SYSTEM AND METHOD FOR CLASSIFYING ELEMENTS OF A PRODUCT

Disclosed is a method and system for classifying elements of a product. The method comprises identifying elements of the product. Thereupon, features of the one or more elements are determined, using a feature recognition technique. The features correspond to manufacturing operations required for manufacturing the elements, and include sheet metal operations, turn operations, injection moulding operations, and machining operations. The manufacturing operations are determined in a priority order with the sheet metal operation having a highest priority and the machining operation having a least priority.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

The present application claims benefit from Indian Patent Application No. 202011046694 filed on 26 Oct. 2020 the entirety of which is hereby incorporated by reference.

TECHNICAL FIELD

The present subject matter described herein, in general, relates to mechanical automation, and more particularly to a system and a method for automatically identifying mechanical operations required to manufacture elements of a product.

BACKGROUND

Automation is the order of the day in today's manufacturing world. There exists a large pool of assembly components without manufacturing information. An automated system and method for generating information about manufacturing requirements of a product is required. Such information could be used in process planning, cost planning, and plans related to development of mechanical products.

Existing modeler tools like SolidWorks, NX, CREO, and Catia can merely assist in designing assemblies but they lack the capability to determine mechanical operations required to develop a component present in a product. Thus, there remains a need to develop such system and method using which mechanical operations required to develop components present in a product could be identified, and subsequently such information could be used to manufacture the components.

SUMMARY

Before the present systems and methods for classifying elements of a product are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations or versions or embodiments only and is not intended to limit the scope of the present application.

This summary is provided to introduce aspects related to a system and a method for classifying elements of a product. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.

In one implementation, a system for classifying elements of a product is disclosed. In one aspect, the system comprises a memory and a processor coupled to the memory. Further, the processor may be capable of executing instructions in the memory to perform one or more steps. In the aspect, the system may identify one or more elements of the product. The system may further determine, using a feature recognition technique, features of the one or more elements. The features correspond to manufacturing operations required for manufacturing the one or more elements, and include sheet metal operations, turn operations, injection moulding operations, and machining operations. The manufacturing operations are determined in a priority order with the sheet metal operation having a highest priority and the machining operation having a least priority.

In one implementation, a method for classifying elements of a product is disclosed. In one aspect, the method may comprise identifying one or more elements of the product. The method may further comprise determining, using a feature recognition technique, features of the one or more elements. The features correspond to manufacturing operations required for manufacturing the one or more elements, and include sheet metal operations, turn operations, injection moulding operations, and machining operations. The manufacturing operations are determined in a priority order with the sheet metal operation having a highest priority and the machining operation having a least priority.

DETAILED DESCRIPTION

Although any systems and methods for classifying elements of a product, similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods for classifying elements of a product are now described. The disclosed embodiments for classifying elements of a product are merely examples of the disclosure, which may be embodied in various forms.

Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments for classifying elements of a product. However, one of ordinary skill in the art will readily recognize that the present disclosure for classifying elements of a product is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.

Referring now toFIG. 1, a network implementation diagram100of a system102for classifying elements of a product, in accordance with an embodiment of the present subject matter may be described. In one example, the system102may be connected with devices104-1through104-N (collectively referred as104) through a communication network106.

It should be understood that the system102and the devices104are different computing devices. The system102may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment, and a mobile. The devices104may include a laptop104-1, a smart phone104-2, or a data storage device such as a Hard Disk Drive (HDD)104-N. The devices104may be used for providing a 3-Dimensional (3D) model of the product. Upon receiving the 3D model from the devices104, the system102may perform further processing, as described in later sections.

In one implementation, the communication network106may be a wireless network, a wired network, or a combination thereof. The communication network106can be implemented as one of the different types of networks, such as intranet, Local Area Network (LAN), Wireless Personal Area Network (WPAN), Wireless Local Area Network (WLAN), wide area network (WAN), the internet, and the like. The communication network106may be either a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, MQ Telemetry Transport (MQTT), Extensible Messaging and Presence Protocol (XMPP), Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the communication network106may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

Referring now toFIG. 2, a block diagram200of the system102is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system102may include at least one processor202, an input/output (I/O) interface204, and a memory206. The at least one processor202may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor202may be configured to fetch and execute computer-readable instructions stored in the memory206.

The memory206, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of modules208. The memory206may include any computer-readable medium or computer program product 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 (EPROM), Electrically Erasable and Programmable ROM (EEPROM), flash memories, hard disks, optical disks, and magnetic tapes.

The memory206may include data generated as a result of the execution of one or more of the modules208. In one implementation, the memory206may include data210. The modules208include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules208may include a sheet metal operation module212, a turn operation module214, an injection moulding operation module216, a machining operation module218, and other modules220. The other modules220may include programs or coded instructions that supplement applications and functions of the system102. The modules208described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system102.

