CN104005074A - Magnesium-based material biological composite coating controllable-degradation rate control method - Google Patents
Magnesium-based material biological composite coating controllable-degradation rate control method Download PDFInfo
- Publication number
- CN104005074A CN104005074A CN201410190182.3A CN201410190182A CN104005074A CN 104005074 A CN104005074 A CN 104005074A CN 201410190182 A CN201410190182 A CN 201410190182A CN 104005074 A CN104005074 A CN 104005074A
- Authority
- CN
- China
- Prior art keywords
- coating
- arc oxidation
- differential arc
- degradation rate
- biological composite
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Abstract
The invention discloses a magnesium-based material biological composite coating controllable-degradation rate control method. The method realizes preparation of composite coatings having different degradation rates by change of micro-arc oxidation technological parameters, and realizes network training by the technological parameter and degradation rate respectively as an input layer and an output layer and by a BP artificial neural network optimized by a genetic algorithm thereby acquiring a prediction model having a small error.
Description
(1) technical field
The present invention relates to a kind of controlled Forecasting Methodology of degradation rate of differential arc oxidation biological composite coating, specifically realize the measurable of coating degradation rate by the BP artificial neural network that adopts genetic algorithm optimization.
(2) background technology
From the nineties in 20th century, the study hotspot that body is implanted into bio-medical material starts to change degradation material into by Biostatic shaped material, and it is a kind of revolutionary metallic biomaterial that degradable magnesium alloy has been described as America and Europe.Magnesium alloy has tempting application prospect as biological absorbable orthopedic implant material.But bone implanting part need maintain in vivo the time of 12 to 18 weeks and keep enough intensity conventionally, until damaged tissue recovery from illness.Magnesium alloy embedded material contains degraded rapidly in the corrosive medium of chlorion at human body, makes it to maintain time enough and loses efficacy in advance, and its Pitting corrosion behavior also may be induced the inflammatory reaction that local organization is serious; Too high magnesium ion concentration also can cause Delicious peptide (BMP-2) secrete excessive, activate osteoclast, cause molten bone phenomenon.The current technology that is used for improving corrosion stability of magnesium alloy is mainly process for modifying surface, and its effect is remarkable.The embedded material of current clinical needs can be corrosion-resistant in human body environment at the implantation initial stage, keeps enough intensity, and degrade with controlled speed in the process that completes function, and degraded is afterwards by human body is absorbed or metabolism.But the degradation rate of the magnesium alloy embedded material after surface modification is unknowable, how to ensure that magnesium alloy embedded material is not degraded in advance after implant into body, be all a difficult problem all the time.Therefore, must realize predicting magnesium alloy embedded material degradation rate.But the efficiency that obtains related data by numerous and diverse experiment is very low, is unfavorable for the formation of industrialization.In recent years, the forecasting techniques that appears as with the neural network of nonlinearity and very strong adaptive learning ability is brought new thought, and neural network was used in prediction work and had obtained certain effect in recent years.So, by conjunction with Magnesiumalloy surface modifying technology and neural network, thereby obtain a kind of degradation rate Forecasting Methodology of surface modification mg-based material, there is landmark significance for the application and development of bio-medical metal implantation instrument.
(3) summary of the invention
The present invention will overcome the not good shortcoming of controlled degradation of the mg-based material surface biological compound coating of prior art, and a kind of controllable degradation rate control method of mg-based material biological composite coating is provided.
The present invention carrys out matching lot of experimental data with BP artificial neural network, obtains a reliable degradation rate predictive model.Can reduce like this by numerous and diverse experiment and reach to obtain the data of degradation rate, be beneficial to industrialization.
