CN116993102A - MIM forming process - Google Patents
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- CN116993102A CN116993102A CN202311005361.0A CN202311005361A CN116993102A CN 116993102 A CN116993102 A CN 116993102A CN 202311005361 A CN202311005361 A CN 202311005361A CN 116993102 A CN116993102 A CN 116993102A
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- 238000000034 method Methods 0.000 title claims abstract description 61
- 239000002994 raw material Substances 0.000 claims abstract description 228
- 238000004519 manufacturing process Methods 0.000 claims abstract description 68
- 239000000843 powder Substances 0.000 claims abstract description 50
- 238000000465 moulding Methods 0.000 claims abstract description 39
- 238000005245 sintering Methods 0.000 claims abstract description 25
- 238000001746 injection moulding Methods 0.000 claims abstract description 22
- 239000011230 binding agent Substances 0.000 claims abstract description 16
- 238000002156 mixing Methods 0.000 claims abstract description 15
- 238000005238 degreasing Methods 0.000 claims abstract description 9
- 238000002347 injection Methods 0.000 claims abstract description 9
- 239000007924 injection Substances 0.000 claims abstract description 9
- 238000005070 sampling Methods 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 31
- 239000000463 material Substances 0.000 claims description 11
- 238000009472 formulation Methods 0.000 claims description 8
- 239000000203 mixture Substances 0.000 claims description 8
- 239000000047 product Substances 0.000 claims description 7
- 238000003032 molecular docking Methods 0.000 claims description 5
- 238000012795 verification Methods 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 4
- 230000009193 crawling Effects 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000005242 forging Methods 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 230000036314 physical performance Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 239000013589 supplement Substances 0.000 claims description 3
- 230000009469 supplementation Effects 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000003466 welding Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 8
- 239000002184 metal Substances 0.000 description 21
- 238000005516 engineering process Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 239000002245 particle Substances 0.000 description 3
- 238000005272 metallurgy Methods 0.000 description 2
- 238000004663 powder metallurgy Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
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- 239000000853 adhesive Substances 0.000 description 1
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- 238000000280 densification Methods 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F3/00—Manufacture of workpieces or articles from metallic powder characterised by the manner of compacting or sintering; Apparatus specially adapted therefor ; Presses and furnaces
- B22F3/22—Manufacture of workpieces or articles from metallic powder characterised by the manner of compacting or sintering; Apparatus specially adapted therefor ; Presses and furnaces for producing castings from a slip
- B22F3/225—Manufacture of workpieces or articles from metallic powder characterised by the manner of compacting or sintering; Apparatus specially adapted therefor ; Presses and furnaces for producing castings from a slip by injection molding
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F1/00—Metallic powder; Treatment of metallic powder, e.g. to facilitate working or to improve properties
- B22F1/10—Metallic powder containing lubricating or binding agents; Metallic powder containing organic material
- B22F1/108—Mixtures obtained by warm mixing
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F3/00—Manufacture of workpieces or articles from metallic powder characterised by the manner of compacting or sintering; Apparatus specially adapted therefor ; Presses and furnaces
- B22F3/10—Sintering only
- B22F3/1017—Multiple heating or additional steps
- B22F3/1021—Removal of binder or filler
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0633—Workflow analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The application relates to an MIM forming process, which comprises the following steps: acquiring order information in real time, and determining a molding formula based on the order information, wherein the molding formula comprises at least one MIM raw material powder, at least one binder and process flow information; sampling and detecting the formula raw materials to obtain MIM raw material powder and a binder meeting the requirements of a molding formula; adding the MIM raw material powder and the binder which are qualified in detection into mixing production equipment in batches based on the process flow information, and uniformly mixing to obtain uniform feed; feeding the uniform feed obtained by mixing into an injection device, and controlling the injection device to perform injection molding based on process flow information to obtain a green part; and (3) placing the green part obtained by injection molding in a sintering furnace, and controlling the sintering furnace to carry out thermal degreasing and compact sintering based on the technological process information to obtain a compact workpiece. The application has the effects of effectively improving the production quality of the workpiece and reducing the production cost.
Description
Technical Field
The application relates to the field of metal powder, in particular to an MIM forming process.
