Patent Description:
The disclosure herein generally relates to optimal utilization of rotary cutters, and, more particularly, to methods and systems for real time estimation of pressure change requirements for rotary cutters.

Rotary knifes/cutters plays an important role in the continuous manufacturing of finished products like diapers, sanitary pads, etc. They require periodic maintenance and need to be changed at regular intervals. Generally, the rotary cutters work in a very specific manner and are supported by a cylinder that imparts vertical force on the rotary knives, thus enabling them to optimally rest/touch the surface beneath where a free rotating anvil lies. The rotary cutters tend to lose their cutting material and sharpness over time due to usage. Hence to compensate for the same, pressure applied by the cylinder needs to be changed / increased manually. But this change / increase in pressure needs to be optimum as too high pressure can lead to loss of cutting material and too low pressure can stop the desired cutting operation.

However, as this pressure change requirement for cylinders is usually decided based on human judgement, it leads to some unplanned and sudden stoppage of the machine thus leading to wastage of operational time as well as the material. Further, as the pressure change is handled manually by human operators, it becomes necessary for the operators to have a good understanding of the pressure change requirement as the amount of pressure change at any step can have a great impact on the rotary cutter life, thereby putting stress on the operators to accurately handle/decide the pressure change requirement and the change in pressure.

Additionally, even though the operator may have a good understanding of the pressure change requirement, a manual operation sometimes lacks the exact update owing to pressure measurement and the update being analog in nature.

<CIT> shows manually adjusting the cutting force of a rotary cutter to maintain the quality of the cut even with loss of sharpness over time due to usage.

<CIT> shows measuring wear of a cutting tool and comparing the measured tool wear with a model based predicted tool wear and informing a user of a deviation.

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, there is provided a processor implemented method for real time estimation of pressure change requirements for rotary cutters according to claim <NUM>.

In another aspect, there is provided a pressure change requirement estimation system for real time estimation of pressure change requirements for rotary cutters according to claim <NUM>.

In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause a method for real time estimation of pressure change requirements for rotary cutters according to claim <NUM>.

The manufacturing industry, especially consumer packaged goods (CPG) industry is heavily dependent on rotary cutters. As discussed previously, rotary cutters tend to lose material and sharpness over its usage cycle. So, to make it work for a longer time, a cylinder is used to impart vertical force. The force (pressure) that is to be applied to the rotary cutters is usually measured using an analog scale and is changed manually by an operator based on his/her experience. Most of the times, the pressure change is made once the machine stops automatically as the rotary cutter cannot complete the cutting operation, thereby leading to wastage of operational time as well as the material which has to be discarded. Moreover, because of inefficient utilization of the rotary cutter, the rotary cutter loses its cutting efficiency early which further leads to increase in cost as the rotary cutter needs to be changed frequently for proper functioning of the machine.

Further, the systems that are available in the prior art for prolonging the life of rotary cutters works towards minimizing wear and tear losses during cutting process and wear and tear of the die used in the cutting process. However, handling the pressure change which is one of the important aspects is still not well handled by the systems available in the prior art.

Embodiments of the present disclosure overcome the above-mentioned disadvantages, like inefficient utilization, wastage of operational time and material, etc., by providing methods and systems for real time estimation of pressure change requirements for rotary cutters. More specifically, a pressure change requirement estimation system (also referred as system and interchangeably used herein) is provided by the present disclosure that uses a dual step prediction methodology to identify the pressure change requirement while considering both time of usage as well as physics-based signals. Once the pressure change requirement is identified, the system automatically verifies and calculates the exact pressure change requirement, thereby guiding the system to automatically update the pressure applied on the rotary cutters in real-time. Basically, the system automatically predicts the next rotary cutter pressure change time based on historical rotary cutter usage data and physical parameters of the rotary cutter and alerts a user/operator of the machine to change the pressure when it is due, thereby mitigating the need for human judgement that further helps in ensuring optimal utilization of the rotary cutters and higher productivity of the machine while reducing the wastage of the operational time and the material.

