CN113496094A - Method for manufacturing operation tool for electrochemical-based metal micro-component operation - Google Patents

Method for manufacturing operation tool for electrochemical-based metal micro-component operation Download PDF

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CN113496094A
CN113496094A CN202110769514.3A CN202110769514A CN113496094A CN 113496094 A CN113496094 A CN 113496094A CN 202110769514 A CN202110769514 A CN 202110769514A CN 113496094 A CN113496094 A CN 113496094A
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CN113496094B (en
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李东洁
孙亮
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Harbin University of Science and Technology
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Abstract

A method for manufacturing an operation tool for electrochemical-based metal micro-component operation. The problem that an operation tool is too large and operation efficiency is low is solved. The invention comprises the following steps: the minimum nozzle opening size required is calculated based on the micro-wire operation that can be performed by electrochemical deposition under ideal conditions. Through experiments, the influence of current, tension, drawing speed and cooling time on the pipette nozzle during drawing is determined. And then fitting a mapping relation between the influence factors and the nozzle diameter based on the BP neural network, and optimizing each parameter by using the determined pipette nozzle diameter as a target value and combining a genetic algorithm, thereby finding out the most suitable drawing parameter according to the determined pipette nozzle diameter. And drawing the glass tube according to the determined drawing parameters. After the drawing is completed, the pipette is tempered with a polisher. The invention is used for manufacturing an operation tool for an electrochemical-based metal micro-component operation.

