wherein the influencing factor is selected from the group consisting of light, heat, a magnetic field, sound, and electricity.
16. 16. The computer program product of claim 9, wherein the program instructions are readable and/or executable by the processor to cause the processor to: In response to determining that the 4D object has not deviated from the delivery path, sending one or more instructions to maintain the initial influencing factors applied to the smart material, and In response to determining that the 4D object has reached the target location, one or more instructions are sent to remove the initial influencing factor from being applied to the smart material.
17. 17. A system, comprising: processor, and Logic integrated with, executable by, or integrated with and executable by the processor, the logic configured to: transmitting one or more instructions to apply an initial influencing factor to smart material of a 4-dimensional 4D object, wherein the 4D object is configured to deliver one or more microparticles from a starting location to a target location along a delivery path in response to the initial influencing factor being applied to the smart material; transmitting one or more instructions to monitor movement of the 4D object along the delivery path in response to applying the initial influencing factor to the smart material, and In response to determining that the 4D object has deviated from the delivery path, one or more instructions are sent to dynamically weight the initial influencing factors applied to the smart material using one or more machine learning models.
18. 18. The system of claim 17, wherein sending one or more instructions to dynamically weight the initial influencing factors using the one or more machine learning models comprises: Determining an amount of force generated by the 4D object in response to the initial influencing factor being applied to the smart material; Comparing the amount of force generated by the 4D object with movement of the 4D object along the delivery path in response to application of the initial influencing factor to the smart material, and A weight value is generated, the weight value configured to adjust movement of the 4D object back along the delivery path in response to applying the weight value to the initial influencing factor.
19. 19. The system of claim 17, wherein the 4D object comprises the smart material and a static material, wherein the smart material is configured to physically deform in response to the initial influencing factor being applied thereto.
20. 20. The system of claim 17, wherein the logic is configured to: In response to determining that the 4D object has not deviated from the delivery path, sending one or more instructions to maintain the initial influencing factors applied to the smart material, and In response to determining that the 4D object has reached the target location, one or more instructions are sent to remove the initial influencing factor from being applied to the smart material.

Description

Transporting microparticles to a target location using 4-dimensional (4D) objects Background The present disclosure relates to microparticles, and more particularly, to transporting microparticles to a target location using 4D objects. A "microparticle" is a particle having a physical size (e.g., diameter) of about 1 to 1000 micrometers (μm). Microparticles are traditionally available as raw materials, such as ceramics, glass, polymers and metals. Particulates such as pollen, sand, dust, flour and sugar powder are also naturally encountered in everyday life. While conventional microparticles have been limited to naturally occurring items and small amounts of material, advances in technology have allowed for further miniaturization of functional components in many different technical areas. As a result, particulates have become more advanced and can be used in many instances to achieve desired results. For example, advances in medicine have allowed the development of biodegradable microparticles. These biodegradable microparticles may be capable of use as cellular microcarriers, drug delivery containers, 3-dimensional scaffolds, and the like. In other examples, the microparticles may be used to assemble electronic particles, small mechanical particles, and the like. It follows that particles are particularly useful in many situations involving limited space, but the small size of particles also affects the process how they can be handled. Thus, forming and storing microparticles is a detailed and accurate process. The physical system itself, which is sufficiently accurate to handle the particles, is typically too large to reach the target location of the particles. Disclosure of Invention According to one method, a computer-implemented method includes sending one or more instructions to apply an initial influencing factor to smart material of a 4D object. Further, the 4D object is configured to deliver one or more microparticles from the starting location to the target location along the delivery path in response to the initial influencing factor being applied to the smart material. One or more instructions are also sent to monitor movement of the 4D object along the delivery path in response to applying the initial influencing factor to the smart material. In response to determining that the 4D object has deviated from the delivery path, one or more instructions are further sent to dynamically weight initial influencing factors applied to the smart material using one or more machine learning models. According to another method, a computer program product includes a computer-readable storage medium having program instructions embodied therewith. Furthermore, the program instructions may be readable by a processor, executable by the processor, or both, to cause the processor to perform the foregoing method. According to yet another method, a system includes a processor and logic integrated with, executable by, or integrated with and integratable by the processor. Furthermore, the logic is configured to perform the aforementioned method. Other aspects and implementations of the present disclosure will become apparent from the following detailed description, which, when taken in conjunction with the drawings, illustrate by way of example the principles of the disclosure. Drawings FIG. 1 is a diagram of a computing environment in accordance with a method. Fig. 2 is a representative view of a distributed system according to one approach. Fig. 3A is a flow chart according to one method. FIG. 3B is a flow chart of a sub-process of one of the operations in the method of FIG. 3A according to one method. FIG. 3C is a flow chart of a sub-process of one of the operations in the method of FIG. 3A according to one method. Fig. 4 is a method flow diagram according to one method. Detailed Description The following description is made for the purpose of illustrating the general principles of the present disclosure and is not meant to limit the inventive concepts claimed herein. Furthermore, certain features described herein may be used in combination with other described features in various possible combinations and permutations. Unless otherwise specifically defined herein, all terms are to be given their broadest possible interpretation, including the meaning implied in the specification and the meaning understood by those skilled in the art and/or defined in dictionaries, papers, and the like. It must also be noted that, as used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless otherwise specified. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The