WO2020222145A1

WO2020222145A1 - System and method for evaluation of foundry sand

Info

Publication number: WO2020222145A1
Application number: PCT/IB2020/054054
Authority: WO
Inventors: Deepak CHOWDHARY
Original assignee: Chowdhary Deepak
Priority date: 2019-04-30
Filing date: 2020-04-30
Publication date: 2020-11-05
Also published as: IN201921017228A

Abstract

The present disclosure pertains to a system (102) and method (300) to evaluate one or more attributes for optimization of testing criterion for a sample of foundry sand for use in a casting process. The system (102) includes a processor (202) to receive sample information associated with the sample of the foundry sand, compute the received sample information to obtain at least one variability measure data, receive at least one testing time data, receiving at least one influencing factor (IF), compute at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF, and evaluate, the one or more attributes associated with the sample of foundry sand tested over the plurality of time interval, based on the at least one influencing parameter (IP).

Description

SYSTEM AND METHOD FOR EVALUATION OF FOUNDRY SAND

TECHNICAL FIELD

[0001] The present disclosure generally relates to granular material testing structures and methods. More particularly, the present disclosure relates generally to foundry sand testing methods, apparatus and system, and refers more specifically to an automatic structure for performing a plurality of tests at varying frequencies for foundry sand.

BACKGROUND

[0002] Known arrangements for testing of foundry sand do not effectively identify many important characteristics of the foundry sand which dynamically and variably influence casting outcomes. Known foundry sand testing arrangements, while typically complex, provide limited information to facilitate proper conditioning or reconditioning for effective use of the foundry sand being tested.

[0003] It is well known that the characteristics of molding sand directly impacts the casting quality. Almost 50% of the overall casting rejections in Green Sand Foundries are owing to causality from varying sand parameters. In the absence of meaningful data generated and captured at the right time, frequency and accuracy and maintaining optimal sand parameters has always been and remained a critical decision-making issue for foundry men. To have tight control in sand systems, close monitoring of the characteristics and authenticity of prepared sand data is mandatory.

[0004] The varying product-mix in a foundry results in variable bum out losses of additives and is compounded by varying core sand infiltration, thus bringing uncertainty in the batch-to-batch return sand. In the absence of sufficient, authentic and meaningful measurement of sand properties based on variable retur sand, the decision on accurate, variable additive dosing for each batch cannot be relied upon. Typically, 7 to 12 sand properties out of almost 19 properties listed in the AFS sand molding handbook are being measured by foundries in typically static frequencies in each shift. The frequency of measurement for each property is different and mainly relies on the Man and Machine hours required for its testing. Generally, the frequency is driven by the domain and experiential legacy of the system established by generations of sand personnel. [0005] The conventional methods may use static testing frequency approach in spite of different variability of sand properties across different periods, wherein the techniques use same sand testing frequencies for each component irrespective of variable production planning. Further, insufficiency and lack of meaningful data insights related to testing can impact the casting outcomes (quality), which can further impact time, cost and efficiency of purpose of laboratory processes which is basically to help target sand properties for helping decision making for best casting outcomes. Furthermore, in absence of effective optimization techniques of attributes such as testing frequency may lead to futile utilization of manual or machine hours, which may unnecessarily increase the operation costs.

[0006] Hence, there is a need in the art, for an effective method and system for evaluating one or more attributes of foundry sand testing such as frequency of testing, for use in casting process. More particularly, a need therefore exists to provide systems, methods, devices and apparatuses to predict optimal frequency of sand testing properties in a day/shift based on the cause and effect relationship of rejections with sand properties, within the available time of man and machine hours and by considering the variability of sand parameters.

OBJECTS OF THE PRESENT DISCLOSURE

[0007] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0008] It is an object of the present disclosure to provide a system and method for evaluating one or more attributes related to sand testing.

[0009] It is another object of the present disclosure to provide a simple and effective system and method for sand testing frequency optimization.

[0010] It is another object of the present disclosure to provide an effective system and method that may be considered in production scheduling and resource management for effective optimization of attributed such as sand testing frequency for improved utilization of manual or machine hours, which may reduce/control operation costs.

SUMMARY

[0011] The present disclosure generally relates to granular material testing structures and methods. More particularly, the present disclosure relates generally to foundry sand testing methods, apparatus and system, and refers more specifically to a system and method to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process for performing a plurality of tests for foundry sand.

[0012] This summary is provided to introduce simplified concepts of a system and method to evaluate one or more attributes for optimization of testing criterion for a sample of foundry sand for use in a casting process, which are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended for use in determining/limiting the scope of the claimed subject matter.