Background
Metal injection molding (MIM for short) is a novel near net shape forming technique of powder metallurgy which is led out from the plastic injection molding industry, and it is well known that plastic injection molding technology is low in price to produce various products with complex shapes, but plastic products have low strength, and in order to improve the performance, metal or ceramic powder can be added into plastic to obtain products with high strength and good wear resistance. In recent years, this idea has evolved to maximize the solids content and to completely remove the binder and densify the shaped blank during the subsequent sintering process. This new powder metallurgy forming process is known as metal injection forming.
The basic process steps of metal injection molding are: firstly, selecting metal powder and a binder which meet MIM requirements, then adopting a proper method to mix the powder and the binder into uniform feed at a certain temperature, granulating, then performing injection molding, and sintering and densification to obtain a finished product after degreasing treatment. One of the advantages of metal injection molding is a wide range of applicable materials, a wide range of applications, and basically any powder material that can be cast at high temperatures can be formed into parts by MIM processes, including difficult-to-machine materials and high-melting materials in conventional manufacturing processes. The finer the particles, the larger the specific surface area, the easier the MIM raw material powder is to mold and sinter, and the more circular or nearly circular the particle shape is optimal. However, the existing raw material preparation technology is limited, so that high-quality small-diameter nearly circular raw material powder is difficult to obtain and high in price, and the cost of a workpiece is easy to be excessively high. However, if large-particle MIM raw material powder is adopted at one time, the proportion of the binder is increased, the molding and sintering difficulties are high, and the workpiece performance is poor, so that the production expectation cannot be met
With respect to the related art, the existing metal injection molding technology for preparing a workpiece is easy to cause insufficient performance of the workpiece and excessive production cost.
Disclosure of Invention
In order to solve the problems that the existing metal injection molding technology for preparing a workpiece easily causes insufficient workpiece performance and overhigh production cost, the application provides an MIM molding process.
In a first aspect, the present application provides a MIM molding process, which adopts the following technical scheme:
a MIM molding process comprising the steps of:
the formula is selected: acquiring order information in real time, and determining a molding formula based on the order information, wherein the molding formula comprises at least one MIM raw material powder, at least one binder and process flow information;
and (3) raw material verification: sampling and detecting the formula raw materials to obtain MIM raw material powder and a binder meeting the requirements of a molding formula;
mixing: adding the MIM raw material powder and the binder which are qualified in detection into mixing production equipment in batches based on the process flow information, and uniformly mixing to obtain uniform feed;
injection molding: feeding the uniform feed obtained by mixing into an injection device, and controlling the injection device to perform injection molding based on process flow information to obtain a green part;
degreasing and sintering: and (3) placing the green part obtained by injection molding in a sintering furnace, and controlling the sintering furnace to carry out thermal degreasing and compact sintering based on the technological process information to obtain a compact workpiece.
Preferably, the acquiring order information in real time, and determining the molding formula based on the order information specifically includes the following steps:
acquiring order information in real time, and generating a demand instruction of a workpiece required by a user based on the order information, wherein the order information comprises workpiece demand quantity, workpiece unit price information, delivery deadline information, workpiece performance index information and workpiece specification information; the demand instructions include performance demand instructions and raw material cost demand instructions;
determining a plurality of alternative workpiece formulas meeting the performance requirements of the order based on the performance requirement instruction matching;
acquiring the raw material cost, the raw material acquisition difficulty and the formula component volume ratio of each alternative workpiece formula, and calculating the raw material score of each alternative workpiece formula through a preset formula raw material score calculation formula;
and selecting the alternative workpiece formula with the highest raw material score to determine the alternative workpiece formula as a forming formula.
Preferably, the determining a plurality of candidate workpiece formulas meeting the performance requirement of the order based on the performance requirement instruction matching specifically includes the following steps:
collecting the existing raw material powder performance data to establish a raw material performance database, and periodically crawling raw material powder performance data disclosed in a public resource website to supplement the raw material powder performance data into the raw material performance database;
establishing a machine learning model, training raw material powder performance data in a raw material performance database through formula selection historical data to obtain a formula matching model, and periodically supplementing updated raw material powder performance data in the raw material performance database into the formula matching model for data supplementation and iterative training;
and matching a plurality of alternative workpiece formulas meeting the performance requirements of the order form based on the performance requirement instructions through a formula matching model.