The technical solution used in the present invention is:
(1) utilize micro-arc oxidation and electrophoretic deposition to prepare the differential arc oxidation biological composite coating of controlled degradation at Mg alloy surface;
(2) preparation of differential arc oxidation coating: select taking water glass, Sodium Fluoride and sodium hydroxide as main electrolyte component, under the micro-arc oxidation process parameter of setting, prepare differential arc oxidation coating at Mg alloy surface;
(3) preparation of differential arc oxidation biological composite coating: taking differential arc oxidation coating sample as the pending sample of electrophoretic deposition, taking sodium phosphate and hydroxyapatite (HA) in the suspension of main ingredient, set suitable electrophoretic deposition electrical parameter, differential arc oxidation coating is carried out to electrophoretic deposition and prepare differential arc oxidation biological composite coating;
(4) described differential arc oxidation biological composite coating main body is differential arc oxidation coating, and differential arc oxidation coating is also compound coating rot-resistant main part; Electrophoretic deposition coating is mainly to improve the biology performance of differential arc oxidation coating;
(5) in view of described in (4), be the antiseptic power of capable of regulating compound coating by changing differential arc oxidation related process parameter, therefore different processing parameters can be set and prepare the different coating of degradation rate, reach controlled effect;
(6) affecting topmost three factors of differential arc oxidation coating degradation rate is: current density, treatment time and concentration of electrolyte.By changing this three parameters, can prepare the different differential arc oxidation coating of degradation rate, on differential arc oxidation coating, carry out electrophoretic deposition and prepare biological composite coating.The compound coating of preparing is carried out to the external immersion test of SBF solution, obtain the degradation rate parameter of coating;
(7) select the neural net model establishing through the improved 3-tier architecture BP of genetic algorithm, input layer is three neurones, is respectively current density, treatment time and three factors of concentration of electrolyte, and output layer is a neurone, the i.e. degradation rate of coating; The number of hidden nodes is determined according to simulation training and test result;
The whole process of described genetic algorithm:
71, first produce initial population, and colony is encoded;
72, colony is carried out to fitness analysis, directly export optimum individual and parameter if meet optimization principles, and finish; Otherwise continue next step;
73, select individuality according to fitness, and retain the individuality of high fitness;
74, crossover operator acts on whole individuality, produces a new generation;
75, use mutation operator to carry out structure variation adjustment to the individuality in colony, form new individual;
76, the individuality after selection, intersection, mutation process becomes colony of future generation, repeats 2.
(8) using N (N >=50) group input layer experimental data and corresponding output layer experimental data as learning sample, the antigradient direction changing along network error by the weights to network and threshold value regulates, finally make network error reach mnm. or minimum value, thereby obtain a BP artificial neural network control model that specific inaccuracy is minimum.
Principle of the present invention:
The differential arc oxidation biological composite coating of controlled degradation is prepared based on differential arc oxidation coating, by adjusting the processing parameter of three major effect coating degradation rates of differential arc oxidation, can obtain the differential arc oxidation coating under different parameters, the electrophoretic deposition HA carrying out under the same terms on these differential arc oxidation coating surfaces obtains biological composite coating, compound coating is carried out to the external immersion test of SBF solution, thereby obtain the degradation rate of respective coatings.Using differential arc oxidation corresponding this degradation rate three parameters as one group of training data, degradation rate is as network output layer, differential arc oxidation three parameters, as input layer, are carried out BP artificial neural network training and testing, obtain a BP Artificial Neural Network Prediction Model that error is minimum.Once this model is set up, in conjunction with differential arc oxidation system, as long as input three processing parameters of differential arc oxidation at the input layer of model, can obtain the degradation rate of the differential arc oxidation coating of preparing under this parameter.
Beneficial effect major embodiment of the present invention: use enough experimental datas to train the BP artificial nerve network model after improving by genetic algorithm, obtain a control model that error is minimum; The degradation rate that can control the coating of preparing under any one group of differential arc oxidation three parameter by this model, has reduced follow-up numerous and diverse experimental exploring, for the industrialization of bone implant material is laid a good foundation.
(4) brief description of the drawings
Fig. 1 genetic algorithm optimization BP artificial neural network schematic diagram;
Fig. 2 BP artificial neural network schematic diagram.
(5) embodiment
Below in conjunction with accompanying drawing, the present invention is described further.