[0013] In an aspect, the present disclosure provides a method to evaluate one or more attributes for optimization of testing criterion for a sample of foundry sand for use in a casting process. The method including step of receiving, sample information associated with the sample of the foundry sand, by a processor of a computing device, wherein the sample information may be associated with one or more sample characteristics of the sample tested over a plurality of time interval; computing the received sample information to obtain at least one variability measure data, by the processor, wherein the at least one variability measure data may be based on deviation present in each of the one or more sample characteristics of the sample information; receiving at least one testing time data obtained over the plurality of time interval for testing each of the one or more sample characteristics, by the processor; receiving at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics, by the processor; computing at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF, by the processor; and evaluating, the one or more attributes associated with the sample of foundry sand tested over the plurality of time interval, based on the at least one influencing parameter (IP), by the processor, wherein the one or more attributes may be obtained for optimization of the testing criterion related to the sample of foundry sand for use in the casting process.

[0014] In an aspect, the one or more sample characteristics may be any or a combination of active clay content, wet tensile strength, green compression strength (GCS), loss on ignition (LOI), compactability, moisture, inert fines, volatile matter, permeability, shear strength, friability index, or sand temperature.

[0015] In another aspect, the present disclosure provides a system to evaluate one or more attributes for optimization of testing criterion for a sample of foundry sand for use in a casting process. The system may include one or more processors that may be configured to: receive sample information associated with the sample of the foundry sand, the sample information being associated with one or more sample characteristics of the sample tested over a plurality of time interval; compute the received sample information to obtain at least one variability measure data, wherein the at least one variability measure data may be based on deviation present in each of the one or more sample characteristics of the sample information; receive at least one testing time data of a total time duration for testing each of the one or more sample characteristics over the plurality of time interval; receive at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics; compute at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF; and evaluate, based on the at least one influencing parameter (IP), the one or more attributes associated with the sample of foundry sand tested over the plurality of time interval, wherein the one or more attributes may be obtained for optimization of the testing criterion related to the sample of foundry sand for use in the casting process.

[0016] In an aspect, the processor is configured to receive said any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF, from one or more repositories.

[001h In an aspect, the at least one IF is received from the repository including experiential foundry data.

[0018] In an aspect, the rejection parameter is based on one or more rejection factors related to the one or more characteristics of said sample, the one or more rejection factors being a criterion for rejection of said sample.

[0019] In an aspect, the one or more attributes in said step of evaluating is related to an optimal frequency, which is indicative of the testing frequency of the one or more sample characteristics of the sample of foundry sand, for use in the casting process.

[0020] In an aspect, the at least one variability measure data is based on deviation present in each of the one or more sample characteristics from one or more estimated values.

[0021] In an aspect, the at least one testing time data is associated with said total time duration taken for manual measurement and equipment measurement for testing each of said one or more sample characteristics over said plurality of time interval. [0022] In an aspect, the system includes a database configured to store said any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF.

[0023] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0025] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:

[0026] FIG. 1 illustrates exemplary network architecture in which or with which proposed system may be implemented, in accordance with an embodiment of the present disclosure.

[002h FIG. 2 illustrates an exemplary system to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process, in accordance with an embodiment of the present disclosure.

[0028] FIG. 3 is a flow diagram illustrating steps of a method to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process, in accordance with an embodiment of the present disclosure.

[0029] FIG. 4 illustrates an exemplary system in which or with which embodiments of the present invention may be utilized in accordance with embodiments of the present disclosure.

[0030] FIG. 5 illustrates an exemplary flowchart illustrating the basic flow diagram of sand testing frequency optimization (STFO), in accordance with an exemplary embodiment of the present disclosure.

[0031] FIG. 6A illustrates pictorial representation of the time distribution based on an exemplary testing frequency of foundry sand. [0032] FIG. 6B illustrates pictorial representation of the time distribution based on an optimal frequency of testing foundry sand, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

[0033] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0034] The system and method of the present disclosure may not be limited to foundry sand testing, and can be used for testing other material in construction or other manufacturing industries.

[0035] As used in the following description and claims, it may be appreciated that foundry sand may include, but not limiting to, bentonite or clay bonded sand, often referred to as green sand, and chemically bonded sand.

[0036] Various objects, features, aspects and advantages of the present disclosure will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like features.

[003h FIG. 1 illustrates exemplary network architecture in which or with which a system (102) may be implemented in accordance with an embodiment of the present disclosure. FIG. 2 illustrates an exemplary system (102) to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process, in accordance with an embodiment of the present disclosure.

[0038] In an aspect, the system (102) may include one or more processors (202) that may be configured to: receive sample information associated with the sample of the foundry sand, the sample information being associated with one or more sample characteristics of the sample tested over a plurality of time interval; compute the received sample information to obtain at least one variability measure data, wherein the at least one variability measure data may be based on deviation present in each of the one or more sample characteristics of the sample information; receive at least one testing time data of a total time duration for testing each of the one or more sample characteristics over the plurality of time interval; receive at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics; compute at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF; and evaluate, based on the at least one influencing parameter (IP), the one or more attributes associated with the sample of foundry sand tested over the plurality of time interval, wherein the one or more attributes may be obtained for optimization of the testing criterion related to the sample of foundry sand for use in the casting process.