Preferably, the step of calculating the raw material score of each candidate workpiece formula by a preset formula raw material score calculation formula specifically includes the following steps:
accounting the raw material cost Y of each alternative workpiece formula based on the order information connection supply chain channel;
determining the acquisition modes of various raw materials in each alternative workpiece formula based on the order information connection supply chain channel, and further calculating the raw material acquisition difficulty score N of each alternative workpiece formula;
performing mixed simulation on each alternative workpiece formula to determine the volume fraction T of the raw material powder of each alternative workpiece formula unit workpiece;
calculating the raw material scores of all the alternative workpiece formulas according to a preset formula raw material score calculation formula, wherein the formula raw material score calculation formula specifically comprises the following steps:wherein H is i Scoring the raw materials of the ith candidate workpiece formulation, Y i The raw material cost for the formula of the ith candidate workpiece is set according to the total price of the order, y is the preset raw material cost standard, and P 1 Scoring coefficient for raw material cost, P 2 Scoring coefficients for volume fractions, and P 1 、P 2 Are set by the manager.
Preferably, the raw material cost for accounting each candidate workpiece formula based on the order information connection supply chain channel specifically comprises the following steps:
calculating and determining various raw material demand total amounts of all candidate workpiece formulas based on order information;
the method comprises the steps of connecting a supply chain channel to obtain the recent optimal unit price of various raw materials of each alternative workpiece formula;
calculating the raw material cost of each alternative workpiece formula through a preset raw material cost calculation formula, wherein the raw material cost calculation formula specifically comprises the following steps:
wherein n is the total quantity of raw material types and X of the formula of the alternative workpiece i Z is the total required amount of the ith raw material of the alternative workpiece formula i For the i-th raw material of the alternative workpiece formula, the recent optimal unit price is calculated, C is the purchase guarantee coefficient dynamically generated based on historical data, and C>1。
Preferably, the dynamically generating the purchase security coefficient based on the historical data specifically includes the following steps:
acquiring historical production data of the alternative workpiece formula, and determining production loss coefficients of each production in the formula history, wherein the production loss coefficients are the ratio of the actual consumption of materials to the rated consumption of materials of qualified products;
drawing each production loss coefficient of the alternative workpiece formula based on a time axis to generate a loss curve, and calculating a predicted loss coefficient A of the alternative formula based on the loss curve through a preset loss prediction formula;
acquiring the near ten times of molding production data of an enterprise, acquiring the production loss coefficient of the enterprise, and taking an average value to obtain a reference loss coefficient B;
calculating the purchase guarantee coefficient of the alternative workpiece formula through a preset purchase guarantee coefficient calculation formula, wherein the purchase guarantee coefficient calculation formula specifically comprises the following steps: c=0.8a+0.2b+0.1.
Preferably, the preset loss prediction formula specifically includes:
wherein a is m The production loss coefficient of the latest production of the alternative workpiece formula, m is the historical production times of the alternative workpiece formula, a j And (5) formulating the production loss coefficient of the jth production for the alternative workpiece.
Preferably, the method for obtaining the recent optimal unit price of each raw material of each candidate workpiece formula by the connection supply chain channel specifically comprises the following steps:
connecting a supply chain channel to obtain the supply unit price E of each supplier of the target raw materials;
acquiring the freight F and the raw material tariffs G of each supplier of the target raw material;
calculating the actual unit price D of each supplier of the target raw material according to a preset actual unit price calculation formula, sequencing the actual unit price, and selecting the lowest actual unit price U as the optimal unit price Z of the target raw material, wherein the actual unit price calculation formula specifically comprises the following steps:
wherein D is the actual unit price of each supplier of the target raw material, and X is the required quantity of the target raw material in the alternative formula.
Preferably, the determining, based on the order information connection supply chain channel, an obtaining manner of each raw material in each candidate workpiece formula, and further calculating a raw material obtaining difficulty score N of each candidate workpiece formula specifically includes: determining the acquisition modes of various raw materials in each alternative workpiece formula based on an order information connection supply chain channel, wherein the acquisition modes comprise domestic purchase, trade priority purchase and other national purchase; and generating a raw material acquisition difficulty score N of each alternative workpiece formula according to a preset purchasing mode difficulty comparison table corresponding to the raw material acquisition mode.
Preferably, the performance demand instructions include one or more of a foundry performance demand, a forging performance demand, a welding performance demand, a cutting performance demand, a forming performance demand, and a heat treatment process performance demand, and the use performance demand includes one or more of a mechanical performance demand, a physical performance demand, and a chemical performance demand.