The technical solution used in the present invention is:
The controllable degradation rate control method of mg-based material biological composite coating, comprises the steps:
(1) utilize micro-arc oxidation and electrophoretic deposition to prepare the differential arc oxidation biological composite coating of controlled degradation at Mg alloy surface;
(2) preparation of differential arc oxidation coating: select taking water glass, Sodium Fluoride and sodium hydroxide as main electrolyte component, under the micro-arc oxidation process parameter of setting, prepare differential arc oxidation coating at Mg alloy surface;
(3) preparation of differential arc oxidation biological composite coating: taking differential arc oxidation coating sample as the pending sample of electrophoretic deposition, taking sodium phosphate and hydroxyapatite (HA) in the suspension of main ingredient, set suitable electrophoretic deposition electrical parameter, differential arc oxidation coating is carried out to electrophoretic deposition and prepare differential arc oxidation biological composite coating;
(4) described differential arc oxidation biological composite coating main body is differential arc oxidation coating, and differential arc oxidation coating is also compound coating rot-resistant main part; Electrophoretic deposition coating is mainly to improve the biology performance of differential arc oxidation coating;
(5) in view of described in (4), be the antiseptic power of capable of regulating compound coating by changing differential arc oxidation related process parameter, therefore different processing parameters can be set and prepare the different coating of degradation rate, reach controlled effect;
(6) affecting topmost three factors of differential arc oxidation coating degradation rate is: current density, treatment time and concentration of electrolyte.By changing this three parameters, can prepare the different differential arc oxidation coating of degradation rate, on differential arc oxidation coating, carry out electrophoretic deposition and prepare biological composite coating.The compound coating of preparing is carried out to the external immersion test of SBF solution, obtain the degradation rate parameter of coating;
(7) select the neural net model establishing through the improved 3-tier architecture BP of genetic algorithm, input layer is three neurones, is respectively current density, treatment time and three factors of concentration of electrolyte, and output layer is a neurone, the i.e. degradation rate of coating; The number of hidden nodes is determined according to simulation training and test result;
The whole process of described genetic algorithm:
71, first produce initial population, and colony is encoded;
72, colony is carried out to fitness analysis, directly export optimum individual and parameter if meet optimization principles, and finish; Otherwise continue next step;
73, select individuality according to fitness, and retain the individuality of high fitness;
74, crossover operator acts on whole individuality, produces a new generation;
75, use mutation operator to carry out structure variation adjustment to the individuality in colony, form new individual;
76, the individuality after selection, intersection, mutation process becomes colony of future generation, repeats 2.
(8) using N (N >=50) group input layer experimental data and corresponding output layer experimental data as learning sample, the antigradient direction changing along network error by the weights to network and threshold value regulates, finally make network error reach mnm. or minimum value, thereby obtain a BP artificial neural network control model that specific inaccuracy is minimum.
Utilize micro-arc oxidation and electrophoretic deposition to prepare differential arc oxidation biological composite coating.
Concentration of electrolyte composition: the Na of 1.75A g/L
2siO
310H
2o, the NaF of A g/L, NaOH solution regulates the pH value of electrolytic solution, using deionized water as solvent.
Electrophoretic deposition liquid composition: Na
3pO
410.0g/L, HA nano powder (100nm) 3.0g/L, 3ml/L ethylene glycol, deionized water is as solvent.Use magnetic stirrer 1h, then ageing 6h, using the upper strata anionic turbid solution after ageing as electrophoretic deposition liquid.
(1) in the differential arc oxidation electrolytic solution preparing, carry out differential arc oxidation experiment, adopt DC pulse micro-arc oxidation device, differential arc oxidation electrical parameter is: the differential arc oxidation time is B min, current density CA/dm
2, dutycycle is 37.5%, electrolyte temperature is 20 DEG C.After finishing, processing by sample seasoning, obtains arc differential oxide ceramic coating.Using differential arc oxidation coating sample as the pending sample of electrophoretic deposition, adopt the pulse power, under the condition that is 350V at voltage, electrophoretic deposition is processed 10min.Handle rear seasoning, the sample obtaining is differential arc oxidation biological composite coating sample.
(2) use the differential arc oxidation biological composite coating preparing as sample, in the SBF solution of 37 DEG C, carry out external immersion test.The average degradation rate D within the immersion time limit of testing coating.
SBF solution final concentration consists of (solvent is deionized water)
(3) referring to Fig. 2: the input parameter using concentration of electrolyte A, treatment time B and current density C as input layer, using average degradation rate D as output parameter.Adopt the BP artificial nerve network model through genetic algorithm optimization (Fig. 1) as shown in the figure to carry out network training, by continuous adjustment weights and bias, improve training precision to reduce the error of predictive model.
(4) whole training process, through iteration repeatedly, makes training error reach target error or following.
(5) by using BP Artificial Neural Network Prediction Model, the degradation rate that can reach the differential arc oxidation biological composite coating to preparing under known micro-arc oxidation process parameter is controlled, thereby reaches controlled to coating degradation rate.
