CN107723527A - A kind of automotive light weight technology chassis aluminum alloy junction component and preparation method thereof - Google Patents

A kind of automotive light weight technology chassis aluminum alloy junction component and preparation method thereof Download PDF

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Publication number
CN107723527A
CN107723527A CN201710964788.1A CN201710964788A CN107723527A CN 107723527 A CN107723527 A CN 107723527A CN 201710964788 A CN201710964788 A CN 201710964788A CN 107723527 A CN107723527 A CN 107723527A
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value
node
image
component
aluminum alloy
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张鹏
马海英
杨淑贞
其他发明人请求不公开姓名
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Huanghe Jiaotong University
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Huanghe Jiaotong University
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    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22C—ALLOYS
    • C22C21/00—Alloys based on aluminium
    • C22C21/02—Alloys based on aluminium with silicon as the next major constituent
    • C22C21/04—Modified aluminium-silicon alloys
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D17/00—Pressure die casting or injection die casting, i.e. casting in which the metal is forced into a mould under high pressure
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22C—ALLOYS
    • C22C1/00—Making non-ferrous alloys
    • C22C1/02—Making non-ferrous alloys by melting
    • C22C1/026—Alloys based on aluminium
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component

Abstract

The invention belongs to metal manufacturing field, discloses a kind of automotive light weight technology chassis aluminum alloy junction component and preparation method thereof, automotive light weight technology chassis aluminum alloy junction component is composed of the following components：Titanium, lead, manganese, silicon, iron, copper, magnesium, zinc, chromium, nickel, tin, surplus are aluminium.Bubble is few in the automotive light weight technology chassis aluminum alloy junction component of the present invention, satisfactory mechanical property, and intensity is high, it is corrosion-resistant, resistance to elevated temperatures is high, stability is good when being used as auto parts, is worthy to be popularized, improve original thin-walled support class product stress concentration, the product defects that product easily cracks, toughness is strong, and elongation percentage is high, it is much better than the elongation percentage of existing product, the present invention is strictly controlled al alloy component, and Mn and Mg content ratio are individually matched, to improve mechanical performance；Therefore, automotive light weight technology chassis aluminum alloy junction component of the invention has good market prospects.

Description

A kind of automotive light weight technology chassis aluminum alloy junction component and preparation method thereof
Technical field
The invention belongs to metal manufacturing field, more particularly to a kind of automotive light weight technology chassis aluminum alloy junction component and its preparation Method.
Background technology
In auto parts machinery production industry, traditional aluminum alloy junction component exist low intensity, mechanical performance it is not high, it is corrosion-resistant, The defects of resistance to elevated temperatures difference, in automobile making, because connecting base plate to other adjacent components needs to support, so such zero Part requires very high to comprehensive mechanical performance.Under automobile dither, chassis product stress and mechanical performance will reach very high Level, in order to avoid part crack or directly fracture.
The density of fine aluminium is small, corrosion resistance is high, fusing point is low, about 660 DEG C, and has very high plasticity, easy to process. But the low intensity of fine aluminium, in order to improve the scope of application of aluminium, by long-term research, by added in fine aluminium it is some its While can improving intensity after the obtained aluminium alloy of its element, decay resistance, the high-ductility performance of fine aluminium are still kept.And In the case where with the addition of some elements such as titanium, its intensity exceedes many steel alloys, and light specific gravity, is used extensively In machine-building, aircraft industry, automobile and building materials field to substitute iron or ferroalloy, mitigate deadweight and reduction energy consumption to realize.
The intensity of aluminium alloy is high, more than the intensity of many steel alloys, but in casting, following defect also be present, is exactly In casting process, because during high-pressure casting, the structural change in aluminum alloy organization can be caused, and cause aluminium alloy table Phenomena such as face is cracked, this phenomenon result in the performance of product and defect occurs in decay resistance.
In summary, the problem of prior art is present be：Existing automotive light weight technology chassis aluminum alloy junction component low intensity, Mechanical performance is not high, corrosion-resistant, resistance to elevated temperatures is poor, under automobile dither, part crack or directly fracture occurs.
The content of the invention
The problem of existing for prior art, the invention provides a kind of automotive light weight technology chassis aluminum alloy junction component and its Preparation method.
The present invention is achieved in that a kind of group of automotive light weight technology chassis aluminum alloy junction component by following percentage by weight It is grouped into：
Titanium 0.10-0.25%, lead 0.20-0.4%, manganese 0.3-0.7%, silicon 8.0-10.0%, iron 0.5-1.3%, copper 2.0-3.0%, magnesium 0.2-0.60%, zinc 0.5-1.0%, chromium 0.1-0.20%, nickel 0.1-0.50%, tin 0.1-0.30% are remaining Measure as aluminium.