[0039] As illustrated in FIG. 1, in a network implementation 100, the system (102) may be communicatively coupled with a plurality of computing devices 106-1, 106-2... 106- N (collectively referred to as computing devices (106) and individually referred to as computing device (106) hereinafter) through network (104). The system (102) may be implemented using any or a combination of hardware components and engine components such as a server (112), a computing system, a computing device, a security device and the like.

[0040] Further, the system (102) may interact with input devices 108-1, 108-2... 108-

N (collectively referred to as input devices (108), and individually referred to as input device

108 herein after), through the computing devices (106) or through applications residing on the computing devices (106). In an implementation, the system (102) may be accessed by applications residing on any operating system, including but not limited to, Windows™. Android™, iOS™, and the like. Examples of the computing devices (106) may include but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. In a preferred embodiment, the computing devices (106) are mobile phones associated with respective input devices (108).

[0041] In an embodiment, the input device (108) may include a touch pad, touch enabled screen of a computing device, an optical sensor, an image scanner and the like that may be used to receive a handwriting input that forms part of an input to the system (102).

[0042] In an embodiment, the input device (108) may be implemented such that it forms part of the computing device (106). For example, the input device (108) may be a touch screen implemented with mobile device (106) to receive any input such as sample information provided as a written text from the user.

[0043] In an aspect and as illustrated in FIG. 2, the system (102) may include one or more processors (202). In an embodiment, the one or more processors (202) may be configured to receive sample information associated with sample of foundry sand. In an aspect, the sample information may be associated with one or more sample characteristics of the sample tested over a plurality of time interval.

[0044] In an aspect, the one or more sample characteristics may be any or a combination of active clay content, wet tensile strength, green compression strength (GCS), loss on ignition (LOI), compactability, moisture, inert fines, volatile matter, permeability, shear strength, friability index, or sand temperature. The sample characteristics may not be limited to the mentioned characteristics and various other characteristics of the sample may also be included.

[0045] In an embodiment, the one or more processors (202) may be configured to compute the received sample information to obtain at least one variability measure data. The at least one variability measure data may be based on deviation present in each of the one or more sample characteristics of the sample information. In an aspect, the at least one variability measure data may be based on deviation present in each of the one or more sample characteristics from one or more estimated values.

[0046] In an embodiment, the one or more processors (202) may compute variability measure data for each of the sample characteristics. In an embodiment, the variability measure data may be based on at least a standard deviation of each of the sample characteristics from data accumulated over plurality of time interval. In an exemplary embodiment, the variability measure data may be based on at least a standard deviation present in each of the sample characteristics from data accumulated over plurality of time interval in a period of 3 months. In another exemplary embodiment, the variability measure data may be based on at least a standard deviation present in each of the sample characteristics from data accumulated over plurality of time interval in a period of 15 days. The accumulated data related to variability measure data may be stored in database 230. In an embodiment, the variability measure may be expressed as a number in the range of 1 to 10.

[004h In an embodiment, the one or more processors (202) may be configured to receive at least one testing time data of total time duration for testing each of the one or more sample characteristics over the plurality of time interval. In an aspect, the at least one testing time data may be associated with the total time duration taken for manual measurement and equipment measurement for testing each of the one or more sample characteristics over the plurality of time interval. [0048] In an embodiment, the one or more processors (202) may be configured to receive at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics. In an aspect, the rejection parameter may be based on one or more rejection factors related to the one or more characteristics of the sample, wherein the one or more rejection factors may be a criterion for rejection of the sample. In an embodiment, the at least one IF associated with rejection parameter for each of the one or more tested sample characteristics may be based on data related to rejection parameter accumulated over plurality of time interval. In an exemplary embodiment, the at least one IF associated with rejection parameter for each of the one or more tested sample characteristics is based on data related to rejection parameter accumulated over plurality of time interval in a period of 3 months. In another exemplary embodiment, the at least one IF associated with rejection parameter for each of the one or more tested sample characteristics is based on data related to rejection parameter accumulated over plurality of time interval in a period of 15 days. The accumulated data related to rejection parameter may be stored in database 230.

[0049] In an aspect, the plurality of time interval may be in form of shifts or intervals ranging from 0.5 hours to 24 hours. Other ranged time interval may also be applied.

[0050] In an embodiment, the one or more processors (202) may be configured to compute at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF.

[0051] In an aspect, the processor may be configured to receive the any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF, from one or more repositories. In an aspect, the at least one IF may be received from repository including an experiential foundry data.

[0052] In an embodiment, the one or more processors (202) may be configured to evaluate, based on the at least one influencing parameter (IP), the one or more attributes associated with the sample of foundry sand tested over the plurality of time interval. The one or more attributes may be obtained for optimization of the testing criterion related to the sample of foundry sand for use in the casting process.

[0053] In an aspect, the one or more attributes in the step of evaluating may be related to an optimal frequency, which may be indicative of the testing frequency of the one or more sample characteristics of the sample of foundry sand, for use in the casting process. [0054] In an aspect and as illustrated in FIG. 2, the system (102) may comprise one or more processors) (202). The one or more processors (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 data based on operational instructions. Among other capabilities, the one or more processors) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (102). The memory (204) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as

EPROM, flash memory, and the like.

