WO2001033393A2 - Procede, systeme et dispositif de stockage utiles pour evaluer la conception d'un produit - Google Patents

Procede, systeme et dispositif de stockage utiles pour evaluer la conception d'un produit Download PDF

Info

Publication number
WO2001033393A2
WO2001033393A2 PCT/US2000/029178 US0029178W WO0133393A2 WO 2001033393 A2 WO2001033393 A2 WO 2001033393A2 US 0029178 W US0029178 W US 0029178W WO 0133393 A2 WO0133393 A2 WO 0133393A2
Authority
WO
WIPO (PCT)
Prior art keywords
factors
responses
response
user
design
Prior art date
Application number
PCT/US2000/029178
Other languages
English (en)
Other versions
WO2001033393A3 (fr
Inventor
Rick N. Williams
Andrew Joseph Poslinski
John Kaminsky
Stephen Shuler
Nick Abbatiello
Mandar Chati
Jeff Lemonds
Garron K. Morris
Original Assignee
General Electric Company
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by General Electric Company filed Critical General Electric Company
Priority to AU12244/01A priority Critical patent/AU1224401A/en
Publication of WO2001033393A2 publication Critical patent/WO2001033393A2/fr
Publication of WO2001033393A3 publication Critical patent/WO2001033393A3/fr

Links

Classifications

    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]