A kind of automotive light weight technology chassis aluminum alloy junction component preparation method comprises the following steps：
Step 1：By raw materials melt and 760 DEG C are warming up to, standing 5 hours.
Step 2：Carry out being warming up to 750 DEG C again and simultaneously reserve certain interval into liquid material, matched moulds, oxygen is injected into gap Gas；Continue matched moulds, mould closes completely.
Step 3：Liquid material is injected into barrel；Oxygen is injected into barrel；Then 0.25% alterant is added, is adopted Degasification is carried out with 99% argon gas to operate 10 minutes, prepares die casting within static 13 minutes.
Step 4：Mould is fixed on the active and inactive mold plate of die casting machine, mould is preheated to 200 DEG C, in mold cavity Even spray last layer water based paint, painting type thickness is 0.005~0.007mm, and it is 160 DEG C to heat and keep mold temperature.
Step 5：Will handle complete mixed material liquid press-in die in, injection take out part after, carry out 180 DEG C when Effect processing, required automotive light weight technology chassis aluminum alloy junction component semi-finished product are made by processing in 10 hours.
Step 6：By to semi-finished product cooling after carry out trimming, X flaw detections, ball blast, processing ECM deburrings, the whole cleaning of progress Drying and final inspection, obtain product.
Every kind of component is separately stored in different intelligent blanking devices in raw material, and the intelligent blanking device passes through quality control Module carries out the addition of every kind of component；The control method of quality control module includes：
Step 1, intelligent blanking device tapping channel is obtained by the Video Image Processing device built in quality control module In be tested the image of component particle thing；
A Step 2: specific region of a preview area defined in the image of acquisition；
Step 3: extract an at least preview image using Video Image Processing device；
Step 4: the tested component for judging definition using Video Image Processing device whether there is in the preview image In；
Step 5: being present in when this is tested component in the preview image, determine whether the tested component part appears in the spy Determine a region at least predetermined percentage；And when the predetermined percentage for being tested component appears in the specific region, enable The Video Image Processing device takes pictures processing to extract image by the Video Image Processing device to carry out one；
Step 6: the Video Image Processing device carries out Digital Image Processing to the image for including tested component, Tested component parts are extracted from whole image background, and each tested component in the foreground image to extracting It is identified；
Step 7: Video Image Processing device counts automatically, by scanning tested group identified in view picture foreground image Part simultaneously is counted to obtain the quality of tested component；The ratio of the quality of the existing blanking velocity of tested component is controlled by controller In to predetermined scope；
The predetermined over-segmentation algorithm of the imagery exploitation for including tested particulate matter collected is carried out too being cut into super-pixel Image, to whole input picture, using 8*8 pixel as unit, calculate each unit average gray value and each unit most High-gray level value, obtains at least one region, and the color value of each pixel is identical in the same region；
To obtained super-pixel image zooming-out characteristic vector, the characteristic vector includes profile, texture, brightness and continuous Property；
It is determined that the color value and barycenter in each region；
The barycenter of color value and regional according to corresponding to regional, establishes conspicuousness model；
Prospect sample point and background sample point in described image is obtained according to the conspicuousness model；
According to the conspicuousness model and the prospect sample point and background sample point, background class mould before foundation Type；
Algorithm is cut according to predetermined figure to split described image, the predetermined figure cuts algorithm and utilizes the preceding background class Marginal information between model and pixel is split to described image；
The conspicuousness model is：
Wherein, Si1For region RiThe significance value of middle any pixel point, w (Rj) it is region RjIn pixel number, DS (Ri,Rj) be used to characterize the region RiWith the region RjBetween differences in spatial location metric, DC(Ri,Rj) be used to characterize The region RiWith the region RjBetween color distortion metric, N is that obtained region after over-segmentation is carried out to described image Total number, DS(Ri,Rj) be：DS(Ri,Rj)=exp (- (Center (Ri)-Center(Rj))2/σs 2)；Center(Ri) for institute State region RiBarycenter, Center (Rj) it is the region RjBarycenter, when the equal normalizing of the coordinate of each pixel in described image When changing to [0,1]；
Ask variance image V corresponding to super-pixel image and edge image E, initial window length of side N=3；Window includes letter Breath judges, seeks in edge image E the shared ratio in the window of edge pixel in window corresponding with current window W in original image P, current window includes enough marginal informations if P >=(N-2)/N2, and the condition for meeting to be split then is split, if P< (N-2) then current window does not include enough marginal informations to/N2, without segmentation；
It is described to be tested in step 6 in sample survey using kinzel test, used in kinzel test Bending displacement detection module detects to the stress intensity of bending.