[0055] The system 102 may also comprise an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, SC AD A, Sensors and the like. The interface(s) (206) may facilitate communication of the system (102) with various devices coupled to the system (102) such as the computing device (108). The interface(s) (206) may also provide a communication pathway for one or more components of the system (102). Examples of such components include, but are not limited to, processing engine(s) (202) and database (230).

[0056] The one or more processors (202) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the one or more processors (202). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the one or more processors (202) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the one or more processors (202) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine -readable storage medium may store instructions that, when executed by the processing resource, implement the one or more processors (202). In such examples, the system (102) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine -readable storage medium may be separate but accessible to the system (102) and the processing resource. In other examples, the one or more processors (202) may be implemented by electronic circuitry.

[005h In an aspect, the database (230) may be configured to store any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF. In another aspect, the database (230) may comprise data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) (202).

[0058] In an exemplary embodiment, the processing engine(s) (208) may include a sand parameter engine (210), a testing duration engine (212), a variability measure engine (214), an influencing factor engine (216), a prediction engine (218), and other engines (220) wherein the other engines (220) may further include, without limitation, sample information receiving engine, modification engine, event generation engine, storage engine, or signal generation engine.

[0059] In an exemplary embodiment, the sand parameter engine (210) may receive sample information associated with sample of foundry sand, wherein the sample information may be associated with one or more sample characteristics of the sample tested over a plurality of time interval. The received sample information may be computed by variability measure engine (214) to obtain at least one variability measure data, wherein the at least one variability measure data may be based on deviation present in each of the one or more sample characteristics of the sample information. In an aspect, testing duration engine (212) may receive at least one testing time data of a total time duration for testing each of the one or more sample characteristics over the plurality of time interval. In an aspect, influencing factor engine (216) may receive at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics. In an aspect, prediction engine (218) may compute at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF. The prediction engine (218) may evaluate, one or more attributes associated with the sample of foundry sand tested over the plurality of time interval, based on the at least one influencing parameter (IP).

[0060] In an exemplary embodiment, the system (102) may require user to fill up the form or enter details using his/her computing device (108), to enter the sample information. The details entered may include any of the date mentioned herein. Once the sample information is received the system 102 proceeds to the next step. [0061] In an embodiment, the variability measure engine (214) may compute variability measure data for each of the sample characteristics. In an embodiment, the variability measure data may be based on at least a standard deviation present in each of the sample characteristics from data accumulated over plurality of time interval. In an exemplary embodiment, the variability measure data may be based on at least a standard deviation present in each of the sample characteristics from data accumulated over plurality of time interval in a period of 3 months. In another exemplary embodiment, the variability measure data may be based on at least a standard deviation present in each of the sample characteristics from data accumulated over plurality of time interval in a period of 15 days. In other embodiments, other measures associated with variability known in the art may also be envisioned. The accumulated data related to variability measure data may be stored in database 230. In an embodiment, the variability measure may be expressed as a number in the range of 1 to 10.

[0062] In an embodiment, the influencing factor engine (216) may receive an at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics, wherein the rejection parameter may be based on one or more rejection factors related to the one or more characteristics of the sample, the one or more rejection factors being a criterion for rejection of the sample. In an embodiment, the at least one IF associated with rejection parameter for each of the one or more tested sample characteristics is based on data related to rejection parameter accumulated over plurality of time interval. In an exemplary embodiment, the at least one IF associated with rejection parameter for each of the one or more tested sample characteristics is based on data related to rejection parameter accumulated over plurality of time interval in a period of 3 months. In another exemplary embodiment, the at least one IF associated with rejection parameter for each of the one or more tested sample characteristics is based on data related to rejection parameter accumulated over plurality of time interval in a period of 15 days. The accumulated data related to rejection parameter may be stored in database 230.

[0063] In an embodiment, it would be appreciated by the person skilled in the art that the system (102) may be processed occur in an automated mode without human intervention and would reduce the time duration for evaluation of one or more attributes associated with the sample. [0064] It would be appreciated by the person skilled in the art that a cloud-based processing of the generated modified based on the instructions stored in the build server may be perform the functions enumerated herein.

[0065] FIG. 3 is a flow diagram illustrating steps of a method 300 to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process, in accordance with an embodiment of the present disclosure.

[0066] At 301, the process or method 300 may include receiving sample information associated with one or more sample characteristics of a sample of foundry sand tested over a plurality of time interval, at a processor (202). At 302, the method 300 may include computing the received sample information to obtain at least one variability measure data, at the processor (202). At 303, the method 300 may include receiving at least one testing time data of total time duration for testing each of the one or more sample characteristics over the plurality of time interval, at the processor (202). At 304, the method 300 may include receiving at least one influencing factor (IF) associated with rejection parameter for each of the one or more tested sample characteristics, at the processor (202). At 305, the method 300 may include computing at least one influencing parameter (IP) based on any or a combination of the at least one variability measure data, the at least one obtained testing time data, and the at least one obtained IF, at the processor (202). At 306, the method 300 may include evaluating, based on the at least one influencing parameter (IP), one or more attributes associated with the sample of foundry sand tested over the plurality of time interval, at the processor (202), wherein the one or more attributes are obtained for optimization of the testing criterion related to the sample of foundry sand for use in the casting process.