In summary, the present application includes at least one of the following beneficial technical effects:
1. according to order requirement screening order matching, a molding formula which meets the requirement of a user and has low raw material cost and low raw material acquisition difficulty is obtained, a molding formula with large metal powder ratio is selected as far as possible so as to improve sintering shrinkage stability, the quality of the raw materials is ensured by sampling and detecting the raw materials, intelligent powder injection molding sintering metallurgy production is realized according to the technological process of the molding formula, and the effects of effectively improving the production quality of workpieces and reducing the production cost are achieved;
2. generating a performance demand instruction and a raw material cost demand instruction based on order information extraction, determining a plurality of alternative workpiece formulas according to the performance demand instruction matching, and grading the alternative workpiece formulas based on the three sides of raw material cost, raw material acquisition difficulty and metal powder volume ratio in the formulas so as to realize that the alternative workpiece formulas with low raw material cost, low raw material acquisition difficulty and large metal raw material volume ratio are selected as forming formulas, so that enterprise profit is improved as much as possible on the basis of ensuring workpiece performance, workpiece shrinkage rate is reduced, workpiece sintering shrinkage stability is improved, and the effects of effectively improving workpiece production quality and reducing production cost are achieved;
3. and generating a predicted loss coefficient A and a reference loss coefficient B of the alternative workpiece formula based on the historical production data of the alternative workpiece formula and the recent production data of the enterprise, and further calculating and determining a purchase guarantee coefficient of the alternative workpiece formula, thereby being beneficial to accurate purchase of the number of raw materials and reducing the cost of the raw materials.
Drawings
FIG. 1 is a process flow diagram of an MIM forming process in an embodiment of the application;
FIG. 2 is a flow chart of a method of matching a determined molding formulation in an embodiment of the application;
FIG. 3 is a flow chart of a method of matching a determination of an alternative workpiece recipe in an embodiment of the application;
FIG. 4 is a flow chart of a method of calculating a feedstock score for each alternative workpiece recipe in an embodiment of the application;
FIG. 5 is a flow chart of a method of accounting for raw material costs for various alternative workpiece formulations in an embodiment of the application;
FIG. 6 is a flow chart of a method of generating procurement assurance coefficients for an alternative workpiece formulation in an embodiment of the application;
FIG. 7 is a flow chart of a method for recent optimal unit price of various feedstocks in an embodiment of the application.
Detailed Description
The application is described in further detail below with reference to fig. 1-7.
The embodiment of the application discloses an MIM forming process. Referring to fig. 1, a MIM molding process includes the steps of:
s1, selecting a formula: acquiring order information in real time, and determining a molding formula based on the order information, wherein the molding formula comprises at least one MIM raw material powder, at least one binder and process flow information;
s2, raw material verification: sampling and detecting the formula raw materials to obtain MIM raw material powder and a binder meeting the requirements of a molding formula;
sampling raw materials, detecting the granularity and shape of sampled metal powder, detecting the purity of the material and the cleanliness of the surface, and detecting the cohesiveness verification and degreasing verification of the sampled adhesive;
s3, mixing: adding the MIM raw material powder and the binder which are qualified in detection into mixing production equipment in batches based on the process flow information, and uniformly mixing to obtain uniform feed;
s4, injection molding: feeding the uniform feed obtained by mixing into an injection device, and controlling the injection device to perform injection molding based on process flow information to obtain a green part;
s5, degreasing and sintering: and (3) placing the green part obtained by injection molding in a sintering furnace, and controlling the sintering furnace to carry out thermal degreasing and compact sintering based on the technological process information to obtain a compact workpiece. According to order demand screening order matching, a molding formula which meets the performance demands of users and has low raw material cost and low raw material acquisition difficulty is obtained, a molding formula with large metal powder ratio is selected as far as possible so as to improve sintering shrinkage stability, then the raw materials are sampled and detected, the quality of the raw materials is ensured, intelligent powder injection molding sintering metallurgy production is realized according to the technological process of the molding formula, and the effects of effectively improving the production quality of workpieces and reducing the production cost are achieved.