Description

Method for manufacturing operation tool for electrochemical-based metal micro-component operation
The technical field is as follows:
the invention relates to a method for manufacturing an operation tool for metal micro-component operation based on electrochemistry.
Background art:
in recent years, the rapid development of microelectronic processes and traditional ultra-precision machining methods enables the micro-nano manufacturing technology to be developed dramatically, and the micro-nano manufacturing technology becomes a mark for measuring the manufacturing level of one country. Micro-nano manufacturing comprises micro-manufacturing and nano-manufacturing. Micro-manipulation is one of the key technologies in micro-fabrication, and plays an increasingly important role in the fabrication and controlled production of micron-scale functional device products.
The micro-operation tool is the basis of the development of micro-operation technology, and is a component of the micro-operation robot, the tail end of which directly acts with an operation object. The micromanipulation tool is generally composed of a driver and an actuator.
Conventional micro-manipulation tools are too bulky for micro-components and are inefficient to operate. It is therefore necessary to design a tool-pipette with dimensions smaller than the micromanipulation target.
The invention content is as follows:
the invention relates to a research of a manufacturing method of an operation tool based on electrochemical metal micro-component operation design.
The above purpose is achieved by the following scheme:
a method of making a working tool for electrochemical-based metal micro-component operations, the method comprising the steps of:
step one, calculating the required minimum nozzle opening size r according to the micro metal wire pickup through electrochemical deposition under ideal conditionsN
The minimum nozzle opening size rNIs expressed as
Figure BDA0003152326080000011
In the formula (1), AH is Hamaker constant, and AH is 1 × 10-19J;
R is the radius of the micro metal wire and the unit is m;
l is the length of the micro metal wire and the unit is m;
d is the distance between the micro metal wire and the substrate, and the unit is m;
δeis the tensile strength of the glass tube, and the unit is MPa;
determining the influence of current, tension, drawing speed and cooling time on the nozzle during drawing through experiments;
thirdly, fitting a mapping relation between the influence factors and the nozzle diameter based on a BP neural network, and optimizing each parameter by using the determined pipette nozzle diameter as a target value and combining a genetic algorithm, thereby finding out the most suitable drawing parameter according to the determined pipette nozzle diameter;
step four, drawing the glass tube according to the determined drawing parameters;
and fifthly, tempering the pipette tip to improve the strength of the pipette tip.
As a further explanation of the method for manufacturing an electrochemical-based metal micro-component-operation-oriented operating tool according to the present invention, the current in the second step is a value of a current flowing through the metal sheet; the tensile force refers to the tensile force generated on the glass after the glass is softened; draw speed refers to the separation speed of the draw rod when the glass is first melted; the cooling time refers to the time interval during which the heating sheet stops heating until the tension and speed are applied to both ends of the glass tube.
As a further explanation of the method for manufacturing an electrochemical-based metal microstructure-operating tool according to the present invention, the current value during drawing in the second step is ensured to reach the softening point of the glass tube.
As a further description of the manufacturing method of the electrochemical-based metal micro-component operation-oriented operation tool, the population scale set in the genetic algorithm in the third step is 20, the evolution times are 100, the crossover probability is 0.4, the variation probability is 0.2, floating point number coding is adopted, and the individual length is 2.
As a further explanation of the method for manufacturing the electrochemical-based metal micro-component operation-oriented operation tool according to the present invention, the glass tube used in the fourth step is a borosilicate glass tube.
As a further explanation of the manufacturing method of the electrochemical-based metal micro-component operation-oriented operating tool of the present invention, the glass tube used in the fourth step is BF100-50-10, and has an outer diameter of 1mm, an inner diameter of 0.5mm, and a length of 10 cm.
The principle of the invention is as follows:
the invention firstly calculates the diameter of the pipette nozzle, then carries out finite element simulation on the drawing process, and analyzes the change of temperature and stress on the glass tube during drawing. The influence of the factors of current, drawing force, drawing speed and heat transfer time on the nozzle was analyzed in combination with the experiment. And (4) searching an extremum of a function based on a neural network genetic algorithm, and finding out the most suitable drawing parameter by taking the determined pipette nozzle diameter as a target value.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the micro-operation tool designed by the invention has the size smaller than that of the metal micro-component, takes the micro-metal wire as a pickup object, and operates a large object through the small tool. The micro-operation which is successful once by using the operation only needs a few minutes, and the operation efficiency is high. Compared with the traditional manufacturing method of the operating tool, the method has the advantages of simple operation, high success rate and easy achievement of ideal effect.
The specific implementation mode is as follows:
in one embodiment, a method for manufacturing an operation tool for an electrochemical-based metal micro-component operation includes the following steps:
step one, calculating the required minimum nozzle opening size r according to the micro metal wire pickup through electrochemical deposition under ideal conditionsN
The minimum nozzle opening size rNIs expressed as
Figure BDA0003152326080000031
In the formula (1), AH is Hamaker constant, and AH is 1 × 10-19J;
R is the radius of the micro metal wire, the unit is m, and the value R is 2 multiplied by 10-5
l is the length of the micro metal wire, the unit is m, and the value is 4 multiplied by 10-4
d isThe distance between the micro metal wire and the substrate is m, and the value is 2 multiplied by 10-10
Determining the influence of current, tension, drawing speed and cooling time on the nozzle during drawing through experiments;
the greater the current passed through the sheet during drawing, the greater the length of the resulting pipette tip. The greater the pulling force applied across the glass tube, the smaller the resulting pipette tip diameter. The greater the drawing speed applied across the glass tube, the smaller the resulting pipette tip diameter.
The longer the time interval between the heating blade stopping heating and the application of tension and speed to the two ends of the glass tube, the smaller the resulting pipette tip length.
And step three, the factors which have great influence on the diameter of the pipette nozzle mainly comprise two factors of pulling force and speed of drawing. These two factors are therefore selected here and a mapping of drawing tension, drawing speed and nozzle diameter is fitted on the basis of the BP neural network. And the determined pipette nozzle diameter is used as a target value, and each parameter is optimized by combining a genetic algorithm, so that the most suitable pulling force and drawing speed are found according to the determined pipette nozzle diameter.
Selecting two influencing factors of the drawing tension and the drawing speed as input values of the neural network model, and selecting the diameter of the pipette nozzle as an output value of the neural network model. Because the linear function has two input parameters and one output parameter, the BP neural network structure is 2-5-1, namely the input layer has 2 nodes, the hidden layer has 5 nodes, and the output layer has 1 node.
After the training of the BP neural network is finished, a genetic algorithm is used for searching a pulling force and a speed value corresponding to the required pipette nozzle diameter. And taking the error between the predicted output value and the actual measurement value of the trained BP neural network as an individual fitness value. The population scale set in the genetic algorithm is 20, the evolution times are 100, the cross probability is 0.4, the mutation probability is 0.2, floating point number coding is adopted, and the individual length is 2.
And step four, drawing the glass tube according to the determined drawing parameters. And drawing the glass tube by using a needle drawing instrument p-97 according to the result of parameter optimization.
And fifthly, tempering the pipette tip to improve the strength of the pipette tip.
After the drawing is completed, the pipette is tempered with a pin forging gauge mf-900. The needle forging instrument is used for exposing the pipette to radiant heat generated by a heater so that the pipette is bent under the surface tension of a glass capillary tube. The compression strength, the tensile strength and the shear strength of the weakest nozzle are improved by forging treatment, and the strength of the pipe is more than 4 times of that of common glass. The glass pipette thus produced should have a compressive strength greater than 800Mpa, a tensile strength greater than 280Mpa and a shear strength greater than 280 Mpa. The forged pipette provides sufficient strength to ensure that the tip is neither broken nor sheared during the picking of the micro-component.
In a second embodiment, which is a further description of the method for manufacturing an operation tool for electrochemical-based metal micro-component operation according to the first embodiment, the current in the second step is a value of a current flowing through a metal piece; the tensile force refers to the tensile force generated on the glass after the glass is softened; draw speed refers to the separation speed of the draw rod when the glass is first melted; the cooling time refers to the time interval during which the heating sheet stops heating until the tension and speed are applied to both ends of the glass tube.
In a third embodiment, the present embodiment is a further description of the method for manufacturing an operation tool for electrochemical-based metal micro-component operation according to the first embodiment, wherein the glass tube used is a borosilicate glass tube.
In a fourth embodiment, which is a further description of the method of manufacturing the electrochemical-based metal micro-component manipulation-oriented manipulation tool according to the first embodiment, the glass tube used is BF100-50-10, and has an outer diameter of 1mm, an inner diameter of 0.5mm, and a length of 10 cm.