[0067] In an aspect, one or more sample characteristics may be any or a combination of active clay content, wet tensile strength, green compression strength (GCS), loss on ignition (LOI), compactability, moisture, inert fines, volatile matter, permeability, shear strength, friability index, or sand temperature. In an aspect, the processor (202) may be configured to receive the any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF, from one or more repositories. In an embodiment, the at least one IF is received from repository including experiential foundry data.

[0068] In an aspect, rejection parameter may be based on one or more rejection factors related to the one or more characteristics of the sample, the one or more rejection factors being a criterion for rejection of the sample. [0069] In an aspect, one or more attributes in the step of evaluating is related to an optimal frequency, which may be indicative of the testing frequency of the one or more sample characteristics of the sample of foundry sand, for use in the casting process. In an aspect, at least one variability measure data is based on deviation present in each of the one or more sample characteristics from one or more estimated values. In an aspect, at least one testing time data is associated with the total time duration taken for manual measurement and equipment measurement for testing each of the one or more sample characteristics over the plurality of time interval.

[0070] FIG. 4 illustrates an exemplary computer system in which or with which embodiments of the present invention may be utilized in accordance with embodiments of the present disclosure.

[0071] Computer system 400 includes a bus 420 or other communication mechanism for communicating information, and a processor 470 coupled with bus 420 for processing information. Computer system 400 may also include a main memory 430 or other non- transitory computer-readable medium, such as a random-access memory (RAM) or other dynamic storage device, which may then be coupled to bus 420 for storing information and instructions to be executed by processor 470. Main memory 430 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 470. Computer system 400 may further include a read only memory (ROM) 440 or other static storage device coupled to bus 420 for storing static information and instructions for processor 470. A data/extemal storage device 410, such as a magnetic disk or optical disk, is provided and coupled to bus 420 for storing information and instructions.

[0072] The invention is related to the use of computer system 400 for creation and management of BOMs as elaborated above. According to some embodiments of the invention, such use may be provided by computer system 400 in response to processor 470 executing one or more sequences of one or more instructions contained in the main memory 430. Such instructions may be read into main memory 430 from another computer-readable medium, such as storage device 450. Execution of the sequences of instructions contained in main memory 430 causes processor 470 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory 430. In alternative embodiments, hardwired circuitry may be used in place of or in combination with engine instructions to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and engine.

[0073] FIG. 5 illustrates an exemplary flowchart illustrating the basic flow diagram of sand testing frequency optimization (STFO), in accordance with an exemplary embodiment of the present disclosure.

[0074] In an embodiment, the processor (202) may provide a machine learning driven algorithm that may evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process. In an exemplary embodiment, the processor (202) may provide a machine learning driven algorithm to evaluate optimal frequency for measurement of one or more sample characteristics associated with sample of foundry sand for use in casting process. The output may be used by the algorithm provided by the processor (202) for computing/generating influencing parameters for a given sample of sand for a given day/shift. Additionally, the processor (202) may compute variability measure data for a given sample information, receive testing time data and influencing factor (IF) based on the rejection parameter, and consider these factors as input and may apply an optimization algorithm. Based on high influencing factors and variability of the data, the processor (202) may find an optimized solution for the frequency sand testing for a given plurality of time interval in form of day/shift. For the purposes of this document,“day/shift” refers to a given casting task which may encompass a scheduled assortment of casting tasks for a given day, or a scheduled individual casting task. Here, notwithstanding anything to the contrary, the former may be associated with a large-scale project while the latter may be associated with a small-scale singular project or task.

[0075] In an exemplary embodiment, one of the main inputs for this algorithm of the processor (202) may be the influencing factor (IF) for each sand parameter co-related to casting rejections. This may come from the output of processor (202) or influencing factor engine (216) discussed in previous section, which predicts the effect of each parameter with influencing order based on rejection parameter for plurality of time interval in form of day/shift. In an embodiment, the average high influence factor (received by the processor (202) or engine) for plurality of time interval of the data available, may be taken as input. In an exemplary embodiment, the average high influence factor (received by the processor (202) or engine) for 15 days of the data available, may be taken as input. The parameter which impacts high on rejections may be given more weightage and the parameter which impact least on rejections may be given less weightage. [0076] In an exemplary embodiment, another important input parameter for the algorithm is the variability measure of each sand parameter. The engine may inherently compute the standard deviation present in each parameter from the data accumulated over plurality of time interval in the form of day/shift. The variability measure data may be calculated across the shifts and within the shift. The parameter with high variability in past data, may be given more weightage and thus need to be tested for a greater number of times for its in-time control, like highest IP value, scaling in the range of 1-10 may be performed to the variability of each parameters by using min-max scaling.