Referring to fig. 2, the method for acquiring order information in real time and determining a molding formula based on the order information specifically includes the following steps:
a1, generating a demand instruction of a workpiece required by a user: acquiring order information in real time, and generating a demand instruction of a workpiece required by a user based on the order information, wherein the order information comprises workpiece demand quantity, workpiece unit price information, delivery deadline information, workpiece performance index information and workpiece specification information; the demand instructions include performance demand instructions and raw material cost demand instructions;
a2, matching and determining an alternative workpiece formula: determining a plurality of alternative workpiece formulas meeting the performance requirements of the order based on the performance requirement instruction matching;
a3, calculating the raw material scores of the formulas of the alternative workpieces: acquiring the raw material cost, the raw material acquisition difficulty and the formula component volume ratio of each alternative workpiece formula, and calculating the raw material score of each alternative workpiece formula through a preset formula raw material score calculation formula;
a4, selecting an alternative workpiece formula with the highest grading of the raw materials to determine the alternative workpiece formula as a forming formula. And the method comprises the steps of extracting and generating a performance demand instruction and a raw material cost demand instruction based on order information, matching and determining a plurality of alternative workpiece formulas according to the performance demand instruction, and grading the alternative workpiece formulas based on the three sides of raw material cost, raw material acquisition difficulty and metal powder volume ratio in the formulas so as to realize that the alternative workpiece formulas with low raw material cost, low raw material acquisition difficulty and large metal raw material volume ratio are selected as forming formulas, so that enterprise profit is improved as much as possible on the basis of ensuring workpiece performance, the workpiece shrinkage rate is reduced, the sintering shrinkage stability of the workpiece is improved, and the effects of effectively improving the production quality of the workpiece and reducing the production cost are achieved.
Wherein the performance demand instructions include one or more of a foundry performance demand, a forging performance demand, a welding performance demand, a cutting performance demand, a forming performance demand, and a heat treatment process performance demand, and the use performance demand includes one or more of a mechanical performance demand, a physical performance demand, and a chemical performance demand.
Referring to fig. 3, the determining a number of candidate workpiece formulas meeting the performance requirements of the order based on the performance requirement instruction matching specifically includes the following steps:
b1, establishing a raw material performance database: collecting the existing raw material powder performance data to establish a raw material performance database, and periodically crawling raw material powder performance data disclosed in a public resource website to supplement the raw material powder performance data into the raw material performance database;
b2, training to obtain a formula matching model: establishing a machine learning model, training raw material powder performance data in a raw material performance database through formula selection historical data to obtain a formula matching model, and periodically supplementing updated raw material powder performance data in the raw material performance database into the formula matching model for data supplementation and iterative training;
wherein the public resource website is an open source website for collecting metal raw material powder performance parameters; the specific steps for training the machine learning model are not described in detail in the prior art;
b3, matching and determining an alternative workpiece formula: and matching a plurality of alternative workpiece formulas meeting the performance requirements of the order form based on the performance requirement instructions through a formula matching model. The workpiece performance produced by the metal powder injection molding technology greatly depends on the metal raw materials, and by establishing a raw material performance database, the iterative training of a machine model is assisted on the basis that the database provides raw material powder performance data, so that the model stability is improved, the model data integrity is enriched, and a plurality of alternative workpiece formulas meeting the order performance requirements can be efficiently and accurately matched.
Referring to fig. 4, the steps of obtaining the raw material cost, the raw material obtaining difficulty and the formula component volume ratio of each candidate workpiece formula, and calculating the raw material score of each candidate workpiece formula according to the preset formula raw material score calculation formula specifically include the following steps:
and C1, accounting the raw material cost Y of each alternative workpiece formula: accounting the raw material cost Y of each alternative workpiece formula based on the order information connection supply chain channel;
and C2, accounting raw material acquisition difficulty scores N of the formulas of the various alternative workpieces: determining the acquisition modes of various raw materials in each alternative workpiece formula based on the order information connection supply chain channel, and further calculating the raw material acquisition difficulty score N of each alternative workpiece formula;
and C3, determining the volume fraction T of the raw material powder of the unit workpiece of the alternative workpiece formula: performing mixed simulation on each alternative workpiece formula to determine the volume fraction T of the raw material powder of each alternative workpiece formula unit workpiece;
and C4, calculating the raw material scores of the formulas of the alternative workpieces: calculating the raw material scores of all the alternative workpiece formulas according to a preset formula raw material score calculation formula, wherein the formula raw material score calculation formula specifically comprises the following steps:wherein H is i Scoring the raw materials of the ith candidate workpiece formulation, Y i The raw material cost for the formula of the ith candidate workpiece is set according to the total price of the order, y is the preset raw material cost standard, and P 1 Scoring coefficient for raw material cost, P 2 Scoring coefficients for volume fractions, and P 1 、P 2 Are set by the manager. Grading the alternative workpiece formula based on the three aspects of raw material cost, raw material acquisition difficulty and metal powder volume ratio in the formula so as to realize low raw material selection cost and low raw material acquisition difficultyAnd the alternative workpiece formula with large volume ratio of the metal raw materials is used as a forming formula, so that the profit of enterprises is improved as much as possible on the basis of ensuring the performance of the workpiece, the shrinkage rate of the workpiece is reduced, the sintering shrinkage stability of the workpiece is improved, and the effects of effectively improving the production quality of the workpiece and reducing the production cost are achieved.