The detection method of the bending displacement detection module includes：
The bending displacement signal from multiple positions is received using the sensor array containing M array element, using built-in Information gathering submodule per reception signal all the way to sampling, the M roads discrete time-domain mixing letter for the stress intensity being bent NumberM=1,2 ..., M；The interaction times of different time piece between collection sensor array node, according to The data setup time sequence arrived, the interaction times of next timeslice between node are predicted by third index flatness, will Direct trust value of the relative error of interaction times predicted value and actual value as node；Detect the stress intensity of maximum deflection Value；
The specific calculation procedure of direct trust value is：Gather n timeslice between sensor array node i and node j Interaction times：Intervals t is chosen as an observation time piece, to observe sensor array node i and tested battle array Interaction times of the sensor node j in 1 timeslice true interaction times, are denoted as y as observation indext, remember successively Record the y of n timeslicen, and save it in the communications records table of node i；Predict the interaction times of (n+1)th timeslice： According to the interaction times settling time sequence of the n timeslice collected, next time is predicted using third index flatness Interaction times in piece n+1 between sensor array node i and j, interaction times are predicted, are denoted asCalculation formula is as follows：
Predictive coefficient an、bn、cnValue can be calculated by equation below：
Wherein：It is respectively once, is secondary, Three-exponential Smoothing number, is calculated by equation below Arrive：
It is the initial value of third index flatness, its value is
α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic of trust, i.e., timeslice nearer from predicted value ytWeight is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；If data fluctuations are larger, and long-term trend change width Spend larger, obvious α when rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), and increase Recent data is to prediction As a result influence；When data have a fluctuation, but long-term trend change is little, α can between 0.1~0.4 value；If data wave Dynamic steady, α should take smaller value (0.05~0.20)；
Calculate direct trust value：
Sensor array node j direct trust value TDijTo predict interaction timesWith true interaction times yn+1Phase To error,
Indirect trust values are calculated using calculating formula obtained from multipath trust recommendation mode；Trusted node is collected to array Sensor node j direct trust value：Sensor array node i meets TD to allik≤ φ credible associated nodes inquire it To sensor array node j direct trust value, wherein φ is the believability threshold of recommended node, and refinement is wanted according to confidence level Degree, φ span is 0~0.4；Calculate indirect trust values：Trust value collected by COMPREHENSIVE CALCULATING, obtains array sensing Device node j indirect trust values TRij,Wherein, Set (i) is observation sensor array node i Associated nodes in had with j nodes and interacted and its direct trust value meets TDik≤ φ node set；
Comprehensive trust value is drawn by direct trust value and indirect trust values conformity calculation；Comprehensive trust value TijCalculation formula It is as follows：Tij=β TDij+(1-β)TRij, wherein β (0≤β≤1) represents the weight of direct trust value, as β=0, node i and section Point j does not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, and it is more objective to judge；As β=1, Sensor array node i to sensor array node j synthesis trust value all from direct trust value, in such case Under, judge more subjective, the actual calculating value as needed for determining β.
Further, M roads discrete time-domain mixed signal is collectedAfter m=1,2 ..., M, also need to carry out Overlapping adding window Short Time Fourier Transform, obtain the time-frequency domain matrix of M mixed signalp =0,1 ..., P-1, q=0,1 ..., Nfft- 1, wherein P represent total window number, NfftRepresent FFT length；P, q) represent when Frequency indexes, and specific time-frequency value isHere NfftThe length of FFT is represented, p represents adding window number, TsRepresent sampling interval, fsSample frequency is represented, C is integer, represents the sampling number at Short Time Fourier Transform adding window interval, C ＜ Nfft, and Kc=Nfft/ C is integer, using the Short Time Fourier Transform of overlapping adding window.
Further, the method for bending displacement signal of the sensor array reception from multiple positions includes：To reception signal Discrete signal vector carry out linear transformation obtain unitary transformation matrix；
The elements in a main diagonal and counter-diagonal element in the unitary transformation matrix calculate the energy of reception signal Characteristic spectrum；
The linear transformation obtains unitary transformation matrix and specifically included：The bending displacement at multiple positions is received to sensor array Signal s (t) carries out linear transformation, carries out as follows：
WhereinA represents the amplitude of signal, and a (m) represents letter Number symbol, p (t) represent shaping function, fcThe carrier frequency of signal is represented,The phase of signal is represented, by non- Obtained after linear transformation：
Further, described in step 5, preparation method also includes the Quenching Treatment after double_stage guide processing, is quenched described Carry out 1.8-2% pre-stretching processing after fire processing in 3.5-4.5h or after the Quenching Treatment after 46-50h.