[007h In an exemplary embodiment, the testing time data for measuring each sand parameter may be split into man-hours and machine-hours. For example, during LOI testing the manual time required is only 35 sec, whereas the total time of LOI measurement is 165 min. Majority of the time may be taken by the testing machine. During intervening test time, the man/hour available time may often be utilized for measuring some other parameter to avoid time wastage. To accommodate this feature, the algorithm considers both man and machine time as two separate parameters, and the final optimized solution may be predicted for optimizing both man and machine time available. This way foundry may ensure effective utilization of both machine and man time.

[0078] In an embodiment, the evaluation of one or more attributes such as optimal frequency for sand testing may be configured for all components together or for the groups of components. For component-wise sand testing frequency prediction, the processor (202) or engine may take the input corresponding to those components and predicts frequency based on the parameters impacting the rejections for those components. Below are a few direct utilities of optimized sand testing frequency:

• Optimized sand frequency driven solution may help foundry to generate more meaningful data and will have better sand optimization with controlled process consistency.

• Reduced frequency of the lowest influencing parameter or parameter with less variability

• Increased frequency of the highest influencing parameter may help foundry in closely monitoring of the sand parameters in order to control it.

• This may help in-time decision making by the QC department in controlling influencing sand properties and as a corollary, help improve casting quality. [0079] In an exemplary embodiment, the present disclosure may consider below three type of data to predict optimal frequency of sand testing properties in a day/shift based on the cause and effect relationship of rejections with sand properties, and by considering the variability of sand properties:

✓ Sample information associated with one or more characteristics of a sample, wherein the sample information may be accumulated over three months as provided with corresponding date and time.

✓ Testing time data including total, man and machine time for measuring each of the one or more characteristics of the sample.

✓ Influence Factor Highest Influencing Sand Properties (HIP) based on their influence on the rejection parameterthat may suggest the direction of change to reduce the influence.

[0080] As shown in FIG. 5, an objective function to evaluate one or more attributes such as optimal frequency for testing may be achieved based on various parameters. In an exemplary embodiment, the objective function may be defined as:

Where

• Standard deviation is calculated for every column in data set.

• V = Variability measure data [Contains standard deviation of one or more characteristics of sample]

• t = Testing time data [Time taken to take single reading of each of one or more characteristics of sample] in minutes

• I = Influencing factor [Influencing factor for each of one or more characteristics of sample]

• f[i] = [optimal frequency for measurement of each of one or more characteristics of sample over plurality of time interval in form of shift]

• The optimization function may be computed by processor (202) or engine, based on (V, I, t) to get optimal frequencies values.

[0081] In an exemplary embodiment, the evaluation of optimal frequency may be utilized to achieve the exemplary objective function is as provided below:

Objective Function:

• V[i] = Variability measure data (scaled)

• I[i] = Influence factor(scaled)

• t_total [i] = Testing time data i.e.Total time taken to measure each of one or more characteristics of sample in minutes (scaled)

Subject to constraints: (Manual and Machine time as Individual constraints)

Here tmanual and unsealed time of measurement in minutes, the = optimization

problem may be solved using method='S LS QP' (Sequential Least Squares Programming).

Exemplary W orking/Procedure :

1. The exemplary working may be provided assuming selection of three months data as engine period to calculate the variability for each parameter shift wise from prepared sand data.

2. Scaling of Variability V[i] using 1 to 10 scaling

3. High influence factor for last 15 days of the engine period from the sandman high influence page.

Highest influence for a particular day is assigned- 1

Lowest influence for a particular day is assigned- N

4. Now taking the sum over every column as shown in below Table 1 depicting Influence factor of each days and Table 2 depicting Weighted Influence factor for last 30 days.

. The column sum which has minimum value is the highest influencing factor,

. The column sum which has maximum value is the least influencing factor.

. Now scaling using min max scalar and the subtracting it from 1.

. Now higher the value of Influence (1 -Scaled) higher will be the influence.

. By this method relative influence factors may be assigned for all parameters.

Table 1: Influence factor of each days

5. Scaling of Influence factor I[i] using 1 to 10 scaling

6. t_total is also scaled using 1 to 10 scaling

Total manual hours available/shift:

For every parameter the data is divided in three parts for shift X,K and Z.

Then calculate a number of times each parameter is measured in shift X,K and

Z.

• Then divide it with a number of days it is measured this will gives us average no. of times a particular parameter is measured per shift.

• For a particular shift average frequency of a particular parameter may be multiplied to its time of manual measurement for (single reading).

• Summing over all parameters gives the average total manual time available per shift for sand testing. Similar steps are applied for calculating total machine hours available/shift. Table 3 below depicts a sample testing time data from the Sand lab, and Table 4A depicts a high influence page for a day.