In addition, the determining the obtaining mode of various raw materials in each candidate workpiece formula based on the order information connection supply chain channel, and further calculating the raw material obtaining difficulty score N of each candidate workpiece formula specifically includes: determining the acquisition modes of various raw materials in each alternative workpiece formula based on an order information connection supply chain channel, wherein the acquisition modes comprise domestic purchase, trade priority purchase and other national purchase; and generating a raw material acquisition difficulty score N of each alternative workpiece formula according to a preset purchasing mode difficulty comparison table corresponding to the raw material acquisition mode.
Referring to fig. 5, the raw material cost for accounting each candidate workpiece recipe based on the order information docking supply chain channel specifically includes the following steps:
d1, calculating and determining the total amount of raw material requirements: calculating and determining various raw material demand total amounts of all candidate workpiece formulas based on order information;
d2, obtaining the recent optimal unit price of the raw materials: the method comprises the steps of connecting a supply chain channel to obtain the recent optimal unit price of various raw materials of each alternative workpiece formula;
the supply chain channel comprises a cooperative supply chain platform, a domestic and foreign import and export goods supply channel and an Internet public P2P goods supply platform;
d3, calculating the raw material cost of each alternative workpiece formula: calculating the raw material cost of each alternative workpiece formula through a preset raw material cost calculation formula, wherein the raw material cost calculation formula specifically comprises the following steps:
wherein n is the total quantity of raw material types and X of the formula of the alternative workpiece i Z is the total required amount of the ith raw material of the alternative workpiece formula i For the i-th raw material of the alternative workpiece formula, the recent optimal unit price is calculated, C is the purchase guarantee coefficient dynamically generated based on historical data, and C>1. The optimal unit price of the target raw material is obtained through the existing supply chain channel of the connecting enterprise, and then the purchasing guarantee coefficient is generated based on the historical production data accounting of the enterprise, so that the raw material production cost is determined, and the effects of effectively improving the production quality of workpieces and reducing the production cost are achieved.
Referring to fig. 6, the dynamic generation of the purchase security coefficient based on the historical data specifically includes the following steps:
e1, determining production loss coefficients of each production of the alternative workpiece formula: acquiring historical production data of the alternative workpiece formula, and determining production loss coefficients of each production in the formula history, wherein the production loss coefficients are the ratio of the actual consumption of materials to the rated consumption of materials of qualified products;
e2, calculating the purchase guarantee coefficient of the alternative workpiece formula: drawing each production loss coefficient of the alternative workpiece formula based on a time axis to generate a loss curve, and calculating a predicted loss coefficient A of the alternative formula based on the loss curve through a preset loss prediction formula;
e3, acquiring a reference loss coefficient of an enterprise: acquiring the near ten times of molding production data of an enterprise, acquiring the production loss coefficient of the enterprise, and taking an average value to obtain a reference loss coefficient B;
e4, calculating the purchase guarantee coefficient of the alternative workpiece formula: calculating the purchase guarantee coefficient of the alternative workpiece formula through a preset purchase guarantee coefficient calculation formula, wherein the purchase guarantee coefficient calculation formula specifically comprises the following steps: c=0.8a+0.2b+0.1. And generating a predicted loss coefficient A and a reference loss coefficient B of the alternative workpiece formula based on the historical production data of the alternative workpiece formula and the recent production data of the enterprise, and further calculating and determining a purchase guarantee coefficient of the alternative workpiece formula, thereby being beneficial to accurate purchase of the number of raw materials and reducing the cost of the raw materials.
The preset loss prediction formula specifically comprises the following steps:
wherein a is m The production loss coefficient of the latest production of the alternative workpiece formula, m is the historical production times of the alternative workpiece formula, a j And (5) formulating the production loss coefficient of the jth production for the alternative workpiece.