Further, described in step 6, trimming carries out trimming using the mode of CNC Plasma Cutting, to ensure to meet Corresponding size requirement.
Further, it is described in step 6, sample survey take low power, mechanical system can test, welded specimen, welding use TIG methods, specific experiment are welding tension test, kinzel test, mother metal tension test.
Advantages of the present invention and good effect are：Bubble is few in the automotive light weight technology chassis aluminum alloy junction component of the present invention, Satisfactory mechanical property, intensity are high, corrosion-resistant, resistance to elevated temperatures is high, and stability is good when being used as auto parts, is worthy to be popularized, Original thin-walled support class product stress concentration is improved, the product defects that product easily cracks, toughness is strong, and elongation percentage is high, It is much better than the elongation percentage of existing product, the present invention is strictly controlled al alloy component, and Mn and Mg content ratio carry out list Solely proportioning, to improve mechanical performance；
The automotive light weight technology chassis aluminum alloy junction component of the present invention has good market prospects.
The present invention can carry out blanking to more middle components simultaneously, substantially increase operating efficiency, pass through video image acquisition Processor is counted to obtain the quality of tested component particle thing and the ratio of predetermined quality, and this ratio is can control by controller, Reach the purpose of the uniform blanking of intelligence.
The present invention is detected using bending displacement detection module to the stress intensity of bending, multiple according only to what is received The mixed signal of Frequency Hopping Signal, frequency hopping source signal is estimated, the stress intensity of bending can accurately be detected, with only Short Time Fourier Transform, amount of calculation is small, easily realizes, this method to Frequency Hopping Signal while blind separation is carried out, moreover it is possible to portion Point parameter is estimated, practical, has stronger popularization and application value.
The method that sensor array of the present invention receives the bending displacement signal from multiple positions, data accuracy is than existing Technology improves nearly 5 percentage points.Good data are provided for information gathering submodule to support.
Brief description of the drawings
Fig. 1 is the automotive light weight technology chassis aluminum alloy junction component that the present invention implements offer and preparation method thereof flow chart.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.
Below in conjunction with the accompanying drawings and specific embodiment is further described to the application principle of the present invention.
The present invention provides a kind of automotive light weight technology chassis aluminum alloy junction component and consisted of the following components in percentage by weight：
Titanium 0.10-0.25%, lead 0.20-0.4%, manganese 0.3-0.7%, silicon 8.0-10.0%, iron 0.5-1.3%, copper 2.0-3.0%, magnesium 0.2-0.60%, zinc 0.5-1.0%, chromium 0.1-0.20%, nickel 0.1-0.50%, tin 0.1-0.30% are remaining Measure as aluminium.
As shown in figure 1, a kind of automotive light weight technology chassis aluminum alloy junction component preparation method comprises the following steps：
S101：By raw materials melt and 760 DEG C are warming up to, standing 5 hours.
S102：Carry out being warming up to 750 DEG C again and simultaneously reserve certain interval into liquid material, matched moulds, oxygen is injected into gap Gas；Continue matched moulds, mould closes completely.
S103：Liquid material is injected into barrel；Oxygen is injected into barrel；Then 0.25% alterant is added, is adopted Degasification is carried out with 99% argon gas to operate 10 minutes, prepares die casting within static 13 minutes.
S104：Mould is fixed on the active and inactive mold plate of die casting machine, mould is preheated to 200 DEG C, in mold cavity Even spray last layer water based paint, painting type thickness is 0.005~0.007mm, and it is 160 DEG C to heat and keep mold temperature.
S105：Will handle complete mixed material liquid press-in die in, injection take out part after, carry out 180 DEG C when Effect processing, required automotive light weight technology chassis aluminum alloy junction component semi-finished product are made by processing in 10 hours.
S106：By to semi-finished product cooling after carry out trimming, X flaw detections, ball blast, processing ECM deburrings, carry out cleaning baking eventually Dry and final inspection, obtains product.