Table 4A: High influence page for a day

[0082] Below provided in Table 4B, are the results obtained from the above recited experimental analysis i.e., XYZ: in 2 month period, (Novl7 -Janl8), 1 to 10 Scaling (SHIFT

X):

[0083] In an exemplary embodiment, the present disclosure may enable in time data driven insights to a user, so that faster decision may be taken if any properties affecting the rejections or deviating from foundry specification limit. It may be appreciated that this feature when combined with the existing analytical green sand optimization application could provide more meaningful data and add value perception to the foundry. This may be expected to improve castings quality and reduce process or task costs. In addition, the engine may be considered in production scheduling and resource management for their effective utilization. The present invention paves the way to SMART generation’s foundries.

[0084] In an exemplary embodiment of the present disclosure, the one or more attricutesmay be evaluated based on the data obtained from major Iron Foundries in India. The results analyzed against their current sand testing practice, are discussed below. The manual time, machine time and total time taken for measurement of each parameter for this foundry is reported in Table 3 (above). The model was provided with three months prepared sand testing data. The optimized frequency solution along with the model input and predicted frequency output for each parameter are furnished in Table 4A (above).

[0085] FIG. 6A illustrates pictorial representation of the time distribution based on an exemplary testing ftequencyof foundry sand. FIG. 6B illustrates pictorial representation of the time distribution based on an optimal frequency of testing foundry sand, in accordance with an embodiment of the present disclosure.

[0086] Table 5 below provides input properties along with the present and optimized sand testing frequency.

[0087] It may be observed that Inert Fines shows maximum impact on rejection for last two week, to consider its effect model has predicted increase in inert fines frequency from 1 to 2, shown in Table 3. The optimized solution shows reduction in moisture testing frequency from 5 times to 2 times per shift. This is because both variability and the influencing factor for the moisture were observed as low. Though, WTS appeared as least influencing parameter, but it showed high variability in past data, so model has not decreased its frequency and predicted to measure maximum number of times. Another important observation is about LOI and VM. Algorithm showed both VM and LOI as low Influencing parameter, but with high variability. As, testing time for LOI is very high and VM is very less; model predicted to increase the frequency of VM measurement from 1 to 4, whereas frequency for LOI measurement remained same at 1.

[0088] Further, Table 6 below provides Variability analysis of sand properties before and after optimization.

Table 6 Variability analysis of sand properties before and after optimization [0089] To understand the value proposition after considering the optimized sand testing frequency, validation experimentation was performed to compare sand parameter variability before and after optimization. The same foundry’s previous three-month prepared sand data (which is considered in above optimization) was taken and the variance of each parameter was calculated in its original space. Then a simulated data was generated by considering optimized sand testing frequency reported in Table 5. Below are the steps used for generating simulated data-set.

. For the properties where the optimal frequency and actual frequency are same, the data remained unchanged.

. For the properties where the optimal frequency is lesser than the actual frequency, the starting data in each shift are considered and the remaining are ignored.

. For the properties where the optimal frequency is greater than actual frequency then the data is imputed by shift- wise mean of that parameter.

The standard deviation of the properties in the simulated data is found to be reduced with optimized sand testing frequency.

[0090] The method of the present disclosure may account for various rejection incidences vis-i-vis the most influencing green sand parameter, thus allowing the foundry to take timely corrective actions to bring about process consistency may be obtained from any of the existing or newly formed databases either stored or retrieved from online source. In an example, the at least one influencing factor (IF) may be retrieved from“Sandman” a Data analytics driven process optimization and rejection control tool which is a proprietary of “MPM Infosoft Pvt. Ltd.” The details for obtaining the at least one influencing factor (IF) is clearly defined in Patent Application No. 201480006006.8 titled, "Computer Implemented Systems and Methods for Optimization of Sand for Reducing Casting Rejections" based on

PCT/IN2014/000128 dated 26.02.2014 claiming Priority from 553/MUM/2013 dated 26.02.2013.

[0091] “Sandman” is considered as the world’s first predictive data analytics driven decision support softwaie.Sandman acts as a repository of the experiential knowledge of the foundry, with the ability to record, monitor and perform retrospective analysis on any process operation affecting the sand system. In provides multi-parameter, multi-variant analysis enables the foundry to detect and analyze the deviant and/or aberrant process parameters of the greensand, over any specified time period.lt also analyses various rejection incidences vis-à-vis the most influencing green sand parameter, thus allowing the foundry to take timely corrective actions to bring about process consistency.

[0092] In an exemplary embodiment, the at least one influencing parameter (IP) may be computed by utilizing a machine learning technique or an artificial intelligence technique preconfigured in the system.

[0093] In an exemplary embodiment, the testing data may be received from one or more users operating on the received foundry sand over plurality of time interval.

[0094] In an exemplary embodiment, the testing data may be received from one or more repositories storing data on the received foundry sand over plurality of time interval.

[0095] In an exemplary embodiment, the evaluation of one or more attributes may be displayed as an assessed recommendation in at least in one of a tabular format, a graphical representation, an audio-visual representation, or an animated representation.