Referring to fig. 7, the method for obtaining the recent optimal unit price of various raw materials of each candidate workpiece recipe by the docking supply chain channel specifically comprises the following steps:
f1, connecting a supply chain channel to obtain the supply unit price E of each supplier of the target raw material;
f2, acquiring the freight F and the raw material tariffs G of each supplier of the target raw materials;
f3, determining the optimal unit price Z of the target raw material: calculating the actual unit price D of each supplier of the target raw material according to a preset actual unit price calculation formula, sequencing the actual unit price, and selecting the lowest actual unit price U as the optimal unit price Z of the target raw material, wherein the actual unit price calculation formula specifically comprises the following steps:
wherein D is the actual unit price of each supplier of the target raw material, and X is the required quantity of the target raw material in the alternative formula. And (3) connecting the raw materials into a supply chain channel to obtain supply unit prices Y of different suppliers of the target raw materials, and determining freight charges H and raw material tariffs G by combining actual geographic positions of factories and suppliers, and calculating actual unit prices based on the unit prices, so that the optimal unit price matching accuracy and rationality of the target raw materials are improved, and the organic combination degree of profit scoring calculation and actual conditions of the applied factories is further improved.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the scope of the present application. It will be apparent that the described embodiments are merely some, but not all, embodiments of the application. Based on these embodiments, all other embodiments that may be obtained by one of ordinary skill in the art without inventive effort are within the scope of the application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art may still combine, add or delete features of the embodiments of the present application or make other adjustments according to circumstances without any conflict, so as to obtain different technical solutions without substantially departing from the spirit of the present application, which also falls within the scope of the present application.
Claims (10)
1. The MIM forming process is characterized by comprising the following steps of:
the formula is selected: acquiring order information in real time, and determining a molding formula based on the order information, wherein the molding formula comprises at least one MIM raw material powder, at least one binder and process flow information;
and (3) raw material verification: sampling and detecting the formula raw materials to obtain MIM raw material powder and a binder meeting the requirements of a molding formula;
mixing: adding the MIM raw material powder and the binder which are qualified in detection into mixing production equipment in batches based on the process flow information, and uniformly mixing to obtain uniform feed;
injection molding: feeding the uniform feed obtained by mixing into an injection device, and controlling the injection device to perform injection molding based on process flow information to obtain a green part;
degreasing and sintering: and (3) placing the green part obtained by injection molding in a sintering furnace, and controlling the sintering furnace to carry out thermal degreasing and compact sintering based on the technological process information to obtain a compact workpiece.
2. The MIM molding process according to claim 1, wherein the acquiring order information in real time, determining a molding recipe based on the order information, comprises:
acquiring order information in real time, and generating a demand instruction of a workpiece required by a user based on the order information, wherein the order information comprises workpiece demand quantity, workpiece unit price information, delivery deadline information, workpiece performance index information and workpiece specification information; the demand instructions include performance demand instructions and raw material cost demand instructions;
determining a plurality of alternative workpiece formulas meeting the performance requirements of the order based on the performance requirement instruction matching;
acquiring the raw material cost, the raw material acquisition difficulty and the formula component volume ratio of each alternative workpiece formula, and calculating the raw material score of each alternative workpiece formula through a preset formula raw material score calculation formula;
and selecting the alternative workpiece formula with the highest raw material score to determine the alternative workpiece formula as a forming formula.
3. The MIM molding process of claim 2, wherein the determining a number of candidate workpiece recipes that meet the performance requirements of the order based on performance requirement instruction matching comprises:
collecting the existing raw material powder performance data to establish a raw material performance database, and periodically crawling raw material powder performance data disclosed in a public resource website to supplement the raw material powder performance data into the raw material performance database;
establishing a machine learning model, training raw material powder performance data in a raw material performance database through formula selection historical data to obtain a formula matching model, and periodically supplementing updated raw material powder performance data in the raw material performance database into the formula matching model for data supplementation and iterative training;
and matching a plurality of alternative workpiece formulas meeting the performance requirements of the order form based on the performance requirement instructions through a formula matching model.