Every kind of component is separately stored in different intelligent blanking devices in raw material, and the intelligent blanking device passes through quality control Module carries out the addition of every kind of component；The control method of quality control module includes：
Step 1, intelligent blanking device tapping channel is obtained by the Video Image Processing device built in quality control module In be tested the image of component particle thing；
A Step 2: specific region of a preview area defined in the image of acquisition；
Step 3: extract an at least preview image using Video Image Processing device；
Step 4: the tested component for judging definition using Video Image Processing device whether there is in the preview image In；
Step 5: being present in when this is tested component in the preview image, determine whether the tested component part appears in the spy Determine a region at least predetermined percentage；And when the predetermined percentage for being tested component appears in the specific region, enable The Video Image Processing device takes pictures processing to extract image by the Video Image Processing device to carry out one；
Step 6: the Video Image Processing device carries out Digital Image Processing to the image for including tested component, Tested component parts are extracted from whole image background, and each tested component in the foreground image to extracting It is identified；
Step 7: Video Image Processing device counts automatically, by scanning tested group identified in view picture foreground image Part simultaneously is counted to obtain the quality of tested component；The ratio of the quality of the existing blanking velocity of tested component is controlled by controller In to predetermined scope；
The predetermined over-segmentation algorithm of the imagery exploitation for including tested particulate matter collected is carried out too being cut into super-pixel Image, to whole input picture, using 8*8 pixel as unit, calculate each unit average gray value and each unit most High-gray level value, obtains at least one region, and the color value of each pixel is identical in the same region；
To obtained super-pixel image zooming-out characteristic vector, the characteristic vector includes profile, texture, brightness and continuous Property；
It is determined that the color value and barycenter in each region；
The barycenter of color value and regional according to corresponding to regional, establishes conspicuousness model；
Prospect sample point and background sample point in described image is obtained according to the conspicuousness model；
According to the conspicuousness model and the prospect sample point and background sample point, background class mould before foundation Type；
Algorithm is cut according to predetermined figure to split described image, the predetermined figure cuts algorithm and utilizes the preceding background class Marginal information between model and pixel is split to described image；
The conspicuousness model is：
Wherein, Si1For region RiThe significance value of middle any pixel point, w (Rj) it is region RjIn pixel number, DS (Ri,Rj) be used to characterize the region RiWith the region RjBetween differences in spatial location metric, DC(Ri,Rj) be used to characterize The region RiWith the region RjBetween color distortion metric, N is that obtained region after over-segmentation is carried out to described image Total number, DS(Ri,Rj) be：DS(Ri,Rj)=exp (- (Center (Ri)-Center(Rj))2/σs 2)；Center(Ri) for institute State region RiBarycenter, Center (Rj) it is the region RjBarycenter, when the equal normalizing of the coordinate of each pixel in described image When changing to [0,1]；
Ask variance image V corresponding to super-pixel image and edge image E, initial window length of side N=3；Window includes letter Breath judges, seeks in edge image E the shared ratio in the window of edge pixel in window corresponding with current window W in original image Example P, current window includes enough marginal informations if P >=(N-2)/N2, and the condition for meeting to be split then is split, if P<(N-2) then current window does not include enough marginal informations to/N2, without segmentation；
It is described to be tested in step 6 in sample survey using kinzel test, used in kinzel test Bending displacement detection module detects to the stress intensity of bending.
The detection method of the bending displacement detection module includes：
The bending displacement signal from multiple positions is received using the sensor array containing M array element, using built-in Information gathering submodule per reception signal all the way to sampling, the M roads discrete time-domain mixing letter for the stress intensity being bent NumberM=1,2 ..., M；The interaction times of different time piece between sensor array node are gathered, according to obtaining Data setup time sequence, the interaction times of next timeslice between node are predicted by third index flatness, will be handed over Direct trust value of the relative error of mutual number predicted value and actual value as node；Detect the stress intensity of maximum deflection Value；
The specific calculation procedure of direct trust value is：Gather n timeslice between sensor array node i and node j Interaction times：Intervals t is chosen as an observation time piece, to observe sensor array node i and tested battle array Interaction times of the sensor node j in 1 timeslice true interaction times, are denoted as y as observation indext, record successively The y of n timeslicen, and save it in the communications records table of node i；Predict the interaction times of (n+1)th timeslice：Root According to the interaction times settling time sequence of the n timeslice collected, next timeslice n is predicted using third index flatness Interaction times in+1 between sensor array node i and j, interaction times are predicted, are denoted asCalculation formula is as follows：
Predictive coefficient an、bn、cnValue can be calculated by equation below：
Wherein：It is respectively once, is secondary, Three-exponential Smoothing number, is calculated by equation below Arrive：
It is the initial value of third index flatness, its value is
α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic of trust, i.e., timeslice nearer from predicted value ytWeight is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；If data fluctuations are larger, and long-term trend change width Spend larger, obvious α when rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), and increase Recent data is to prediction As a result influence；When data have a fluctuation, but long-term trend change is little, α can between 0.1~0.4 value；If data wave Dynamic steady, α should take smaller value (0.05~0.20)；
Calculate direct trust value：
Sensor array node j direct trust value TDijTo predict interaction timesWith true interaction times yn+1Phase To error,
Indirect trust values are calculated using calculating formula obtained from multipath trust recommendation mode；Trusted node is collected to array Sensor node j direct trust value：Sensor array node i meets TD to allik≤ φ credible associated nodes inquire it To sensor array node j direct trust value, wherein φ is the believability threshold of recommended node, and refinement is wanted according to confidence level Degree, φ span is 0~0.4；Calculate indirect trust values：Trust value collected by COMPREHENSIVE CALCULATING, obtains array sensing Device node j indirect trust values TRij,Wherein, Set (i) is observation sensor array node i Associated nodes in had with j nodes and interacted and its direct trust value meets TDik≤ φ node set；
Comprehensive trust value is drawn by direct trust value and indirect trust values conformity calculation；Comprehensive trust value TijCalculating it is public Formula is as follows：Tij=β TDij+(1-β)TRij, wherein β (0≤β≤1) represents the weight of direct trust value, as β=0, node i and Node j does not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, and it is more objective to judge；When β=1 When, sensor array node i to sensor array node j synthesis trust value all from direct trust value, in this feelings Under condition, more subjective, the actual calculating value as needed for determining β is judged.