<FIG> illustrates an exemplary representation of an environment <NUM> related to at least some example embodiments of the present disclosure. Although the environment <NUM> is presented in one arrangement, other embodiments may include the parts of the environment <NUM> (or other parts) arranged otherwise depending on, for example, time for a next pressure change estimation, next pressure value estimation, etc. The environment <NUM> generally includes a machine <NUM> comprising a rotary cutter and a pressure change requirement estimation system (PCRES) <NUM>, each coupled to, and in communication with (and/or with access to) a network <NUM>.

The network <NUM> may include, without limitation, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, a virtual network, and/or another suitable public and/or private network capable of supporting communication among two or more of the parts or users illustrated in <FIG>, or any combination thereof.

Various entities in the environment <NUM> may connect to the network <NUM> in accordance with various wired and wireless communication protocols, such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), 2nd Generation (<NUM>), 3rd Generation (<NUM>), 4th Generation (<NUM>), 5th Generation (<NUM>) communication protocols, Long Term Evolution (LTE) communication protocols, or any combination thereof.

The machine <NUM> is a mechanical structure used in an industry to produce a particular product, such as sanitary pad, diaper etc. In an embodiment, the machine <NUM> includes a rotary unit in which a rotary cutter is embedded as per the specific usage requirement. The rotary unit further includes a rotary cutter unit 102a, an anvil 102b, and a cylinder 102c. The rotary cutter unit 102a is powered by an external drive motor and is configured to cut input raw material using the rotary cutter to produce the required product. The anvil 102b is a free rotating component where the rotary cutter unit 102a rest while performing its desired operation. The cutting inertia generated by the rotary cutter unit 102a while performing the operation makes the anvil 102b to rotate along with the rotary cutter unit 102a. The cylinder 102c is configured to impart vertical force/pressure onto the rotary cutter in an orthogonal direction of its operation.

The pressure change requirement estimation system (PCRES) <NUM> includes one or more hardware processors, and a memory. The PCRES <NUM> is configured to perform one or more of the operations described herein. The PCRES <NUM> is configured to receive historical rotary cutter usage data associated with the rotary cutter and a real-time pressure value applied on the rotary cutter from the machine <NUM> using the network <NUM>. In an embodiment, the historical rotary cutter usage data includes discrete variables signifying object, such as number of sanitary pads quantity produced or continuous variables such as time wise pressure data, regrinding, number of times the rotary cutter has been used, manufacturer information etc. In one embodiment, the time wise pressure data include timing details when the pressure applied to the rotary cutter was changed. The PCRES <NUM> is also configured to receive historical data associated with each rotary cutter of a plurality of rotary cutters that was used by the machine <NUM> previously. In an embodiment, the historical data includes trends signifying loss of cutting property of each rotary cutter of the plurality of rotary cutters that can cause updating of the applied pressure.

The PCRES <NUM> is then configured to estimate a minimum usage limit and a maximum usage limit for the rotary cutter at the real-time pressure value based on the historical rotary cutter usage data and the real-time pressure value using a first trained model. Thereafter, the PCRES <NUM> is configured to monitor the real-time rotary cutter usage data to determine whether the minimum usage limit is reached for the rotary cutter. In an embodiment, the real-time rotary cutter usage data is received in real-time from the machine <NUM> using the network <NUM>.

Further, upon determining that the minimum usage limit is reached for the rotary cutter, the PCRES <NUM> is configured to estimate a time for a next pressure change based on one or more physical parameters of the rotary cutter using a second trained model. In an embodiment, the one or more physical parameters are determined based on the real-time rotary cutter usage data that is collected in real-time from one or more sensors that are installed in the machine <NUM>. The sensors basically collects operation related parameters such as current, speed etc. In case the minimum limit is yet to be reached for the rotary cutter, the machine <NUM> continues normal operation till the minimum usage limit is reached for the rotary cutter provided in the machine <NUM>.