Claims (1)
1. the controllable degradation rate control method of mg-based material biological composite coating, comprises the steps:
(1) utilize micro-arc oxidation and electrophoretic deposition to prepare the differential arc oxidation biological composite coating of controlled degradation at Mg alloy surface;
(2) preparation of differential arc oxidation coating: select taking water glass, Sodium Fluoride and sodium hydroxide as main electrolyte component, under the micro-arc oxidation process parameter of setting, prepare differential arc oxidation coating at Mg alloy surface;
(3) preparation of differential arc oxidation biological composite coating: taking differential arc oxidation coating sample as the pending sample of electrophoretic deposition, taking sodium phosphate and hydroxyapatite (HA) in the suspension of main ingredient, set suitable electrophoretic deposition electrical parameter, differential arc oxidation coating is carried out to electrophoretic deposition and prepare differential arc oxidation biological composite coating;
(4) described differential arc oxidation biological composite coating main body is differential arc oxidation coating, and differential arc oxidation coating is also compound coating rot-resistant main part; Electrophoretic deposition coating is mainly to improve the biology performance of differential arc oxidation coating;
(5) in view of described in (4), be the antiseptic power of capable of regulating compound coating by changing differential arc oxidation related process parameter, therefore different processing parameters can be set and prepare the different coating of degradation rate, reach controlled effect;
(6) affecting topmost three factors of differential arc oxidation coating degradation rate is: current density, treatment time and concentration of electrolyte.By changing this three parameters, can prepare the different differential arc oxidation coating of degradation rate, on differential arc oxidation coating, carry out electrophoretic deposition and prepare biological composite coating.The compound coating of preparing is carried out to the external immersion test of SBF solution, obtain the degradation rate parameter of coating;
(7) select the neural net model establishing through the improved 3-tier architecture BP of genetic algorithm, input layer is three neurones, is respectively current density, treatment time and three factors of concentration of electrolyte, and output layer is a neurone, the i.e. degradation rate of coating; The number of hidden nodes is determined according to simulation training and test result;
The whole process of described genetic algorithm:
71, first produce initial population, and colony is encoded;
72, colony is carried out to fitness analysis, directly export optimum individual and parameter if meet optimization principles, and finish; Otherwise continue next step;
73, select individuality according to fitness, and retain the individuality of high fitness;
74, crossover operator acts on whole individuality, produces a new generation;
75, use mutation operator to carry out structure variation adjustment to the individuality in colony, form new individual;
76, the individuality after selection, intersection, mutation process becomes colony of future generation, repeats 2;
(8) using N (N >=50) group input layer experimental data and corresponding output layer experimental data as learning sample, the antigradient direction changing along network error by the weights to network and threshold value regulates, finally make network error reach mnm. or minimum value, thereby obtain a BP artificial neural network control model that specific inaccuracy is minimum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410190182.3A CN104005074B (en) | 2014-05-07 | 2014-05-07 | Magnesium-based material biological composite coating controllable-degradation rate control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410190182.3A CN104005074B (en) | 2014-05-07 | 2014-05-07 | Magnesium-based material biological composite coating controllable-degradation rate control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104005074A true CN104005074A (en) | 2014-08-27 |
| CN104005074B CN104005074B (en) | 2017-01-11 |
Family
ID=51365975
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410190182.3A Active CN104005074B (en) | 2014-05-07 | 2014-05-07 | Magnesium-based material biological composite coating controllable-degradation rate control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104005074B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105862107A (en) * | 2016-05-26 | 2016-08-17 | 浙江工业大学 | Method for preparing composite biological coating on magnesium alloy micro-arc oxidation coating |
| CN111593279A (en) * | 2020-05-26 | 2020-08-28 | 浙江工业大学 | Method for controlling degradation rate of medical magnesium-based material composite biological coating |
| CN113981502A (en) * | 2021-10-29 | 2022-01-28 | 大连海事大学 | Aluminum alloy surface corrosion-resistant antifriction composite coating and preparation method thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6981423B1 (en) * | 2002-04-09 | 2006-01-03 | Rockwell Automation Technologies, Inc. | System and method for sensing torque on a rotating shaft |