Definitions

  • the invention relates to a method and system for evaluating a product design.
  • a product design may be represented by product factors (e.g., material, processing parameters, dimensions) that affect product responses (e.g., cost, performance).
  • the factors and responses define a design space.
  • Much of the above- described iterative cycle conventionally performed in the art is an attempt to locate a region in the design space in which product factors and product responses are within desired limits or constraints.
  • a system that accelerates the design process by allowing a designer to determine areas in the design space meeting design criteria.
  • An exemplary embodiment of the invention is a method for designing a product.
  • the method includes specifying a plurality of factors related to the product and specifying a plurality of responses affected by said factors.
  • a design of experiments routine is performed to generate design of experiments data relating at least one factor to at least one response.
  • Regression is performed to generate a transfer function in response to the design of experiments data.
  • the transfer function is optimized in response to user-defined optimization criteria to generate an optimized factor and an optimized response.
  • the optimized factor and optimized response are then displayed.
  • Another exemplary embodiment is a system for designing a product.
  • the system includes an interface for receiving a plurality of factors related to the product and a plurality of responses affected by the factors.
  • a design of experiments module performs a design of experiments routine to generate design of experiments data relating at least one factor to at least one response.
  • a regression module performs regression to generate a transfer function in response to the design of experiments data.
  • An optimization module optimizes the transfer function in response to user- defined optimization criteria to generate an optimized factor and an optimized response.
  • a visualization module displays the optimized factor and the op" ; mized response.
  • FIG. 1 is flowchart of a process for designing a product in an exemplary embodiment of the invention
  • FIG. 2 is a block diagram of a system for designing a product
  • FIG. 3 depicts an exemplary interface to an engineering design calculator
  • FIG. 4 depicts an exemplary interface for entering application factors
  • FIG. 5 depicts an exemplary interface for selecting materials
  • FIG. 6 depicts an exemplary interface for entering responses
  • FIG. 7 depicts an exemplary interface for entering manufacturing factors
  • FIG. 8 depicts an exemplary factor/response summary
  • FIG. 9 depicts an exemplary interface with a DOE module
  • FIG. 10 depicts exemplary design of experiments data
  • FIG. 11 depicts an exemplary interface for optimization
  • FIG. 12 depicts an exemplary interface for setting up a visualization
  • FIG. 13 depicts an exemplary visualization for two materials.
  • FIG. 1 is a flowchart of a process for designing a product
  • FIG. 2 is a block diagram of a product design system shown generally at 10. As the user goes through the process shown in FIG. 1, parts of the product design system 10 are utilized as described herein. As shown in FIG. 2, the product design system 10 includes a number of modules for performing certain functions during the design process.
  • FIG. 2 Shown in FIG. 2 are a quality function deployment (QFD) module 12, an engineering design calculator 14, a design of experiments (DOE) module 16, a regression module 20, an optimization module 22 and a visualization module 24.
  • Each module may be implemented through a software application implemented by a general purpose computer.
  • the modules may be implemented on a single general purpose computer and accessed by the user through a user interface 26. Alternatively, the modules may be implemented on a plurality of general purpose computers remotely located from each other.
  • the user interface 26 may access the various modules over a network 27 such as a local area network (LAN), wide area network (WAN), global network (e.g., Internet), etc.
  • the modules may be implemented on computers which act as servers for multiple client computers.
  • the user interface may include a user interface application (e.g., web browser) or interfacing with one or more servers that execute software applications corresponding to the modules shown in FIG. 2.
  • the process begins at step 30 where the user selects a desired task such as quality function deployment (QFD) at step 32, engineering calculations at step 34 or use of a design for six sigma (DFSS) toolset at step 36. If the user selects QFD at step 32, the
  • the QFD module 12 is accessed.
  • the QFD module 12 allows the user to perform a quality function deployment process in which process variables or product design parameters (often referred to as key control parameters (KCPs) or factors) are analyzed to determine effects on critical to quality parameters (CTQs) or responses.
  • KCPs key control parameters
  • CTQs critical to quality parameters
  • Conventional QFD applications software may be used to allow the user to define CTQs and analyze the interaction between KCPs and the CTQs.
  • FIG. 3 depicts an exemplary interface to the engineering design calculator 14 which is directed to performing calculations related to molding of plastic components.
  • the engineering design calculator 14 allows the user to select material through a select material icon 40. This connects the user to a database of plastics which includes parameters of the plastics such as cost, hardness, etc. The user can select different materials to view the effect that different materials have on certain responses or Y's. The user can also select a geometry for the molded plastic component as shown at geometry selection option 42.
  • the user then enters values 45 for factors 44 (or X's) related to the plastic component and the molding process.
  • the values 45 are then used to compute responses 46 (or Y's) which provide information such as cycle time and cost to the user.
  • the calculations which derive the responses 46 from the factors 44 are based on predetermined functions.
  • the engineering design calculator 14 performs calculations based on a single set of factors 44.
  • the engineering design calculator is used to generally determine the effect of factors 44 on responses 46, but more robust tools are used, as described herein, to optimize one or more responses 46.
  • step 48 the process flows to step 48 where the user enters application factors concerning the product to be manufactured.
  • the application factors define the product to be manufactured and generally will not vary with materials or processing parameters.
  • FIG. 4 depicts an exemplary user interface for entering the application factors. As shown in FIG. 4, the user can select a geometry at geometry selection area 70 and can specify values 73 for application factors 72.
  • the application factors shown in FIG. 4 are directed to a plastic part. It is understood that other types application factors may be used given the application and the invention is not limited to plastic components.
  • the user can also enter statistical data in addition to the value 73 for each application factor 72. As shown in FIG. 4, the user can enter a standard deviation 74, a low limit 76 and a high limit 78 for each application factor. One or more of the statistical data may be used in the design of experiments process described herein.
  • the user can specify that an application factor 72 be used in a design of experiments (DOE) by checking a design of experiments indicator 80. Typically, the user enters a low limit 76 and/or a high limit 78 if an application factor is to be used in a design of experiments.
  • the application factors 72 may also include one or more user-defined application factors 82. Several of the application factors 72 are predefined. The user-defined application factors 82 allow the user to enter an application factor that is not provided for in the predetermined application factors and have this user-defined application factor 82 considered in a subsequent design of experiments.
  • FIG. 5 is an exemplary interface for selecting materials.
  • the user can identify a material through a select material icon 86 which may direct the user to a database of commercially available materials. If the user selects a commercially available material, the material characteristics (cost, hardness, melt temperature, etc.) are contained in the database and are accessible during later stages of the design process.
  • the engineering design calculator 14, described above, may be used to help the user select appropriate materials for a particular application by providing responses 46 for a given material. Instead of selecting a predefined material, the user may define characteristics of a material that is not commercially available.
  • the user may define a custom material by entering material characteristics (cost, hardness, etc.) that are not realized by any commercially available material. This allows the user to design a product based on non-existing materials and evaluate whether the expense in generating the custom material is warranted.
  • FIG. 6 is an exemplary interface for entering responses 90.
  • the responses 90 represent parameters that the user may want to control or optimize.
  • the user can enter statistical data including a low limit 92, a target value 94 and a high limit 96.
  • the low limit 92, target value 94 and/or high limit 96 may all be used in the design of experiments process described herein.
  • the user can also define a type of optimization to be performed on a response 90 through an optimization indicator 98. As described herein, the system can determine factors so that one or more responses are optimized.
  • the optimization indicator 98 allows the user to define the type of optimization (e.g., minimize, maximize, meet a target value, etc.).
  • the user can designate that a response 90 be used in a subsequent design of experiments process by selecting a design of experiments indicator 100.
  • the responses 90 may also include one or more user-defined responses 102. Several of the responses 90 are predefined.
  • the user- defined responses 102 allow the user to enter a response that is not provided for in the predetermined responses and have this user-defined response 102 considered in the design of experiments and optimization steps described herein.
  • the responses shown in FIG. 6 are directed to a molding a plastic part. It is understood that other types of responses may be used given the application and the invention is not limited to plastic components.
  • FIG. 7 depicts an exemplary user interface for entering the manufacturing factors 108.
  • the manufacturing factors 108 represent factors in the manufacturing process that may be controlled or modified.
  • the user can specify a value 109 for manufacturing factors
  • the user can also enter statistical data in addition to the value 109 for each manufacturing factor 108. As shown in FIG. 7, the user can enter a standard deviation 110, a low limit 112 and a high limit 114 for each manufacturing factor 108. One more of the statistical data may be used in the design of experiments process described herein.
  • the user can specify that a manufacturing factor 108 be used in a design of experiments (DOE) by checking a design of experiments indicator 1 16. Typically, the user enters a low limit 112 and/or a high limit 114 if a manufacturing factor is to be used in a design of experiments.
  • the manufacturing factors 108 may also include one or more user-defined manufacturing factors 1 18. Several of the manufacturing factors 108 are predefined. The user-defined manufacturing factors
  • the manufacturing factors sho m in FIG. 7 are directed to a plastic molding process. It is understood that other types manufacturing factors may be used given the application and the invention is not limited to manufacturing of plastic components.
  • step 56 the user is presented with a factor/response summary such as that shown in FIG. 8.
  • the factor/response summary includes application factors 72, user-defined application factors 82, manufacturing factors 108 and user-defined manufacturing factors 1 18.
  • miscellaneous or other factors 122 may also be included which do not correspond to the categories of application factors, user-defined application factors, manufacturing factors and user-defined manufacturing factors.
  • factors as used herein, is intended to have a broad meaning and is not limited to the particular examples or categories described above.
  • Steps 48, 50, 52 and 54 are directed to a limited set of factors or responses and may help focus the user on specific aspects of the application.
  • An experienced user for example, may proceed directly to step 56 and enter factors
  • the ability to enter user-defined application factors, user-defined materials, user-defined responses and user-defined manufacturing factors allows the system 10 to simulate manufacturing of products based, in part, on hypothetical, user-defined data.
  • the factors, materials and responses, and their interrelationships may be defined based on existing simulation designs, empirical data, scientific analysis (e.g., thermodynamics, physics) and hypothetical, user-defined data. This provides a powerful tool for the designer in that user-defined data can be entered along with established data.
  • the design of experiments, transfer function generation and optimisation, described herein, is performed in response to the user-defined data.
  • the factor/response summary also includes responses 90 and user-defined responses 102. As shown in FIG. 8, a value 126 may be calculated for responses 90 and user-defme responses 102. The calculations are performed by the engineering design calculator 14. This provides the user with a general indication of how factor values effect response values. If the user wants to determined how changes in a factor effect a response, the user must alter the value of a factor and instruct the engineering design calculator to recalculate the responses. The user may view the factor/response summary and determine that certain responses (e.g., total cost) are too far from desired values and return to prior steps, such as material selection to effect the response. To optimize responses, more sophisticated tools are used as described herein.
  • certain responses e.g., total cost
  • FIG. 9 depicts an exemplary user interface with the DOE module 16 for initiating a design of experiments.
  • the DOE module 16 is a design of experiments software application as described above.
  • the DOE module 16 may be implemented using commercially available design of experiments software applications.
  • the user sets up the design of experiments by selecting a DOE type through DOE type icons 130.
  • the user can select a default DOE, launch a DOE advisor to help select the appropriate DOE or specify a custom DOE.
  • the user is also presented with an identification of the materials 132, factors 134 and responses 136 that are to be considered in the design of experiments as selected by the user through DOE indicators.
  • step 58 flow proceeds to step 60 where the design of experiments data is generated.
  • the DOE module 16 performs the design of experiments process to generate design of experiments data.
  • FIG. 10 depicts exemplary design of experiments data.
  • the design of experiments module 16 perturbs the factors 134 to assume values within a range defined by a low limit and a high limit and obtains values for responses 136.
  • the low limit and high limi . may be taken from the appropriate application factors or the manufacturing factors entered by the user through steps 48 and 54, respectively.
  • Design of experiments data is generated for each material 132 identified in the DOE setup step 58. For each material, a design space is generated corresponding to the relationship between factors and responses.
  • the user can select a Perform DOE icon 137. This initiates the DOE process in which values are determined for each response 136.
  • the user can also select a portion of the DOE data for computation of values by selecting the Perform Area icon 139.
  • the user can then select a subset of the DOE data (e.g., lines 1-3) and determined values for responses 136 for only this subset of DOE data.
  • the DOE module determines the values for responses 136 by calling one or more other application modules.
  • the Melt Pressure to Fill may be calculated by an engineering design module 17 (e.g., software application) that is initiated by the DOE module 16.
  • the engineering design module 17 returns the value for Melt Pressure to Fill and this value is added to the DOE data.
  • the Total Cycle Time may be derived by another software module such as a molding simulation module 19.
  • the modules used to derive values for responses 136 may have access to all the factors provided by the user.
  • the modules called by the DOE module 16 to obtain values for responses can be established by the user or a system administrator. Alternatively, certain DOE responses 136 are determined by experimental data and thus, the user must enter the responses 136 based on experimental data.
  • step 62 one or more transfer functions are generated which mathematically relate the factors 134 to responses 136 for each material 132.
  • the regression module 20 performs regression on the design of experiments data to generate the transfer functions which mathematically relate the factors 132 to the responses 136 for each material.
  • the transfer functions may bt stored in a transfer function database 21 for use in subsequent applications.
  • step 64 optimization is performed. Optimization is performed by optimization module 22.
  • the user defines the type of optimization through a user interface such as that shown in FIG. 1 1.
  • the user can optimize one or more responses 136 in multiple ways using an optimization indicator 98.
  • the user can enter low limit 92, target value 94, high limit 96 as described above with respect to FIG. 6. These values may be carried over from step 52 where the responses 136 were identified by the user or modified by the user. For example, as shown in FIG. 11, the user has indicated that the Melt Pressure to Fill to be minimized, the Cycle Time be a predetermined target value and the Total Cost be minimized.
  • the optimization module 22 uses the transfer functions generated by the regression module 20 and determines the appropriate values for factors 134 to optimize the responses 136 as identified by the user. In addition, the optimization module 22 can determine statistical factors such as defects per million opportunity (DPMO) 150. A defect occurs when a response value exceeds an upper or lower limit.
  • the DPMO value can be used to generate a Zst value which is commonly used in the six sigma design process to evaluate designs. Based on normal distributions, a DPMO value of 3.4 equals a Zst score of 6 meaning that the design meets the six sigma quality standards.
  • Additional constraints 152 on the optimization can entered which will impose further limits on the optimization beyond those defined by optimization indicators 98.
  • the user may specify that the product of Mold Temperature and Melt
  • Pressure to Fill be less than a predetermined value.
  • the user enters this constraint in the additional constraints field 152 by entering a mathematical representation of the constraint and selecting a optimize indicator 154.
  • the constraint serves as a boundary in the design space preventing the optimization mod _le from producing a solution that violates the constraint.
  • Additional optimization may be performed through the other optimization field 160.
  • the optimization performed on responses 136 assumes that all three responses are equally important to the user.
  • the other optimization field 160 allows the user to assign a weight to one or more responses 1 6 to generate a global transfer function and to perform optimization on the global transfer function. For example, if
  • Melt Pressure to Fill was three times more critical than Cycle Time (tcycle) and Total Cost (totalCost), the user may enter the following relationship in the other optimization field 160.
  • the meltP response has been modified by a weight (e.g., 3) to reflect its importance.
  • the optimization module 22 can then optimize on the variable Y.
  • the user requests this global optimization by defining the global transfer function in the other optimization field 160 and selecting an optimization indicator 161.
  • FIG. 12 depicts an exemplary user interface for setting up the visualization. The user can select the materials 132, factors 134 and responses 136 which are to be displayed and select the type of display through a visualization identifier 140.
  • FIG. 13 depicts an exemplary visualization for two materials 132. Each of the responses 136 is plotted against each factor 134 for each material. Since two materials were specified in the visualization setup in FIG. 12, two plots are presented on each graph.
  • Each graph also includes the optimization data entered by the user in the optimization step 64. For example, as shown in the plot of Melt Pressure to F ;, 1 (meltP) versus Melt Temperature (meltTemp), a horizontal line is provided at the upper limit of 140 MPa specified by the user. The optimum value for Melt Temperature is shown as a vertical line at 304.45 degrees C.
  • the user can see the optimum value for the Melt Temperature as determined by the optimization module 22 and the user can see that the Melt Temperature must remain above a certain value (approximately 290 degrees C) to have the Melt Pressure to Fill remain below the upper limit of 140 MPa.
  • the other plots in FIG. 13 may similarly depict the optimum value for a factor 134, a low limit 92 and a high limit 96.
  • the invention can be embodied in the form of computer-implemented processes and apparatuses for practicing those processes.
  • the present invention can also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention.
  • the present invention can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention.
  • computer program code segments configure the microprocessor to create specific logic circuits.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Marketing (AREA)
  • Geometry (AREA)
  • Game Theory and Decision Science (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • General Factory Administration (AREA)

Abstract

Une forme de réalisation représentative de la présente invention concerne un procédé d'évaluation de la conception d'un produit. Le procédé consiste à spécifier une pluralité de facteurs (44) relatifs au produit et à spécifier une pluralité de réponses (46) affectées par lesdits facteurs. Une structure de programme d'expériences est réalisée pour générer des structures de données d'expériences reliant les facteurs aux réponses. La régression est effectuée pour générer une fonction de transfert en réponse à la structure des données d'expériences. La fonction de transfert est optimisée en réponse aux critères d'optimisation définis par l'utilisateur pour générer un facteur optimisé et une réponse optimisée. Le facteur optimisé et la réponse optimisée sont ensuite présentés sur un écran.
PCT/US2000/029178 1999-10-29 2000-10-20 Procede, systeme et dispositif de stockage utiles pour evaluer la conception d'un produit WO2001033393A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU12244/01A AU1224401A (en) 1999-10-29 2000-10-20 Method, system and storage medium for evaluating a product design

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US16238899P 1999-10-29 1999-10-29
US60/162,388 1999-10-29
US59528100A 2000-06-15 2000-06-15
US09/595,281 2000-06-15

Publications (2)

Publication Number Publication Date
WO2001033393A2 true WO2001033393A2 (fr) 2001-05-10
WO2001033393A3 WO2001033393A3 (fr) 2002-02-21

Family

ID=26858714

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2000/029178 WO2001033393A2 (fr) 1999-10-29 2000-10-20 Procede, systeme et dispositif de stockage utiles pour evaluer la conception d'un produit

Country Status (2)

Country Link
AU (1) AU1224401A (fr)
WO (1) WO2001033393A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1302878A2 (fr) * 2001-10-10 2003-04-16 Toyota Jidosha Kabushiki Kaisha Système et méthode pour la conception de produits et support d'enregistrement
US7219068B2 (en) * 2001-03-13 2007-05-15 Ford Motor Company Method and system for product optimization

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5253331A (en) * 1991-07-03 1993-10-12 General Motors Corporation Expert system for statistical design of experiments
EP0855661A2 (fr) * 1997-01-23 1998-07-29 Nhk Spring Co., Ltd. Méthode d'assistance à la conception d'une structure et similaires
EP0867233A1 (fr) * 1997-03-24 1998-09-30 Basf Corporation Méthode et dispositif de configuration pour un équipement de peinture

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5253331A (en) * 1991-07-03 1993-10-12 General Motors Corporation Expert system for statistical design of experiments
EP0855661A2 (fr) * 1997-01-23 1998-07-29 Nhk Spring Co., Ltd. Méthode d'assistance à la conception d'une structure et similaires
EP0867233A1 (fr) * 1997-03-24 1998-09-30 Basf Corporation Méthode et dispositif de configuration pour un équipement de peinture

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JACK P.C. KLEIJNEN: "Sensitivity Analysis and Optimization in Simulation: Design of Experiments and Case Studies" PROCEEDINGS OF THE 1995 WINTER SIMULATION CONFERENCE, 3 - 6 December 1995, XP002181266 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7219068B2 (en) * 2001-03-13 2007-05-15 Ford Motor Company Method and system for product optimization
EP1302878A2 (fr) * 2001-10-10 2003-04-16 Toyota Jidosha Kabushiki Kaisha Système et méthode pour la conception de produits et support d'enregistrement
EP1302878A3 (fr) * 2001-10-10 2006-01-25 Toyota Jidosha Kabushiki Kaisha Système et méthode pour la conception de produits et support d'enregistrement

Also Published As

Publication number Publication date
WO2001033393A3 (fr) 2002-02-21
AU1224401A (en) 2001-05-14

Similar Documents

Publication Publication Date Title
US6725112B1 (en) Method, system and storage medium for optimizing a product design
US8768500B2 (en) Manufacturing design and process analysis system
US7239991B2 (en) Manufacturing design and process analysis and simulation system
KR100451603B1 (ko) 능력예측기
US9037444B2 (en) Energy usage in injection molding simulations
Mourtzis et al. Equipment design optimization based on digital twin under the framework of zero-defect manufacturing
US7672745B1 (en) Manufacturing process analysis and optimization system
US6535775B1 (en) Processor system and method for integrating computerized quality design tools
US6675135B1 (en) Six sigma design method
Atilano et al. Lean Design-for-X: Case study of a new design framework applied to an adaptive robot gripper development process
US6351680B1 (en) Method for quality function deployment
US6377908B1 (en) Method for optimizing transfer function outputs
US6766205B1 (en) Method, system and storage medium for providing network based optimization tools
WO2001033393A2 (fr) Procede, systeme et dispositif de stockage utiles pour evaluer la conception d'un produit
JP5775803B2 (ja) 製造ラインシミュレーションモデル構築方法、製造ラインシミュレーションモデル構築装置、及び製造ラインシミュレーションモデル構築プログラム
Battini et al. Buffer design for availability: a new simulative study in case of infant and random failures
JP7080199B2 (ja) 作業工数予測システムおよび作業工数予測システムを備える生産計画立案支援システム
US10929904B1 (en) Automated fabrication price quoting and fabrication ordering for computer-modeled structures
CN115827051A (zh) 一种软件集成管理系统、方法及服务器
Gunara et al. Comparison Of Jenkins Box Method And Multiple Linier Regression In Predicting The Noble Metal Price
Sherbelts et al. The methods and benefits of establishing a process window
Gunara et al. Comparison Of Jenkins Box Method And Multiple Linier Regression In Predicting The Noble Metal Price: Comparison Of Jenkins Box Method And Multiple Linier Regression In Predicting The Noble Metal Price
Bernard et al. Quotation for the value added assessment during product development and production processes
Cappelletti et al. Design optimization for environmental sustainability: a framework to select the best eco-design strategy
Elaskari et al. Introducing the Central Backup Cellular Manufacturing System (CBCMS)

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A2

Designated state(s): AL AM AT AU AZ BA BB BG BR BY CA CH CN CU CZ DE DK EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT UA UG UZ VN YU ZW

AL Designated countries for regional patents

Kind code of ref document: A2

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
AK Designated states

Kind code of ref document: A3

Designated state(s): AL AM AT AU AZ BA BB BG BR BY CA CH CN CU CZ DE DK EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT UA UG UZ VN YU ZW

AL Designated countries for regional patents

Kind code of ref document: A3

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase in:

Ref country code: JP