The data210may include a repository222for storing data processed, computed, received, and generated by one or more of the modules208. Furthermore, the data210may include other data224for storing data generated as a result of the execution of one or more modules in the other modules220.

In one implementation, at first, a 3-Dimensional (3D) representation of the product is obtained. Alternatively, a 3D model of the product may be inputted to the system. The 3D model of the product may be used to capture details of the product from an appropriate view, such as a top view, a bottom view, a front view, a back view, a side view, or a perspective view.

Successively, features of the one or more elements of the product may be determined using a feature recognition technique. The feature recognition technique operates on boundary representation models of products. The features may correspond to manufacturing operations required for manufacturing the one or more elements. Such manufacturing operations may include sheet metal operations, turn operations, injection moulding operations, and machining operations. In one preferred embodiment, the manufacturing operations are determined in a priority order with the sheet metal operation having a highest priority and the machining operation having a least priority, amongst the listed four operations.

In one embodiment, features such as walls, bends, cut-outs, flanges, and stamps may be identified by the sheet metal operation module212, to be manufactured using the sheet metal operations. Main criterion for recognition of features related to the sheet metal operations is that an element should have a uniform thickness. A wall, as illustrated inFIG. 3a, is characterized by two parallel planar faces offset by thickness.FIG. 3aalso shows a top surface300and a wall direction302. The top surface and a bottom surface of the wall can be of any shape. A bend, as illustrated inFIG. 3b, is defined as a thin sheet having uniform thickness and having both top and bottom faces of either cylindrical or conical shapes.FIG. 3balso shows a bend axis304. A flange, as illustrated inFIG. 3c, is defined by a profile, swept along a path. The profile as well path of the flange can be multi-segment.FIG. 3calso shows a path edge306of the flange. A stamp, as illustrated inFIG. 3d, is a feature created by embossing sheets.

In one embodiment, features such as an external end profile feature and an internal end profile feature may be identified to be manufactured using the turn operations, by the turn operation module214. The turn module recognizes axis-symmetric features mainly found on elements. Input provided to the turn module is a potential axis about which the element is going to be manufactured. A foremost criterion for the turn operations is that the element should consist of cylindrical and conical faces. Once this criterion is satisfied, the turn operation module214can operate on the product for recognising potential turn features. Presence of turn features ensures the element to be manufactured using lathe operations.

The turn operation module214may operate using a turn feature. The turn feature may include details for recognizing an external end profile feature. The external end profile feature, as illustrated inFIG. 4a, is a composition of consecutive external element features with monotonically increasing or equal diameters. The external end profile feature always starts from one side of the part and stops at one of the neighbour external element features with largest diameter. The external end profile feature is usually machined with a face down process or outer turning operation towards chuck. The external end profile feature only exists at either side of a element, so a maximum of two external end profile features can be formed. As visible inFIG. 4a, two external end profile features share one common external element feature E3. Therefore, E1, E2, and E3form one external end profile feature on one direction and E5, E4, and E3form another external end profile feature on another direction.

The turn feature library may also include details for recognizing an internal end profile feature. To an extent, the internal end profile feature is similar to the external end profile feature, but constituent features are internal. Thus, the internal end profile feature is a composition of consecutive internal element features with monotonically equal or decreasing diameters, as illustrated inFIGS. 4band 4c. The internal end profile feature always starts from one side of an element and stops at one neighbour component features with smallest diameter. The internal end profile feature is usually machined with a face up process or an inner turning operation towards the chuck. As visible inFIG. 4b, internal element feature E3is shared by two internal end profile features. Further, as visible inFIG. 4c, sharing of an internal element feature is not possible.

Features such as ribs and boss may be identified to be manufactured using the injection moulding operations by the injection moulding operation module216. The injection moulding operation module216may operate based on an injection moulding feature including details for recognizing the ribs and boss features. Presence of the ribs and rib networks indicates that the element is going to be manufactured using the injection moulding process. Further, presence of the boss features is an additional criterion for classifying the elements for Injection moulding process.

A profile of a rib500can be present as shown inFIG. 5a. A rib feature502can have two or more parallel faces separated by a small distance, as illustrated inFIG. 5b. Such distance corresponds to a thickness of the rib. Typically, the thickness ranges from 3-5 mm for most of the parts. Parallelism restriction is waived for drafted ribs. Rib networks are formed as a collection of ribs. A rib network profile504can be present as shown inFIG. 5c. Further,FIG. 5dillustrates an exemplary rib network506. The rib network506is created by making a planar profile and extruding the profile in a direction perpendicular to profile plane. A thickness is also given to the profile during an extrude operation. In normal ribs, the profile is thickened along a normal of the profile plane; while in a rib network, the profile is thickened perpendicular to the profile plane. A boss508, as illustrated inFIG. 5e, is a stand-alone protruding feature present on a base.