Claims (5)

1. A method of making a working tool for electrochemical-based metal micro-component operations, the method comprising the steps of:
step one, calculating the required minimum nozzle opening size r according to the operation of the micro metal wire by electrochemical deposition under ideal conditionsN
The minimum nozzle opening size rNIs expressed as
Figure FDA0003152326070000011
In the formula (1), AHIs Hamaker constant, AH=1×10-19J;
R is the radius of the micro metal wire and the unit is m;
l is the length of the micro metal wire and the unit is m;
d is the distance between the micro metal wire and the substrate, and the unit is m;
δeis the tensile strength of the glass tube, and the unit is MPa;
determining the influence of current, tension, drawing speed and cooling time on the pipette tip during drawing through experiments;
thirdly, fitting a mapping relation between the influence factors of the pipette nozzle and the nozzle diameter based on a BP neural network, and optimizing each parameter by using the determined pipette nozzle diameter as a target value and combining a genetic algorithm, thereby finding out the most suitable drawing parameter according to the determined pipette nozzle diameter;
step four, drawing the glass tube according to the determined drawing parameters;
and fifthly, tempering the pipette tip to improve the strength of the pipette tip.
2. The method of claim 1, wherein the drawing is performed at a current value greater than or equal to a melting point of the glass tube.
3. The method of claim 1, wherein the glass tube used is a borosilicate glass tube.
4. The method of claim 1, wherein a glass tube of type BF100-50-10 is used having an outer diameter of 1mm, an inner diameter of 0.5mm and a length of 10 cm.
5. The method of claim 1, wherein the genetic algorithm is set to have a population size of 20, an evolutionary number of 100, a crossover probability of 0.4, a mutation probability of 0.2, floating point number coding, and an individual length of 2.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011141700A (en) * 2010-01-06 2011-07-21 Fujitsu Ltd Component selecting device, cad device, component selecting method, and component selecting program
CN104239646A (en) * 2014-09-23 2014-12-24 工业和信息化部电子第五研究所 Method and system for verifying forecast simulation model of vibration fatigue life of micro assembly component
CN104549592A (en) * 2013-10-25 2015-04-29 大连民族学院 Pipette
CN105302985A (en) * 2015-11-12 2016-02-03 哈尔滨工业大学 Alloy micro-cast forming process simulation method based on fluent software
CN108108509A (en) * 2017-10-26 2018-06-01 哈尔滨理工大学 The reliable lossless operation of micro structures based on electrochemistry
CN109196331A (en) * 2016-04-15 2019-01-11 贝克顿·迪金森公司 Closed droplet sorter and its application method
CN109508488A (en) * 2018-11-07 2019-03-22 西北工业大学 Contour peening technological parameter prediction technique based on genetic algorithm optimization BP neural network
CN110315464A (en) * 2019-08-06 2019-10-11 哈尔滨理工大学 A kind of metal micro member pick-up method based on electrochemical deposition
CN110408978A (en) * 2019-08-06 2019-11-05 哈尔滨理工大学 A kind of metal micro member interconnected method based on electrochemical deposition

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011141700A (en) * 2010-01-06 2011-07-21 Fujitsu Ltd Component selecting device, cad device, component selecting method, and component selecting program
CN104549592A (en) * 2013-10-25 2015-04-29 大连民族学院 Pipette
CN104239646A (en) * 2014-09-23 2014-12-24 工业和信息化部电子第五研究所 Method and system for verifying forecast simulation model of vibration fatigue life of micro assembly component
CN105302985A (en) * 2015-11-12 2016-02-03 哈尔滨工业大学 Alloy micro-cast forming process simulation method based on fluent software
CN109196331A (en) * 2016-04-15 2019-01-11 贝克顿·迪金森公司 Closed droplet sorter and its application method
CN108108509A (en) * 2017-10-26 2018-06-01 哈尔滨理工大学 The reliable lossless operation of micro structures based on electrochemistry
CN109508488A (en) * 2018-11-07 2019-03-22 西北工业大学 Contour peening technological parameter prediction technique based on genetic algorithm optimization BP neural network
CN110315464A (en) * 2019-08-06 2019-10-11 哈尔滨理工大学 A kind of metal micro member pick-up method based on electrochemical deposition
CN110408978A (en) * 2019-08-06 2019-11-05 哈尔滨理工大学 A kind of metal micro member interconnected method based on electrochemical deposition

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
DONGJIE LI等: "Simulation of metal microcomponents picking up by electrochemical based on ABAQUS", 《MODERN PHYSICS LETTERS B》 *
DONGJIE LI等: "Simulation of Picking Up Metal Microcomponents Based on Electrochemistry", 《MICROMACHINES》 *
EFFAT DEHGHANIAN等: "Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction", 《MONATSHEFTE FUR CHEMIE-CHEMICAL MONTHLY》 *
刘赛赛等: "电沉积增材制造微镍柱的工艺研究", 《表面技术》 *
王小锋等: "考虑固液范德华力作用的微圆管流动数学模型", 《东北石油大学学报》 *
胡俊峰等: "基于Kriging模型的微夹持器优化设计", 《中国机械工程》 *
郭黎滨等: "《先进制造技术》", 31 January 2010, 哈尔滨:哈尔滨工程大学出版社 *

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