4. The MIM molding process according to claim 3, wherein the raw material cost, raw material acquisition difficulty, and formula component volume ratio of each candidate workpiece formula are obtained, and the raw material score of each candidate workpiece formula is calculated by a preset formula raw material score calculation formula, specifically comprising the steps of:
accounting the raw material cost Y of each alternative workpiece formula based on the order information connection supply chain channel;
determining the acquisition modes of various raw materials in each alternative workpiece formula based on the order information connection supply chain channel, and further calculating the raw material acquisition difficulty score N of each alternative workpiece formula;
performing mixed simulation on each alternative workpiece formula to determine the volume fraction T of the raw material powder of each alternative workpiece formula unit workpiece;
calculating the raw material scores of all the alternative workpiece formulas according to a preset formula raw material score calculation formula, wherein the formula raw material score calculation formula specifically comprises the following steps:wherein H is i Scoring the raw materials of the ith candidate workpiece formulation, Y i The raw material cost for the formula of the ith candidate workpiece is set according to the total price of the order, y is the preset raw material cost standard, and P 1 Scoring coefficient for raw material cost, P 2 Scoring coefficients for volume fractions, and P 1 、P 2 Are set by the manager.
5. The MIM molding process of claim 4, wherein the accounting for raw material costs for each candidate workpiece recipe based on the order information docking supply chain channel comprises:
calculating and determining various raw material demand total amounts of all candidate workpiece formulas based on order information;
the method comprises the steps of connecting a supply chain channel to obtain the recent optimal unit price of various raw materials of each alternative workpiece formula;
calculating the raw material cost of each alternative workpiece formula through a preset raw material cost calculation formula, wherein the raw material cost calculation formula specifically comprises the following steps:
wherein n is the total quantity of raw material types and X of the formula of the alternative workpiece i Z is the total required amount of the ith raw material of the alternative workpiece formula i For the i-th raw material recent optimal unit price of the alternative workpiece formula, C is dynamically generated based on historical data for purchasing guarantee coefficients,and C>1。
6. The MIM molding process according to claim 5, wherein the dynamically generating purchase security coefficients based on historical data comprises:
acquiring historical production data of the alternative workpiece formula, and determining production loss coefficients of each production in the formula history, wherein the production loss coefficients are the ratio of the actual consumption of materials to the rated consumption of materials of qualified products;
drawing each production loss coefficient of the alternative workpiece formula based on a time axis to generate a loss curve, and calculating a predicted loss coefficient A of the alternative formula based on the loss curve through a preset loss prediction formula;
acquiring the near ten times of molding production data of an enterprise, acquiring the production loss coefficient of the enterprise, and taking an average value to obtain a reference loss coefficient B;
calculating the purchase guarantee coefficient of the alternative workpiece formula through a preset purchase guarantee coefficient calculation formula, wherein the purchase guarantee coefficient calculation formula specifically comprises the following steps: c=0.8a+0.2b+0.1.
7. The MIM molding process according to claim 6, wherein the pre-set loss prediction formula is specifically:
wherein a is m The production loss coefficient of the latest production of the alternative workpiece formula, m is the historical production times of the alternative workpiece formula, a j And (5) formulating the production loss coefficient of the jth production for the alternative workpiece.
8. The MIM molding process of claim 5, wherein the docking supply chain channel obtains recent optimal unit prices for the various raw materials for each candidate workpiece recipe, comprising the steps of:
connecting a supply chain channel to obtain the supply unit price E of each supplier of the target raw materials;
acquiring the freight F and the raw material tariffs G of each supplier of the target raw material;
calculating the actual unit price D of each supplier of the target raw material according to a preset actual unit price calculation formula, sequencing the actual unit price, and selecting the lowest actual unit price U as the optimal unit price Z of the target raw material, wherein the actual unit price calculation formula specifically comprises the following steps:
wherein D is the actual unit price of each supplier of the target raw material, and X is the required quantity of the target raw material in the alternative formula.
9. The MIM molding process according to claim 1, wherein determining the acquisition mode of each raw material in each candidate workpiece formulation based on the order information docking supply chain channel, and further accounting the raw material acquisition difficulty score N of each candidate workpiece formulation specifically comprises: determining the acquisition modes of various raw materials in each alternative workpiece formula based on an order information connection supply chain channel, wherein the acquisition modes comprise domestic purchase, trade priority purchase and other national purchase; and generating a raw material acquisition difficulty score N of each alternative workpiece formula according to a preset purchasing mode difficulty comparison table corresponding to the raw material acquisition mode.
10. The MIM molding process of claim 1, wherein: the performance demand instructions include one or more of a foundry performance demand, a forging performance demand, a welding performance demand, a cutting performance demand, a forming performance demand, and a heat treatment process performance demand, and the use performance demand includes one or more of a mechanical performance demand, a physical performance demand, and a chemical performance demand.
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