In one embodiment, features such as holes, pockets, slots, and islands may be identified to be manufactured using the machining operations by the machining operation module218. The machining operations may be performed by a Computerised Numeric Control (CNC) machine. The machining operation module218may operate based on a machining library including details to recognize holes of different types, such as counter-bore holes, counter-sunk holes, counter-drill holes, taper holes, and simple holes. Holes are depressions in elements with cylindrical, conical, or toroidal side faces. The machining library may also include details to recognize pockets. The pockets are feature that are completely covered from all sides. The pockets are either blind pockets or through pockets. A blind pocket600, as illustrated inFIG. 6a, have defined bottom. A through pocket602, as illustrated inFIG. 6b, does not have a defined bottom. A slot is a pocket feature having multiple openings. The slots are either blind slots or through slots. A blind slot604is illustrated inFIG. 6c, as an example. Islands606are identified as protrusion features present on pockets or slots, as illustrated inFIG. 6d.

Referring now to an exemplary product as illustrated inFIG. 7, working of the system102for classifying elements of a product is explained. The product is made of different elements which are manufactured using different mechanical operations. The elements identified to be present in the product include a Bush700, a Pivot702, U-Support704, a Pin706, and a Bracket708.

In accordance with the above defined priority of identifying elements, the system102may firstly identify the Bush700from its 3D representation, as illustrated inFIG. 7a. From the 3D representation, the system102may determine a 2D representation of the Bush700, as illustrated inFIG. 7a′. The system102may determine that the Bush700will be manufactured using the turn operation, as the Bush700is axis symmetric and has cylindrical faces. An external end profile and an internal end profile of the Bush700may also be determined.

Successively, the system102may identify the Pivot702from its 3D representation, as illustrated inFIG. 7b. From the 3D representation, the system102may determine a 2D representation of the Pivot702, as illustrated inFIG. 7b′. The system102may determine that the Pivot702will be manufactured using the machining operation, as the Pivot702comprises 5-hole features and 2-slot features.

Successively, the system102may identify the U-Support704from its 3D representation, as illustrated inFIG. 7c. From the 3D representation, the system102may determine a 2D representation of the U-Support704, as illustrated inFIG. 7c′. The system102may determine that the U-Support704will be manufactured using the sheet metal operation, as the U-Support704has a uniform thickness and comprises 3 walls and 2 bend features.

Thereupon, the system102may identify the Pin706from its 3D representation, as illustrated inFIG. 7d. From the 3D representation, the system102may determine a 2D representation of the Pin706, as illustrated inFIG. 7d′. The system102may determine that the Pin706will be manufactured using the turn operation, as the Pin706is axis symmetric and has cylindrical faces.

Finally, the system102may identify the Bracket708from its 3D representation, as illustrated inFIG. 7e. From the 3D representation, the system102may determine a 2D representation of the Bracket708, as illustrated inFIG. 7e′. The system102may determine that the Bracket708will be manufactured using the machining operation, as the Bracket708comprises holes, slots, and island features.

In the above described manner, manufacturing process required to produce different elements of a product could be automatically determined and operation of an entire manufacturing assembly could be automated.

Referring now toFIG. 8, a method800for classifying elements of a product is described, in accordance with an embodiment of the present subject matter. The method800may 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 order in which the method800for classifying elements of a product 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 method800or alternate methods. Additionally, individual blocks may be deleted from the method800without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method800may be considered to be implemented in the above described system102.

At block802, an assembly file including details of a product is opened in a 3D modeler tool.

At block804, all the parts/elements present in the product are identified and segregated, and their details stored as different elements in a list.

At block806, starting from beginning, all the elements present in the list are traversed.

At block808, presence of sheet metal elements in the list is identified. In case, sheet metal elements are identified, their details are forwarded to a sheet metal operation machine, at block810.

At block812, presence of turn elements in the list is identified. In case, turn elements are identified, their details are forwarded to a turn operation machine, at block814.

At block816, presence of injection moulding elements in the list is identified. In case, injection moulding elements are identified, their details are forwarded to an injection moulding operation machine, at block818.

At block820, details of remaining elements are forwarded for machining operation.

Thereupon, at block806, it is determined if details of all the elements present in the list is traversed. If details of all the elements are found to be traverse, the program ends.

Although implementations for methods and systems for classifying elements of a product 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 methods are disclosed as examples of implementations for classifying elements of a product.