Collect M roads discrete time-domain mixed signalAfter m=1,2 ..., M, also need to carry out overlapping adding window Short Time Fourier Transform, obtain the time-frequency domain matrix of M mixed signalP=0, 1 ..., P-1, q=0,1 ..., Nfft- 1, wherein P represent total window number, NfftRepresent FFT length；P, q) represent time-frequency rope Draw, specific time-frequency value isHere NfftThe length of FFT is represented, p represents adding window number, TsTable Show sampling interval, fsSample frequency is represented, C is integer, represents the sampling number at Short Time Fourier Transform adding window interval, C ＜ Nfft, and Kc=Nfft/ C is integer, using the Short Time Fourier Transform of overlapping adding window.
The method that sensor array receives the bending displacement signal from multiple positions includes：To the discrete letter of reception signal Number vector carry out linear transformation obtain unitary transformation matrix；
The elements in a main diagonal and counter-diagonal element in the unitary transformation matrix calculate the energy of reception signal Characteristic spectrum；
The linear transformation obtains unitary transformation matrix and specifically included：The bending displacement at multiple positions is received to sensor array Signal s (t) carries out linear transformation, carries out as follows：
WhereinA represents the amplitude of signal, and a (m) represents letter Number symbol, p (t) represent shaping function, fcThe carrier frequency of signal is represented,The phase of signal is represented, by non- Obtained after linear transformation：
It is described in S105, preparation method also include double_stage guide processing after Quenching Treatment, after the Quenching Treatment 1.8-2% pre-stretching processing is carried out in 3.5-4.5h or after the Quenching Treatment after 46-50h.
Described trimming carries out trimming using the mode of CNC Plasma Cutting in S106, to ensure to meet corresponding chi Very little requirement.
It is described in S106, sample survey take low power, mechanical system can test, welded specimen, welding use TIG methods, specifically Test as welding tension test, kinzel test, mother metal tension test.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention All any modification, equivalent and improvement made within refreshing and principle etc., should be included in the scope of the protection.

Claims (6)

  1. A kind of 1. preparation method of automotive light weight technology chassis aluminum alloy junction component, it is characterised in that the automotive light weight technology chassis The preparation method of aluminum alloy junction component comprises the following steps：
    Step 1：By raw materials melt and 760 DEG C are warming up to, standing 5 hours；
    Step 2：Carry out being warming up to 750 DEG C again and simultaneously reserve certain interval into liquid material, matched moulds, oxygen is injected into gap；After Continuous matched moulds, mould close completely；
    Step 3：Liquid material is injected into barrel；Oxygen is injected into barrel；Then 0.25% alterant is added, is used 99% argon gas carries out degasification and operated 10 minutes, prepares die casting within static 13 minutes；
    Step 4：Mould is fixed on the active and inactive mold plate of die casting machine, mould is preheated to 200 DEG C, uniformly sprayed in mold cavity Last layer water based paint, painting type thickness are 0.005~0.007mm, and it is 160 DEG C to heat and keep mold temperature；
    Step 5：It will handle in the mixed material liquid press-in die completed, after part is taken out in injection, carry out at 180 DEG C of timeliness Reason, required automotive light weight technology chassis aluminum alloy junction component semi-finished product are made by processing in 10 hours；
    Step 6：By to semi-finished product cooling after carry out trimming, X flaw detections, ball blast, processing ECM deburrings, the whole cleaning, drying of progress And final inspection, obtain product；
    Every kind of component is separately stored in different intelligent blanking devices in raw material, and the intelligent blanking device passes through quality control module Carry out the addition of every kind of component；The control method of quality control module includes：
    Step 1, quilt in intelligent blanking device tapping channel is obtained by the Video Image Processing device built in quality control module Survey the image of component particle thing；
    A Step 2: specific region of a preview area defined in the image of acquisition；
    Step 3: extract an at least preview image using Video Image Processing device；
    Step 4: the tested component for judging definition using Video Image Processing device whether there is in the preview image；
    Step 5: being present in when this is tested component in the preview image, determine whether the tested component part appears in the given zone A domain at least predetermined percentage；And when the predetermined percentage for being tested component appears in the specific region, enable this regard Frequency image acquisition and processing device takes pictures processing to extract image by the Video Image Processing device to carry out one；