| CN101539781A (en) * | 2009-04-22 | 2009-09-23 | 北京中冶设备研究设计总院有限公司 | Electrogalvanizing zinc coating thickness BP neural network control method and application in PLC thereof |
| CN102902257A (en) * | 2012-10-30 | 2013-01-30 | 威水星空（北京）环境技术有限公司 | Sewage treatment process optimization and energy-saving control system and method |
| CN103194782A (en) * | 2013-04-11 | 2013-07-10 | 浙江工业大学 | Method for preparing magnesium-based ceramic coating by micro-arc oxidation-electrophoretic deposition |
-
2014
- 2014-05-07 CN CN201410190182.3A patent/CN104005074B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6981423B1 (en) * | 2002-04-09 | 2006-01-03 | Rockwell Automation Technologies, Inc. | System and method for sensing torque on a rotating shaft |
| CN101539781A (en) * | 2009-04-22 | 2009-09-23 | 北京中冶设备研究设计总院有限公司 | Electrogalvanizing zinc coating thickness BP neural network control method and application in PLC thereof |
| CN102902257A (en) * | 2012-10-30 | 2013-01-30 | 威水星空（北京）环境技术有限公司 | Sewage treatment process optimization and energy-saving control system and method |
| CN103194782A (en) * | 2013-04-11 | 2013-07-10 | 浙江工业大学 | Method for preparing magnesium-based ceramic coating by micro-arc oxidation-electrophoretic deposition |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105862107A (en) * | 2016-05-26 | 2016-08-17 | 浙江工业大学 | Method for preparing composite biological coating on magnesium alloy micro-arc oxidation coating |
| CN111593279A (en) * | 2020-05-26 | 2020-08-28 | 浙江工业大学 | Method for controlling degradation rate of medical magnesium-based material composite biological coating |
| CN113981502A (en) * | 2021-10-29 | 2022-01-28 | 大连海事大学 | Aluminum alloy surface corrosion-resistant antifriction composite coating and preparation method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104005074B (en) | 2017-01-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103751840B (en) | A kind of have bone defect repair support of the controlled low modulus of porous and preparation method thereof | |
| US5413693A (en) | Electrocrystallization of strongly adherent brushite coatings on prosthetic alloys | |
| CN101559239A (en) | Magnesium base degradable implant material and preparation method thereof | |
| CN107119304B (en) | A kind of magnetism TiO2The differential arc oxidation preparation method of bioactivity coatings | |
| CN105274603B (en) | Composite modified coating of magnesium or Mg alloy surface carbon nanotubes and preparation method thereof | |
| JPH10503108A (en) | Method for forming a gradient coating comprising a calcium phosphate phase and a metal oxide phase on a metal implant | |
| CN104005074A (en) | Magnesium-based material biological composite coating controllable-degradation rate control method | |
| CN101302638A (en) | Preparation of nano-HAP coating/magnesium alloy composite biological material | |
| JP2010540779A (en) | Method for producing metal with biocidal properties | |
| CN103908699B (en) | A kind of HA/TiO of titanium alloy surface2Layer and preparation method thereof | |
| CN104726836B (en) | The method of surface modification and the titanium metal material of modification are carried out to titanium metal material | |
| CN109848546A (en) | A kind of titanium or titanium alloy surface micro-nano structure method of modifying | |
| CN106676605B (en) | Preparation method and applications with the porous pure titanium of lattice structure or titanium alloy surface multiporous biological active ceramic film | |
| CN102409382A (en) | Bioactive coating of metal implant and preparation method thereof | |
| CN111058076B (en) | Zr-based high-entropy alloy material and method for synthesizing porous spherical structure on surface of Zr-based high-entropy alloy | |
| CN103334145A (en) | Method for preparing biological activity micro-arc oxidation ceramic coating with macroscopic/microcosmic double-stage pore structure on surface of medical titanium through two-step method | |
| CN104746073A (en) | Magnesium alloy surface modification method | |
| Kaveh et al. | Games of multicellularity | |
| CN101880890A (en) | Electrochemical method for preparing HA/ZrO2 (hydroxylapatite/zirconia) gradient coating on surface of medical titanium | |
| US5458863A (en) | Cold process for hydroxyapatite coatings | |
| CN104694994A (en) | Method for carrying out electrochemical treatment on surface of biomedical magnesium or magnesium alloy with high biological activity and low degradation rate | |
| CN102449206B (en) | Metal treatment to form a surface layer | |
| CN110109356A (en) | The learning-oriented optimized control method and system of process in zinc electrolyzing model-free adaption | |
| CN108624936B (en) | Method for changing surface stress state of magnesium base material and application thereof | |
| CN105862107B (en) | The method that composite biological coating is prepared on magnesium alloy differential arc oxidation coating |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |