WO2014087427A1

WO2014087427A1 - Method and system for computational design and modeling

Info

Publication number: WO2014087427A1
Application number: PCT/IN2013/000745
Authority: WO
Inventors: Sreedhar Sannareddy Reddy; Smita Subhash GHAISAS; Purushottham Gautham BASAVARSU; Amarendra Kumar Singh; Manoj BHAT; Prasenjit Das; Raghavendra Reddy Yeddula; Sapankumar Hiteshchandra SHAH; Prabhash KUMAR; Nagesh Hari KULKARNI
Original assignee: Tata Consultancy Services Limited
Priority date: 2012-12-05
Filing date: 2013-12-04
Publication date: 2014-06-12

Abstract

The present subject matter discloses a method and a system for computational design and modeling. The method includes, identifying, by a processor (202), requirements and desired properties of a product being developed based on a product development request. One or more processes and materials suitable for developing the product are determined using a materials ontology, a product and process ontology instance, material knowledge elements, and product and process knowledge elements based on the identification. Processing of the material is simulated for developing a simulated product using one or more simulation tools based on the determination. Further, the processor (202) ascertains whether the simulated product meets the requirements and the desired properties. Further, a knowledge base (108) having knowledge and ontology instances corresponding to the material, the process, and the simulation tool is updated based on the assertion for being used for design and modeling of the product and similar products.

Description

METHOD AND SYSTEM FOR COMPUTATIONAL DESIGN AND MODELING

FIELD OF INVENTION

[0001] The present subject matter relates to methods and systems for design modeling and, particularly but not exclusively, to methods and systems for computational design and modeling.

BACKGROUND

[0002] Generally, while designing a new product, materials, and processes to be used for manufacturing the product are identified based on requirements of the product. In case materials suitable to the requirements of the product are not available, either existing material may be processed in a new way or new materials may be developed. For instance, in case the known materials do iot have a particular property, such as strength required for the product to work efficiently within the limits on the weight, new materials may be designed or existing materials may be processed in a new way to achieve the required strength. New material may be defined as a material that has significantly new properties achieved because of changes in either its chemistry and/or its internal structure. Designing such new materials or processes, however, is usually a time consuming and cumbersome process due to various trials and experimentations involved in its design and development. The designing of the new materials usually requires developing a suitable composition for the material and designing an efficient processing route for manufacturing the material to impart appropriate internal structure and properties to the material.

BRIEF DESCRIPTION OF THE FIGURES

[0003] The detailed description is described with reference to the accompanying figures.

In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

[0004] Figure 1 illustrates a network environment implementing a design modeling system in accordance with one embodiment of the present subject matter;

[0005] Figure 2 illustrates a design modeling system, in accordance with an embodiment of the present subject matter; and

[0006] Figure 3 shows a method for computational design and modeling of materials, products, and processes, according to an embodiment of the present subject matter.

[0007] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DESCRIPTION OF EMBODIMENTS

[0008] The present subject matter relates to methods and systems for computational design and modeling of materials, products, and manufacturing processes. Selection of materials for product manufacturing plays a pivotal role in the process of developing a new product. The chemistry and internal structure of materials have significant effect on various properties, such as strength of the product. Thus selecting or designing a suitable material for the product requires careful attention. Further, apart from selecting and/or designing the material, processing of the material may also result in variation in properties of the material. For instance, same material when heated at different temperatures may attain different properties, such as strength, impact resistance, fatigue life, and surface texture. Thus, developing a new product may become a time consuming and cost intensive process due to the designing of the new material and their processing techniques as such designing involves various experiments and trials.

[0009] Conventional approaches for designing the new materials or their processing techniques involve utilizing knowledge and experience of various researchers and design engineers and existing design related literature, such as research data and experimental data available online or in in-house databases or files. Studying such a huge volume of data is however a time consuming task, thus delaying the design process. Further, mere study of such literature may not provide actual relationship between different properties of the material and thus may not be sufficient for designing new materials. Further, such literature may also not be useful in designing new processing techniques for the materials for which experiments and trials are still to be performed.

[0010] Another conventional technique involves providing a platform for simulation and optimization of materials processing. Such platforms typically facilitate use of simulation tools for particular process workflows. However, in the absence of knowledge engineering and learning capabilities, such platforms lack capabilities to provide guidance and process learning thus failing to provide efficient techniques for material, product, and process design.

[0011] According to an embodiment of the present subject matter, systems and methods for computational design and modeling of materials, products, and processing techniques are disclosed. The systems and methods are based on domain knowledge of materials, processing techniques, i.e., material manufacturing and processing methods, internal structure of material, material properties, and products developed using the material. In one implementation, the domain knowledge is organized in the form of knowledge elements, interchangeably referred to as knowledge, and ontologies stored in a knowledge database, interchangeably referred to as knowledge base. Providing the ontologies helps in ensuring that all data, such as properties and parameters related to materials and processes are available directly. Further, capturing associations between various entities, such as the materials and the processes; and the material properties and the internal structure of material helps in identifying suitable materials and processes for developing a product. Additionally such associations may further help in designing new materials and processes. Further, combining the ontology based knowledge database, simulation models, online databases, and system for real time processing and updating of the knowledge base helps in providing an integrated framework for the design modeling.

[0012] The systems and methods can be implemented in a variety of computing devices.

The computing devices include, but are not limited to, desktop computer, hand-held device, cloud servers, mainframe computers, workstation, multiprocessor system, laptop computer, network computer, minicomputer, server, and the like.

[0013] In one implementation, knowledge corresponding to material, product and process, and simulation models may be organized as rules, cases, equation, models and so on, expressed in terms of the common vocabulary provided by the ontology. In another implementation, a specification mechanism may be provided to compose knowledge elements spanning multiple knowledge representation mechanisms, such as rules, cases, equations, models and so on, and using domain ontology as a means to integrate reasoning across these mechanisms. As will be understood, ontology refers to a common vocabulary for people who need to share information within a domain. The ontology contains machine-interpretable and human readable definitions, called ontology instances, of basic concepts of the domain and the relations among these basic concepts. The ontology instances, in one example, may be created using a resource description framework (RDF)-web ontology language (OWL) schema. In one example, three types of ontologies, namely, material ontology, product and process ontologies, model ontology and the relationships between these ontologies are used. In one implementation, these ontologies are generalized in a manner as to be able to specify new processes, models, tools, etc., as ontology instances, thus extending the capabilities of the platform without having to hardwire them into the platform. As mentioned before, the ontology instances are stored in the knowledge base associated with a design modeling system.

[0014] Material ontology instance includes material data, i.e., data related to materials, internal structure of material, and material properties. Materials may be understood as different materials, such as steel, aluminum, wood, plastic, that may be used for manufacturing a product and may be further classified into form of material, such as bar, sheet, powder, pellets, and billet and state of material, such as solid, liquid, and gaseous. Material properties may be understood as properties of the materials, such as strength and corrosion resistance and may be further classified into mechanical, physical, thermal, chemical, electrical, biological, etc. Internal structure of the material may include, for example, bulk phases, such as Ferrite, Martensite, Austenite, Cementite, Pearlite, and Bainite; inclusions; dislocations; and precipitates in the case of steels. Each of these bulk phases may have attributes, such as sub-phases, phase composition, phase percentage, and phase distribution and may be associated with morphology, such as lath and plate; atomic arrangement, such as crystalline and amorphous. The material ontology instance further captures relationships between the materials, the internal structure of material, and the material properties. For instance, it may capture what all properties and internal structure of material may be associated with a particular material and the relationship between these entities. The material ontology instance thus helps in providing data about materials using which materials suitable for a product may be identified, for example, by the system and a product engineer using the system. Further, providing relationship between the materials, the material properties, and the internal structure of material may help in identifying properties or compositions of existing materials that may be modified for designing new materials.

[0015] Product and process ontology instance includes data related to various products that may be manufactured using the materials and various processing techniques that may be carried out for manufacturing of the material and the product. Product data may include product related information, such as geometry, weight, volume, area, and strength that may be useful for developing the product and also identifying materials and the process for manufacturing the product. Further, processing techniques may be classified into primary manufacturing techniques, shaping processes, fabrication processes, etc. Model ontology instance includes various simulation and approximation models on a variety of phenomena at different levels of precision that may be used for testing materials and products in simulated real time environments.

[0016] In one implementation, the design modeling system is configured to interact with the knowledge base for designing the materials and the processing techniques. For instance, on receiving a request for designing a product or a material, the design modeling system interacts with knowledge base to identify and initiate the process(es) that may be followed for developing the product. Initially, requirements and desired properties of the products to be designed are identified. For instance, for manufacturing a steel mill product, initially, requirements and properties of the product, such as strength, fatigue life, and surface texture ma be identified.

[0017] Based on the properties and the requirements of the products, suitable materials, i.e., materials meeting requirements of the products may be ascertained using material knowledge elements and the material ontology instance. For example, based on the product requirements, a set of material selection rules may be determined from the material knowledge elements for ascertaining a suitable steel material, such as a rod, or a sheet of a particular strength, grade, etc. Subsequently, the material may be processed using one or more processes determined using product and process knowledge elements and the process ontology instances. The product and process knowledge elements may include a set of rules or a decision tree using which the product and process ontology instance may be determined and used. In one implementation, the processing of the material may be simulated using one or more simulation models determined using the model ontology instance. For instance, the design modeling system may interact with the knowledge base to ascertain what all processing steps need to be followed to develop the product using the material so that the simulation tools may follow the same steps while simulating the product. For example, in case of the steel product, say, a gear, it may be determined that a piece cut from a steel bar needs to be heated to a particular temperature. The heated piece may then be forged to obtain an intermediate shape and heated up to a specific temperature in a controlled environment. The heated piece in the intermediate shape may further be cooled in a specific medium to achieve desired properties and further machined to obtain the final shape of the gear. The design modeling system may subsequently use the simulation model determined using the model ontology instance to simulate the determined process for developing a simulated product. [0018] The simulated product may be analyzed and verified to determine whether the product meets, the desired requirements of the design modeling system. In one implementation, a global state memory may be used to track evolution of the product during the manufacturing process simulation. Thus, in order to verify the state of the product and its properties at any point of time during the simulation process, the global state memory may be checked to ascertain the current state of the product and compare the current state with its expected state. In case the product does not meet the desired requirements, the design modeling system may interact with the knowledge base to obtain the material knowledge elements and the product and process knowledge elements for assisting the user or to make decisions to modify the material ontology instances, and in turn the material, and or the process ontology instances, and in turn the process, used for the product development. Modified processes and materials thus designed may be used for further simulations of the product till the desired properties in the global state are achieved. On achieving the desired requirements, the process may be finished and details of the materials and the process followed may be saved for utilization during actual manufacturing of the products. Modified processes and materials thus designed may be saved in the product and process ontology instance and materials ontology instance, respectively. The knowledge base may be thus updated regularly to facilitate development of similar products in future. Also, a learning engine may mine the data produced by simulation processes to extract useful patterns, rules, and models and store them in the knowledge base.

[0019] Further, the design modeling system may be configured to interact with various online databases and publications to obtain data related to the materials and the processes for updating the knowledge base. Further, the design modeling system may be configured to act as an interface and collaboration tool for users, such as researchers and scientists and may take their inputs for updating and modifying the knowledge saved in the knowledge base, thus updating the knowledge base in real time.

[0020] Further, the said material, product and process ontology instances are modeled in a manner that enables new material systems, product categories and manufacturing processes to be created as ontology instances, thus providing an extensibility capability to the platform whereby support for new materials, products, processes, and simulation models can be easily added to the platform.

[0021] The systems and methods of the present subject matter thus facilitate in designing materials, processes, and products utilizing considerably less time and money. The integrated framework facilitates not only in providing an intelligent system that helps in design modeling of materials, products, and manufacturing based on the engineering knowledge and simulation models, but also ensures that the same system may be used for design modeling across various engineering fields. Further, providing the integrated platform helps in ensuing that the system can be used for end-to-end process starting from designing/manufacturing a material to designing a product with minimal human interference as compared to conventional way of simulating a manufacturing process where data transfer across tools are done manually. The platform also enables integration of mathematical models across various length scales through sharing of variables, parameters, etc., between mathematical models of various processes. Further, providing the integrated platform may help in achieving various other functionalities, such as introduction of new grades of material, process scale-up, etc.

[0022] These and other advantages of the present subject matter would be described in greater detail in conjunction with the following figures. While aspects of described systems and methods for designing materials and processing techniques can be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system(s).

[0023] Figure 1 illustrates a network environment 100 implementing a design modeling system 102 configured to design materials, products, and processing techniques based on domain knowledge of at least materials, internal structure of material, material properties, processing techniques, i.e., material manufacturing and processing methods, and products developed using the materials, according to an embodiment of the present subject matter. In one embodiment, the network environment 100 includes a network 104 for enabling communication between the design modeling system 102 and a plurality of user interface devices 106-1, 106-2, ... , 106-N, hereinafter referred to as user interface device(s) 106.

[0024] The network 104 may be a wireless network, a wired network, or a combination thereof. The network 104 can also be an individual network or a collection of many such individual networks, interconnected with each other and functioning as a single large network, for example, the Internet or an intranet. The network 104 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and such. Further, the network 104 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other.

[0025] Further, the user interface devices 106 may be one or more computing devices, such as mainframe computers, workstations, personal computers, desktop computers, minicomputers, servers, multiprocessor systems, laptops, a cellular communicating device, such as a personal digital assistant, a smart phone, and a mobile phone, and the like. In one implementation, the design modeling system 102 may be one or more computing devices, such as a desktop computer, hand-held device, cloud servers, mainframe computers, workstation, a multiprocessor system, a hand-held device, a personal digital assistant (PDA), a smart phone, a laptop computer, a network computer, a minicomputer, a server, and the like.

[0026] Alternatively, the design modeling system 102 may also be implemented as multiple servers to concurrently perform a number of tasks. In an example the design modeling system 102 may work on a web based platform to facilitate collaborations between various stakeholders, for example, material scientists, product and process designers and engineers, researchers, etc., interacting with the design modeling system 102 through the user interface devices 106. In other implementations, the design modeling system 102 may work on other similar platforms.

[0027] The network environment 100 further comprises a knowledge base 108 associated with the design modeling system 102. The knowledge base 108 includes data utilized for the functioning of the design modeling system 102. It will be understood that although the knowledge base 108 has been shown external to the design modeling system 102; however, the knowledge base 108 may be located within the design modeling system 102.

[0028] In one implementation, the knowledge base 108 includes the data, such as knowledge elements in the form of ontology instances of a plurality of ontologies. In one implementation, the knowledge base 108 may include material knowledge elements, product and process knowledge elements, and models knowledge elements organized as rules, cases, equation, models and so on, all expressed in terms of the common vocabulary provided by the ontology. In another implementation, a specification mechanism may be provided to compose knowledge elements spanning multiple knowledge representation mechanisms, such as rules, cases, equations, models and so on, and using domain ontology as a means to integrate reasoning across these mechanisms.

[0029] The material knowledge elements include various rules and guidelines that may be used for selecting the right material for a given product requirement. The material knowledge elements may include knowledge about materials, their chemical compositions, their properties, and what variations in compositions can achieve what properties of the material and under what processing conditions. Similarly, knowledge about what structural feature of a material gives rise to what properties is also added in the material knowledge elements. For example, martensitic phase for medium carbon steels gives a high hardness property.

[0030] The process knowledge elements include various rules and guidelines that may be used for selecting right processes to be performed on a material in order to achieve right properties in a material. The process may also include testing processes apart from manufacturing processes, for example, a process for performing fatigue test on a manufactured component. The process knowledge elements may include relation between process and structural features of a material, for example, the process knowledge element may include knowledge about what process causes what structural features in a material and what variations in process parameters cause what variations in these structural features.

[0031] The product knowledge elements include various rules and guidelines that may be used for selecting the right product features and right product configuration to meet the product requirements. The product knowledge elements include knowledge about features and configurations to be provided in a product, procedures for its design and design validation. [0032] The model knowledge elements include various rules and guidelines that may be used for selecting the right model to simulate a process phenomenon. The model knowledge elements include knowledge about which simulation models may be used for performing which processes, developing which materials, and etc. For instance, to simulate fluid flow, the model knowledge elements include may suggest use of a laminar flow model or a turbulent flow model, with the selection of either of these being dependent on process parameters, such as velocity, density, viscosity, etc.

[0033] An ontology defines a common vocabulary for users who share information within a domain. A domain may be understood as a field of knowledge, for example, materials and processes may be the domain in the present case of material and process designing. The ontologies are typically defined using various ontology instances which include machine- interpretable and human readable definitions of basic concepts of the domain and the relations among these basic concepts. The ontologies, in one example, may be created using a resource description framework (RDF)-web ontology language (OWL) schema. The ontology instances, in accordance with one embodiment of the present subject matter, include, but are not limited to, material ontology instance, product and process ontology instance, and model ontology instance.

[0034] The material ontology instance may be defined as ontology instance having data related to materials, internal structure of material, and material properties for use in manufacturing the product. Materials may be understood as different categories of materials, such as steel, aluminum, wood and plastic used to manufacture products or different manifestations of each category, such as specific type of alloy of steel or blend of plastic. The materials may be further classified into form of material, such as bar, sheet, powder, pallets, pellets, and billet and state of material, such as solid, liquid, and gaseous. Material properties may be defined as properties, such , as strength and corrosion resistance and may be further classified into mechanical properties, physical properties, thermal properties, chemical properties, electrical properties, biological properties, etc. Further, each property may have a measurable value or range of values and associated units of measurement. Internal structure of the material may include, for example, bulk phases, such as amorphous phase, Ferrite, Martensite, Austenite, Cementite, Pearlite, and Bainite; inclusions; dislocations; and precipitates. Each of these items may have specific attributes; for example, bulk phases may have attributes, such as phase percentage and phase distribution and may be associated with morphology, such as lath and plate; atomic arrangement, such as crystalline and amorphous; and composition.

[0035] The material ontology instance further captures associations between the materials, the internal structure of material, and the material properties. For instance, it may capture what all properties and the internal structure of material may be associated with a particular material. For example, for AISI 8620 steel the knowledge base may associate the properties, such as high strength and rust free and material composition, such as carbon having weight percentage in the range of about 0.18% to 0.23% and chromium having weight percentage in the range of about 0.4% to 0.6%. The material ontology instance thus helps in providing data about available materials using which materials suitable for a product may be identified, for example, by the design modeling system 102 and a product engineer interacting with the design modeling system 102 through the user interface devices 106. Further, providing associations, such as relationships between the materials, the internal structure of material, and the material properties may help in identifying properties or compositions of existing materials that may be modified for designing new material. [0036] Product and process ontology instance may be defined as ontology instance describing data related to various products that may be manufactured using at least the materials and various processing techniques that may be carried out for manufacturing of the material and the product by the design modeling system 102. Product data may include product related information, such as geometry, weight, volume, area, operating environment and loads, and performance targets that may be useful for developing the product and also identifying materials and process for manufacturing the product. Further, processing techniques may be classified into primary manufacturing techniques, shaping processes, fabrication processes, etc. For instance, for manufacturing a gear, the product and process ontology instance may include data about all processes used for manufacturing the gear beginning from manufacturing of the material, say, steel used for the gear, through steel making, casting, rolling up to carburization and quenching processes, and finish machining while simulating manufacturing of the gear. The product and process ontology instance may further include various sub-processes that may be used while performing each step of the gear manufacturing process. Model ontology instance includes various simulation and approximation models that may be used by the design modeling system 102 for testing materials and products in simulated real time environments.

[0037] In one implementation, the design modeling system 102 is configured to interact with the knowledge base 108 for designing the materials and the processing techniques. For instance, on receiving a product development request for designing a product or a material from a user through the user interface devices 106, the design modeling system 102 may interact with the knowledge base 108 to identify and initiate the process that may be followed for developing the product. For the purpose, the design modeling system 102 includes a process execution module 110, a process design module 112, and a knowledge base interaction module 114. In one implementation, the process design module 1 12 and the process execution module 110 are configured to interact with the knowledge base 108 for accessing the ontologies and knowledge elements while designing the material, process, or the product for the user.

[0038] The process design module 1 12 may initially identify the requirements and desired properties of the products based on the data provided in the product and process ontology instance and product and process knowledge elements. The process design module 112 may subsequently determine suitable materials, i.e., materials meeting requirements of the products using the material ontology instance and the product and process knowledge elements. Further, the process execution module 1 10 may simulate the processing · of the selected material undergoing one or more processes determined by the process design module 112 using the product and process ontology instance and the product and process knowledge elements. In one implementation, the process execution module 1 10 may simulate processing of the material using one or more simulation models determined using the model ontology instance and model knowledge elements. For the purpose, the process execution module 1 10 may interact with the knowledge base 108 to ascertain what type of simulation models or tools may be used for developing the product. The process execution module 110 may use the simulation model determined using the model ontology instance and model knowledge elements to simulate the determined process for developing the simulated product, i.e., a virtual product having same properties as that of the product that the user desires to manufacture, for example, in a factory.

[0039] The process execution module 110 may further analyze the simulated product to determine whether the product meets the desired requirements stored in a global state memory of the design modeling system 102. Based on the analysis, the knowledge base interaction module 1 14 may update the knowledge base 108. For instance, in case the product does not meet the desired requirements, the processes and the material may be modified to meet the desired product properties and the corresponding ontology instances and may be subsequently modified by the knowledge base interaction module 114. The knowledge base 108 may thus be updated to facilitate development of similar products in future. Further, using the design modeling system 102 to design the material and the product based on ontologies saved in the knowledge base 108 facilitates in developing products and designing new manufacturing material and processing techniques at minimal cost and very less time. Additionally, regular updation of the knowledge base 108 also ensures that the design modeling system 102 has access to all types of material and processes available till data for developing a product, a manufacturing material, or a processing technique.

[0040] Further, examples of the manufacturing industries where the design modeling system 102 may be employed includes, but is not limited to, original equipment manufacturers (OEMs), suppliers of parts to OEMs, materials industries such as steel producers, plastics producers, and other manufacturing industries, consumer goods industries, semiconductor and chipset manufacturing industries, chemical industries, etc.

[0041] Figure 2 illustrates exemplary components of the design modeling system 102 in accordance with an embodiment of the present subject matter. In said embodiment, the design modeling system 102 includes one or more processor(s) 202, I/O interface(s) 204, and memory 206 coupled to the processor 202. The processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate signals based On operational instructions. Among other capabilities, the processor(s) 202 is configured to fetch and execute computer-readable instructions stored in the memory 206. [0042] The functions of the various elements shown in the figure, including any functional blocks labeled as "processor(s)", may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), non-volatile storage. Other hardware, conventional and/or custom, may also be included.

[0043] The I/O interface(s) 204 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, and an external memory. Further, the interfaces 204 may facilitate multiple communications within a wide variety of protocol types including, operating system to application communication, inter process communication, etc.

[0044] The memory 206 can include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non- volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0045] Further, the design modeling system 102 may include module(s) 208 and data

210. The modules 208 and the data 210 may be coupled to the processor(s) 202. The modules 208, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The modules 208 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulate signals based on operational instructions. In another aspect of the present subject matter, the modules 208 may be computer-readable instructions which, when executed by a processor/processing unit, perform any of the described functionalities. The machine-readable instructions may be stored on an electronic memory device, hard disk, optical disk or other machine-readable storage medium or non-transitory medium. In one implementation, the computer-readable instructions can be also be downloaded to a storage medium via a network connection.

[0046] In an implementation, the module(s) 208 include the process execution module

110, the process design module 1 12, the knowledge base interaction module 1 14, a data extraction module 212, a user interaction module 214, and other module(s) 216. The other module(s) 216 may include programs or coded instructions that supplement applications and functions performed by the design modeling system 102.

[0047] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the module(s) 208. The data 210 includes, for example, global state data 218, knowledge base interaction data 220, interaction data 222, extraction data 224, and other data 226. The other data 226 includes data generated as a result of the execution of one or more modules in the other module(s) 216.

[0048] As previously discussed, the design modeling system 102 is configured to facilitate designing of manufacturing material, processing techniques, and products based on one or more ontologies and their corresponding ontology instances and knowledge elements provided in the knowledge base 108. The process design module 112, on receiving the product development request from the user interface devices 106 for developing a product, a material, or a processing technique, may interact with the knowledge base 108 to obtain data, such as the process useful for developing the product, the material, or the processing technique. The process design module 1 12 may be configured to identify the process, start execution of the process, abort the execution of the process, and monitor status of the process execution. In one implementation, the process identification, execution, and monitoring of the process execution status may be carried out based on inputs from users of the design modeling system 102 received through a user interface device 106 driven by the user.

[0049] The process design module 112 may initially identify requirements and desired properties of the products using the product and process ontology instance of the knowledge base 108. For example, to develop a gear the process design module 1 12 may access the product and process ontology instance to determine requirements and properties that a user may desire in the gear. Further, the process execution module 1 10 may obtain specific requirements received from the user, through the user interface devices 106. In one implementation, the user interaction module 214 may be configured to interact with the user interface devices 106 to obtain the specific requirements. Based on the product and process ontology instance, the product and process knowledge, and the user interactions, the process design module 112 may obtain parameters, such as rated torque, rated speed, reduction ratio, weight, cost, and smoothness; and performance requirements, such as fatigue life that the developed product is desired to possess. The process design module 1 12 may save the product requirements and the desired properties thus obtained in the global state data 218. The global state data 218, in one implementation, may be understood as a global state memory used to track evolution of the product during the simulation process. Thus, in order to verify the state of the product and its properties at any point of time during the simulation process, the global state data 218 may be accessed to compare the current state of the product with its expected state. The expected state may be stored in other data 226 or received as an input from the user.

[0050] Further, the product requirements and desired properties, such as parameters and performance requirements obtained from the user may be saved in the knowledge base 108 thus updating the product and process knowledge elements for future purposes. In one implementation, the knowledge base interaction module Π4 may include a knowledge services module 228 and an ontology definition module 230 for updating the knowledge base 108. The ontology definition module 230 may be configured to update ontology instances, such as the product and process ontology instance, while the knowledge services module 228 may be configured to update knowledge elements, such as the product and process knowledge elements to enhance the functionality and efficiency of the design modeling system 102 and the knowledge base 108.

[0051] The process design module 1 12 may further determine processes and various rules and guidelines that may be followed while developing a product. In one implementation, the process design module 1 12 may access the knowledge base 108 to obtain the rules and guidelines provided by, for example, manufacturing associations, regulatory authorities, and the like. For instance, in the above example of gear designing, the process design module 112 may determine design parameters, such as module, number of teeth, face width, addendum, dedendum, strength, and hardness required by gear manufacturers association. In one implementation, the rules and guidelines may be stored in the knowledge base 108 as product and process knowledge elements separately from the product and process ontology instance. [0052] Based on the properties, the requirements, and the rules and guidelines, the process design module 112 may determine suitable materials, i.e., materials meeting requirements of the product using the material ontology instance and material knowledge elements. For example, based on the product requirements, the process design module 1 12 may ascertain materials having properties, such as strength and hardness meeting the properties and requirements of the product, say, the gear. Subsequently the process execution module 1 10 may simulate processing of the material using one or more processes provided in the product and process ontology instance. In one implementation, the process execution module 110 may simulate the process using one or more simulation models determined using the model knowledge elements. For instance, the process design module 112 may interact with the knowledge base 108 to ascertain what all processing steps need to be followed to develop the product using the material. For example, in the case of the gear, the process design module 112 may determine that the material may need to go under the processes of carburization and quenching for manufacturing the gears. In one implementation, the process steps may be ascertained based on inputs received through a user interface from users of the design modeling system 102. The process design module 1 12 may further ascertain various parameters, such as carburization potential, carburization time, carburization temperature, diffusion time, and diffusion temperature for performing the processes. In one implementation, the parameters may be determined based on one or more predefined rules provided in the product and process knowledge elements saved in the knowledge base 108. Further, the process design module 112 may save the parameters in the global state data 218.

[0053] The process execution module 110 may subsequently simulate the carburization and quenching process using a simulation model associated with the carburization and quenching process. In one implementation, relationships between the parameters, the rules for deciding the parameters, and the simulated model may be defined in the process and product knowledge elements and the model knowledge elements in the knowledge base 108. Additionally, the process execution module 110 may use the model ontology instance and the model knowledge elements to identify the simulation model appropriate for simulating the determined process. Further, the process execution module 110 may interact with one or more stimulation tools (not shown in this figure) interfaced with the design modeling system 102 to simulate the processing of the material using the simulation model to obtain the simulated product. In one implementation, the simulation tools may be interfaced as external plugins registered with the design modeling system 102. The user interaction module 214, in one implementation, may be configured to register the simulation tools. Further, the user interaction module 214 may save the registry details in the interaction data 222 for being used by the process execution module 110 to obtain the simulated product. Further, the process execution module 110 may simulate the one or more processes in an integrating manner by saving output of simulation of each of the one or more processes in the global state data 218 for being used for one or more subsequent processes from among the one or more processes.

[0054] The simulated product thus obtained may be analyzed and verified by the process execution module 110 to determine whether the simulated product meets the requirements and properties desirous of an actual product. In one implementation, the process execution module 110 may access the global state data 218 to determine whether the simulated product meets the requirements and properties desirous of the actual product. In one implementation, the process execution module 110 may access the global state data 218 to compare the requirements and the desired properties with properties of the simulated product at each step of simulation. In another implementation, the process execution module 1 10 may access the global state data 218 to compare the requirements and the desired properties with properties of the simulated product at end of the simulation process. Thus, using the global state data 218, the process execution module 1 10 may ascertain the points at which the simulation process led to deviation in properties of the product. The material or process may thus be accordingly redesigned either based on ontology instances or user inputs or both.

[0055] In case the process execution module 110 determines that the simulated product does not meet the requirements and the desired properties as stored in the global state data 218, either the material or the processing steps used for the designing may be modified. In one implementation, the knowledge base interaction module 1 14 may be configured to modify the material compositions and processes used for the simulation based on, for example, comparisons between results based on the specifications of the simulated product obtained during simulation and the requirement specifications of the product saved in the global state data 218. In one implementation, the knowledge services module 228 may be configured to modify the knowledge elements in the knowledge base 108, while the ontology definition module 230 may be configured to update the ontologies in the knowledge base 108. The knowledge base interaction module 114 may further access various other sources, such as online data and inputs received from users and experts, such as material scientists through the user interface devices 106. For instance, in the previous example of gears, if the simulated gear does not meet requirements, an additional step of manufacturing process may be added in the product and process ontology instance.

[0056] Modified processes and materials thus obtained may be used by the process execution module 1 10 for re-processing the product simulation using the modified processes and materials to obtain another simulated product. The process execution module 110 may thus continue the re-processing for further simulations of the product till the desired requirements and the desired properties stored in the global state are achieved; On achieving the desired requirements and properties, the process execution module 110 may store details of the final state of the simulated product in the global state data 218 and the knowledge base 108. Further, the knowledge services module 228 may use the modified materials and the process to update the material knowledge elements and the product and process knowledge elements, respectively. The knowledge services module 228 may thus update the knowledge base 108 regularly to facilitate development of similar products in future.

[0057] Further, the knowledge base 108 may be regularly updated based on inputs from various users and data obtained from various databases. For the purpose, the data extraction module 212 may be configured to access various online databases and publications to obtain data related to the materials and the processes based on regular researches and developments going on in the field of materials. The data thus obtained may be stored in the extraction data 224. Further, the user interaction module 214 may be configured to act as an interface for users, such as researchers and scientists for discussing the various developments in the field of materials and to collaborate for designing new processes and materials and modifying old processes and materials. The inputs thus received from the users may be stored in the interaction data 222 based on which the ontology definition module 230 may update the ontology instances saved in the knowledge base 108. The knowledge services module 228 may be further configured to analyze experimental data, obtained from either online sources or from in house databases of an organization, to obtain patterns, rules, and models for updating the knowledge base 108. The design modeling system 102 may further periodically add new ontology instances and knowledge elements corresponding to new materials, processes, products, and simulation tools to update the knowledge base 108. In one implementation, the new ontology instances and knowledge elements may be obtained from the extraction data 224 or the interaction data 222.

[0058] Figure 3 illustrates a method 300 for computational design and modeling of materials, products, and processes, according to an embodiment of the present subject matter. The order in which the method is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300 or any alternative methods. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the methods can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0059] The method(s) may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

[0060] A person skilled in the art will readily recognize that steps of the methods can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, for example, digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, where said instructions perform some or all of the steps of the described method. The program storage devices may be, for example, digital memories, magnetic storage media, such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover both communication network and communication devices configured to perform said steps of the exemplary methods.

[0061] At block 302, a product development request is received. In one implementation, the product development request is received from a user intending to design a product. For example, a design modeling system, such as the design modeling system 102 may receive the product development request from a user through the user interface device 106.

[0062] At block 304, requirements and desired properties of the product may be identified. The product and process ontology instance may be accessed to determine the properties and specifications that may be desired in the product to be developed.

[0063] At block 306, materials suitable for developing the product may be determined based on the identification. For instance, materials having or capable of providing the requirements and the desired properties in the product may be identified for developing the product. In one implementation, the materials may be determined using materials ontology and knowledge elements provided in the knowledge base.

[0064] At block 308, a process for developing the product is ascertained. In one implementation, a product and process ontology instance and product and process knowledge elements stored in a knowledge base, such as the knowledge base. 108 may be accessed to determine the process that may be followed for designing the product. Further, various parameters and rules that may be followed while executing the process for developing the product may be determined. [0065] At block 310, processing of the material may be simulated using one or more simulation tools for developing a simulated product. The simulated product may be understood as a virtual product having properties and specifications similar to the original product desired by the user. In one implementation, the simulated tools may be determined using a simulation ontology and simulation knowledge elements provided in the knowledge base. Further, a simulation model is identified for simulating the processing of the material based on model ontology instance and model knowledge elements. Furthermore, output of simulation of each of the one or more processes may be saved in global state data 218 for being used for one or more subsequent processes from among the one or more processes, thereby facilitating global integration of the processes.

[0066] At block 312, a product testing process may be simulated for the simulated product. The product testing process may be simulated to test whether the simulated product passes the tests that the intended product may have to undergo to verify the product's compliance with various industry regulations. The product testing process may be determined corresponding to the one or more processes ascertained at the block 308.

[0067] At block 314, a determination is made to ascertain whether the simulated product meets the requirements and the desired properties. For example, the process execution module 110 may compare the specification and properties of the simulated product with the requirements and the desired properties, to ascertain whether the simulated product meets the requirements and the desired properties. If the simulated product meets the requirements and the desired properties, which is the 'Yes^! path from the block 314, it updates the knowledge base having the knowledge elements and ontology instances and the global state data 218 of the design modeling system 102 at block 316. [0068] In case it is determined that the simulated product does not meet the requirements and the desired properties, which is the 'No' path from the block 314, the materials and the processes used for the designing are modified at the block 318. From the block 318, the method proceeds to the block 310 for re-processing of the materials. Further, the modified materials and processes may be used to update the knowledge base 108, thus ensuring regular update of the knowledge base 108.

[0069] Although implementations for methods and systems for computational design and modeling have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and the methods are disclosed as exemplary implementations for the methods and systems for computational design and modeling.

Claims

I/We claim:

1. A computer implemented method for computational design and modeling, the method comprising:

identifying, by a processor (202), requirements and desired properties of a product being developed based on a product development request;

determining, by the processor (202), one or more processes and materials suitable for designing the product using a materials ontology instance, a product and process ontology instance, material knowledge elements, and product and process knowledge elements based on the identification, wherein the material ontology instance is defined as an ontology instance having data related to materials, internal structure of material, and material properties for use in manufacturing the product, and wherein the product and process ontology instance is defined as an ontology instance describing data related to various products that are manufactured using at least the materials and various processing techniques for manufacturing of the material and the product;

simulating, by the processor (202), processing of the material for developing a simulated product using one or more simulation tools, the one or more processes, and the materials;

comparing, by the processor (202), properties of the simulated product with the requirements and the desired properties to ascertain whether the simulated product meets the requirements and the desired properties; and

updating, by the processor (202), a knowledge base (108) having ontology instances corresponding to the materials, the one or more processes, and the simulation tool based on the comparing and ascertaining for being used for design and modeling of the product and similar products.

2. The method as claimed in claim 1 , wherein the updating further comprises:

modifying the material and the process used for product development to obtain modified processes and materials on determining that the simulated product does not meet the requirements and the desired properties based on the ascertaining, and wherein modifying the material and the process comprises modifying the material ontology instance and the product and process ontology instance; and

re-processing the product simulation using the modified processes and materials to obtain another simulated product.

3. The method as claimed in claim 1, wherein the comparing further comprises simulating a product testing process corresponding to the determined process to test whether the simulated product meets One or more industry regulations.

4. The method as claimed in claim 1, wherein the comparing further comprises comparing the requirements and the desired properties with properties of the simulated product at each step of simulation to determine at least one step at which the simulation led to deviation in the desired properties and the requirements of the properties.

5. The method as claimed in claim 1, wherein the comparing further comprises comparing the requirements and the desired properties with properties of the simulated product at end of the simulation.

6. The method as claimed in claim 1, wherein the simulating further comprises:

identifying a simulation model for simulating the processing of the material based on model ontology instance and model knowledge elements; and

interact with the one or more simulation tools to simulate the processing of the material using the simulation model to obtain the simulated product.

7. The method as claimed in claim 1 , wherein the updating further comprises:

obtaining, periodically, data related to the materials and the processes from various online databases and publications; and

updating the knowledge base (108) based on the obtaining.

8. The method as claimed in claim 1, wherein the updating further comprises periodically adding new ontology instances and knowledge elements corresponding to new materials, processes, products, and simulation tools to update the knowledge base (108).

9. The method as claimed in claim 1 , wherein the simulating further comprises simulating the one or more processes in an integrating manner by saving output of simulation of each of the one or more processes in global state data (218) for being used for one or more subsequent processes from among the one or more processes.

10. A design modeling system (102) comprising: a processor (202);

a process design module (112) coupled to the processor (202) to,

identify requirements and desired properties of a product being developed based at least on a product development request; and

determine process and materials suitable for designing the product using a materials ontology instance, a product and process ontology instance, material knowledge elements, and product and process knowledge elements based on the identification, wherein the material ontology instance is defined as an ontology instance having data related to materials, internal structure of material, and material properties for use in manufacturing the product, and wherein the product and process ontology instance is defined as an ontology instance describing data related to various products that are manufactured using at least the materials and various processing techniques for manufacturing of the material and the product;

a process execution module (110) coupled to the processor (202) to,

simulate processing of the material for developing a simulated product using one or more simulation tools, the one or more processes, and the materials based on the determination; and

compare properties of the simulated product with the requirements and the desired properties to ascertain whether the simulated product meets the requirements and the desired properties; and

knowledge base interaction module (114) coupled to the processor (202) to update a knowledge base (108) having ontology instances corresponding to the materials, the one or more processes, and the simulation tool based on the ascertaining for being used for design and modeling of the product and similar products.

11. The design modeling system (102) as claimed in claim 10, wherein the knowledge base interaction module (114) further modifies the material and the process used for product development to obtain modified processes and materials on determining that the simulated product does not meet the requirements and the desired properties based on the ascertaining, and wherein modifying the material and the process comprises modifying the material ontology instance and the product and process ontology instance.

12. The design modeling system (102) as claimed in claim 11, wherein the process execution module (110) further simulates the product using the modified processes and materials to obtain another simulated product.

13. The design modeling system (102) as claimed in claim 11, wherein the knowledge base interaction module (114) further comprises,

a knowledge services module (228) to modify knowledge in the knowledge base (108); and

an ontology definition module (230) to Update one or more ontologies in the knowledge base (108).

14. The design modeling system (102) as claimed in claim 10, wherein the process execution module (110) further simulates a product testing process corresponding to the determined process to test whether the simulated product meets one or more industry regulations.

15. The design modeling system (102) as claimed in claim 10, wherein the process execution module (110) further compares the requirements and the desired properties with properties of the simulated product at each step of simulation to determine at least one step at which the simulation led to deviation in the desired properties and the requirements of the properties.

16. The design modeling system (102) as claimed in claim 10, wherein the process execution module (110) further compares the requirements and the desired properties with properties of the simulated product at end of the simulation.

17. The design modeling system (102) as claimed in claim 10, wherein the process execution module (110) further

identifies a simulation model for simulating the processing of the material based on model ontology instance and model knowledge elements; and

interacts with the one or more simulation tools to simulate the processing of the material using the simulation model to obtain the simulated product.

18. The design modeling system (102) as claimed in claim 17, wherein the knowledge base interaction module (114) further, obtains data related to the materials and the processes from various online databases and publications; and updates the knowledge base (108) based on the obtaining.

19. The design modeling system (102) as claimed in claim 10, wherein the knowledge base interaction module (114) further periodically adds new ontology instances and knowledge elements corresponding to new materials, processes, products, and simulation tools to update the knowledge base (108).

20. The design modeling system (102) as claimed in claim 10, wherein the process execution module (110) further simulates the one or more processes in an integrating manner by saving output of simulation of each of the one or more processes in global state data (218) for being used for one or more subsequent processes from among the one or more processes.

21. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method for computational design and modeling, the method comprising: identifying, by a processor (202), requirements and desired properties of a product being developed based on a product development request; determining, by the processor (202), one or more processes and materials suitable for designing the product using a materials ontology instance, a product and process ontology instance, material knowledge elements, and product and process knowledge elements based on the identification, wherein the material ontology instance is defined as an ontology instance having data related to materials, internal structure of material, and material properties for use in manufacturing the product, and wherein the product and process ontology instance is defined as an ontology instance describing data related to various products that are manufactured using at least the materials and various processing techniques for manufacturing of the material and the product;

updating, by the processor (202), a knowledge base (108) having ontology instances corresponding to the materials, the one or more processes, and the simulation tool based on the ascertaining for being used for design and modeling of the product and similar products.