[0096] It is known in the art that most of the rejections in sand foundries are owing to inconsistent sand parameters. To have tight control in sand system, close monitoring of the characteristics of prepared sand is usually required. Typically, 5 to 12 type of sand parameters are measured by foundries in each shift. The measurement frequency for each parameter is different and that mainly rely on the man and machine hours required for its testing. Many times, the frequency is driven by the domain legacy of the sand lab in-charge, who decides based on his own experience.

[0097] In order to solve the above recited problems, the present invention provides an analytical solution for predicting the optimal frequency of sand testing parameters in each shift based on the cause and effect relationship of rejections with sand parameters, and that too corresponding to each component. The present invention may use an output of existing and/or proprietary available analytical tool, which predicts the highest influencing parameters that impacts the casting rejections for a given day. The present invention may be considered in production scheduling and resource management for their effective utilization.

[0098] The present disclosure provides a method and system to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand for use in a casting process. The method and system may be considered in production scheduling and resource management for effective optimization of testing frequency for improved utilization of manual or machine hours. Further, the processed data obtained by the system of the present disclosure may be accumulated, for improved precision in evaluation of the attributes such as optimal frequency. [0099] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE

[00100] The present disclosure provides a simple and effective system and method for evaluation of attributes such as optimal frequency for sand testing.

[00101] The present disclosure provides an effective system and method that may be considered in production scheduling and resource management for effective optimization of testing frequency for improved utilization of manual or machine hours, which may reduce/control operation costs.

Claims

I Claim:

1. A method (300) to evaluate one or more attributes for optimization of testing criterion, for a sample of foundry sand, for use in a casting process, the method comprising the steps of:

receiving (301), by a processor (202) of a computing device (102), sample information associated with said sample of said foundry sand, the sample information being associated with one or more sample characteristics of said sample tested over a plurality of time interval;

computing (302), by the processor (202), said received sample information to obtain at least one variability measure data, wherein said at least one variability measure data is based on deviation present in each of said one or more sample characteristics of the sample information;

receiving (303), by the processor (202), at least one testing time data obtained over said plurality of time interval for testing each of said one or more sample characteristics; receiving (304), by the processor (202), at least one influencing factor (IF) associated with rejection parameter for each of said one or more tested sample characteristics;

computing (305), by the processor (202), at least one influencing parameter (IP) based on any or a combination of the said at least one variability measure data, said at least one obtained testing time data, and the at least one obtained IF; and

evaluating (306) by the processor (202), based on said at least one influencing parameter (IP), said one or more attributes associated with said sample of foundry sand tested over said plurality of time interval,

wherein said one or more attributes are obtained for optimization of said testing criterion related to said sample of foundry sand for use in the casting process.

2. The method as claimed in claim 1, wherein said one or more sample characteristics is any or a combination of active clay content, wet tensile strength, green compression strength (GCS), loss on ignition (LOI), compactability, moisture, inert fines, volatile matter, permeability, shear strength, friability index, or sand temperature.

3. The method according to claim 1, wherein said processor (202) is configured to receive said any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF, from one or more repositories.

4. The method as claimed in claim 3, wherein the at least one IF is received from the repository including experiential foundry data.

5. The method as claimed in claim 1, wherein said rejection parameter is based on one or more rejection factors related to said one or more characteristics of said sample, said one or more rejection factors being a criterion for rejection of said sample.

6. The method as claimed in claim 1, wherein said one or more attributes in said step of evaluating is related to an optimal frequency, which is indicative of the testing frequency of said one or more sample characteristics of said sample of foundry sand, for use in the casting process.

7. The method as claimed in claim 1, wherein said at least one variability measure data is based on deviation present in each of said one or more sample characteristics from one or more estimated values.

8. The method as claimed in claim 1, wherein at least one testing time data is associated with said total time duration taken for manual measurement and equipment measurement for testing each of said one or more sample characteristics over said plurality of time interval.

9. A system (102) to evaluate one or more attributes for optimization of testing criterion for a sample of foundry sand for use in a casting process, said system (200) comprising: one or more processors (202) configured to:

receive sample information associated with said sample of said foundry sand, the sample information being associated with one or more sample characteristics of said sample tested over a plurality of time interval;

compute said received sample information to obtain at least one variability measure data, wherein said at least one variability measure data is based on deviation present in each of said one or more sample characteristics of the sample information; receive at least one testing time data of a total time duration for testing each of said one or more sample characteristics over said plurality of time interval;

receive at least one influencing factor (IF) associated with rejection parameter for each of said one or more tested sample characteristics;

compute at least one influencing parameter (IP) based on any or a combination of the said at least one variability measure data, said at least one obtained testing time data, and the at least one obtained IF; and evaluate, based on said at least one influencing parameter (IP), said one or more attributes associated with said sample of foundry sand tested over said plurality of time interval,

10. The system (102) as claimed in claim 9, wherein said system (102) further comprises a database (230) configured to store said any or a combination of plurality of sample information, at least one obtained testing time data, and the at least one obtained IF.