    Step 6: the Video Image Processing device carries out Digital Image Processing to the image for including tested component, will be by Survey component parts to extract from whole image background, and each tested component in the foreground image to extracting is carried out Mark；
    Step 7: Video Image Processing device counts automatically, by scanning the tested component identified in view picture foreground image simultaneously Counted to obtain the quality of tested component；The ratio of the quality of the tested existing blanking velocity of component is controlled by controller to pre- In fixed scope；
    The predetermined over-segmentation algorithm of the imagery exploitation for including tested particulate matter collected is carried out too being cut into super-pixel image, To whole input picture, using 8*8 pixel as unit, the average gray value of each unit and the maximum gray scale of each unit are calculated Value, obtains at least one region, and the color value of each pixel is identical in the same region；
    To obtained super-pixel image zooming-out characteristic vector, the characteristic vector includes profile, texture, brightness and continuity；
    It is determined that the color value and barycenter in each region；
    The barycenter of color value and regional according to corresponding to regional, establishes conspicuousness model；
    Prospect sample point and background sample point in described image is obtained according to the conspicuousness model；
    According to the conspicuousness model and the prospect sample point and background sample point, background class model before foundation；
    Algorithm is cut according to predetermined figure to split described image, the predetermined figure cuts algorithm and utilizes the preceding background class model And the marginal information between pixel is split to described image；
    The conspicuousness model is：
    Wherein, Si1For region RiThe significance value of middle any pixel point, w (Rj) it is region RjIn pixel number, DS(Ri, Rj) be used to characterize the region RiWith the region RjBetween differences in spatial location metric, DC(Ri,Rj) described for characterizing Region RiWith the region RjBetween color distortion metric, N be to described image carry out over-segmentation after obtain region it is total Number, DS(Ri,Rj) be：Center(Ri) it is the area Domain RiBarycenter, Center (Rj) it is the region RjBarycenter, when the coordinate of each pixel in described image normalizes to When [0,1]；
    Ask variance image V corresponding to super-pixel image and edge image E, initial window length of side N=3；Window is sentenced comprising information It is disconnected, the shared ratio P in the window of edge pixel in window corresponding with current window W in original image is sought in edge image E, if Then current window includes enough marginal informations to P >=(N-2)/N2, and the condition for meeting to be split then is split, if P<(N- 2) then current window does not include enough marginal informations to/N2, without segmentation；
    In the step 6, tested in sample survey using kinzel test, using bending position in kinzel test Detection module is moved to detect the stress intensity of bending；The detection method of the bending displacement detection module includes：
    The bending displacement signal from multiple positions is received using the sensor array containing M array element；
    Using built-in information gathering submodule to being sampled per reception signal all the way, the M roads for the stress intensity being bent Discrete time-domain mixed signalDifferent time piece between collection sensor array node Interaction times, according to obtained data setup time sequence, during by third index flatness to predict next between node Between piece interaction times, the direct trust value using the relative error of interaction times predicted value and actual value as node；Detect The stress intensity value of maximum deflection；
    The specific calculation procedure of direct trust value is：Gather the friendship of n timeslice between sensor array node i and node j Mutual number：Intervals t is chosen as an observation time piece, is passed with observing sensor array node i and tested array Interaction times of the sensor node j in 1 timeslice true interaction times, are denoted as y as observation indext, n are recorded successively The y of timeslicen, and save it in the communications records table of node i；Predict the interaction times of (n+1)th timeslice：According to adopting The interaction times settling time sequence of the n timeslice collected, predicted using third index flatness in next timeslice n+1 Interaction times between sensor array node i and j, interaction times are predicted, are denoted asCalculation formula is as follows：
    Predictive coefficient an、bn、cnValue can be calculated by equation below：
    Wherein：It is respectively once, is secondary, Three-exponential Smoothing number, is calculated by equation below：
    It is the initial value of third index flatness, its value is
    α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic of trust, i.e., the y of timeslice nearer from predicted valuetWeight It is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；If data fluctuations are larger, and long-term trend amplitude of variation compared with Greatly, α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), and increase Recent data is to prediction result Influence；When data have a fluctuation, but long-term trend change is little, α can between 0.1~0.4 value；If data fluctuations are put down Surely, α should take smaller value (0.05~0.20)；
    Calculate direct trust value：
    Sensor array node j direct trust value TDijTo predict interaction timesWith true interaction times yn+1It is relative by mistake Difference,
    Indirect trust values are calculated using calculating formula obtained from multipath trust recommendation mode；Trusted node is collected to array sensing Device node j direct trust value：Sensor array node i meets TD to allik≤ φ credible associated nodes inquire that it is poised for battle Sensor node j direct trust value, wherein φ is the believability threshold of recommended node, according to the precision prescribed of confidence level, φ span is 0~0.4；Calculate indirect trust values：Trust value collected by COMPREHENSIVE CALCULATING, obtain sensor array section Point j indirect trust values TRij,Wherein, Set (i) is the pass of observation sensor array node i Had in interlink point with j nodes and interacted and its direct trust value meets TDik≤ φ node set；
    Comprehensive trust value is drawn by direct trust value and indirect trust values conformity calculation；Comprehensive trust value TijCalculation formula such as Under：Tij=β TDij+(1-β)TRij, wherein β (0≤β≤1) represents the weight of direct trust value, as β=0, node i and node J does not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, and it is more objective to judge；As β=1, battle array Sensor node i to sensor array node j synthesis trust value all from direct trust value, in this case, Judge more subjective, the actual calculating value as needed for determining β.
  2. 2. the preparation method of automotive light weight technology chassis aluminum alloy junction component as claimed in claim 1, it is characterised in that collect M Road discrete time-domain mixed signalAfterwards, also need to carry out the Fourier's change in short-term of overlapping adding window Change, obtain the time-frequency domain matrix of M mixed signal Wherein P represents total window number, NfftRepresent FFT length；P, q) time-frequency index is represented, specific time-frequency value isHere NfftThe length of FFT is represented, p represents adding window number, TsRepresent sampling interval, fsExpression is adopted Sample frequency, C are integer, represent the sampling number at Short Time Fourier Transform adding window interval, C ＜ Nfft, and Kc=Nfft/ C is integer, Using the Short Time Fourier Transform of overlapping adding window.
  3. 3. the preparation method of automotive light weight technology chassis aluminum alloy junction component as claimed in claim 1, it is characterised in that array sensing The method that device receives the bending displacement signal from multiple positions includes：The discrete signal vector of reception signal is linearly become Get unitary transformation matrix in return；
    The elements in a main diagonal and counter-diagonal element in the unitary transformation matrix calculate the energy feature of reception signal Spectrum；
    The linear transformation obtains unitary transformation matrix and specifically included：The bending displacement signal at multiple positions is received to sensor array S (t) carries out linear transformation, carries out as follows：
    WhereinA represents the amplitude of signal, and a (m) represents signal Symbol, p (t) represent shaping function, fcThe carrier frequency of signal is represented,The phase of signal is represented, by non-linear Obtained after conversion：
  4. 4. the preparation method of automotive light weight technology chassis aluminum alloy junction component as claimed in claim 1, it is characterised in that described in step In rapid five, preparation method also include double_stage guide processing after Quenching Treatment, after the Quenching Treatment in 3.5-4.5h or 1.8-2% pre-stretching processing is carried out after 46-50h after the Quenching Treatment.
  5. 5. the preparation method of automotive light weight technology chassis aluminum alloy junction component as claimed in claim 1, it is characterised in that described in step In rapid six, trimming carries out trimming using the mode of CNC Plasma Cutting.
  6. A kind of 6. automotive light weight technology prepared by preparation method of automotive light weight technology chassis aluminum alloy junction component as claimed in claim 1 Chassis aluminum alloy junction component, it is characterised in that the automotive light weight technology chassis aluminum alloy junction component is by following percentage by weight Component forms：
    Titanium 0.10-0.25%, lead 0.20-0.4%, manganese 0.3-0.7%, silicon 8.0-10.0%, iron 0.5-1.3%, copper 2.0- 3.0%th, magnesium 0.2-0.60%, zinc 0.5-1.0%, chromium 0.1-0.20%, nickel 0.1-0.50%, tin 0.1-0.30%, surplus are Aluminium.
CN201710964788.1A 2017-10-17 2017-10-17 A kind of automotive light weight technology chassis aluminum alloy junction component and preparation method thereof Pending CN107723527A (en)

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Application publication date: 20180223