Once the estimated time for a next pressure change is determined, the PCRES <NUM> is configured to compare the estimated time with a predefined threshold i.e., the estimated maximum usage limit determined for the rotary cutter. If the estimated time crosses the predefined threshold, then a message is displayed to a user/an operator of the machine <NUM>. In an embodiment, the message includes a notification to change the pressure applied on the rotary cutter within the estimated time. In case the estimated time is found to be within the predefined threshold, the PCRES <NUM> is configured to perform the next round of calculation for estimating time for next pressure change.

Additionally, the PCRES <NUM> is configured to determine an amount of pressure to be changed based on a rotary cutter usage index and the real-time pressure value. In an embodiment, the rotary cutter usage index is determined based on the historical rotary cutter usage data received from the machine <NUM>. The historical rotary cutter usage data may include regrinding information, number of times the rotary cutter has been used, manufacturer information etc. Basically, the historical rotary cutter usage data dictates the required change in pressure at any point of time The PCRES <NUM> is then configured to determine a next pressure value for the rotary cutter based on the determined amount of pressure to be changed and the real-time pressure value using a pre-defined pressure calculation formula. Thereafter, the PCRES <NUM> displays the next pressure value for the rotary cutter to the user of the machine <NUM>.

In an embodiment, the user of the machine <NUM> may verify pressure update to decide whether to change the pressure applied on the rotary cutter or not. In another embodiment, the PCRES <NUM> may automatically update the pressure applied to the rotary cutter to the next pressure value. In one embodiment, the PCRES <NUM> may send one or more signals to one or more actuators installed on the machine <NUM> using the network <NUM> for updating the pressure applied to the rotary cutter to the next pressure value.

The number and arrangement of systems, containers, and/or networks shown in <FIG> are provided as an example. There may be additional systems, machines, and/or networks; fewer systems, machines, and/or networks; different systems, machines, and/or networks; and/or differently arranged systems, machines, and/or networks than those shown in <FIG>. Furthermore, two or more systems shown in <FIG> may be implemented within a single system or device, or a single system or device shown in <FIG> may be implemented as multiple, distributed systems or devices. Additionally, or alternatively, a set of systems (e.g., one or more systems) of the environment <NUM> may perform one or more functions described as being performed by another set of systems of the environment <NUM> (e.g., refer scenarios described above).

<FIG> illustrates an exemplary block diagram of a pressure change requirement estimation system (PCRES) <NUM> for real time estimation of pressure change requirements for rotary cutters, in accordance with an embodiment of the present disclosure. In an embodiment, the pressure change requirement estimation system <NUM> may also be referred as a system and may be interchangeably used herein. The system <NUM> is similar to the pressure change requirement estimation system (PCRES) <NUM> explained with reference to <FIG>. In some embodiments, the system <NUM> is embodied as a cloud-based and/or SaaS-based (software as a service) architecture. In some embodiments, the system <NUM> may be implemented in a server system. In some embodiments, the system <NUM> may be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, and the like.

The PCRES <NUM> includes a computer system <NUM> and a database <NUM>. The computer system <NUM> includes one or more processors <NUM> for executing instructions, a memory <NUM>, a communication interface <NUM>, and a user interface <NUM> that communicate with each other via a bus <NUM>.

In some embodiments, the database <NUM> is integrated within computer system <NUM>. For example, the computer system <NUM> may include one or more hard disk drives as the database <NUM>. A storage interface <NUM> is any component capable of providing the one or more processors <NUM> with access to the database <NUM>. The storage interface <NUM> may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing the one or more processors <NUM> with access to the database <NUM>.

In one embodiment, the database <NUM> is configured to store historical rotary cutter usage data associated with a rotary cutter and historical data associated with each rotary cutter of the plurality of rotary cutters. The database <NUM> is also configured to store predefined formulas, such as pre-defined pressure calculation formula and algorithms, such as polygon building algorithm and the like that may be used by the system <NUM> in real time estimation of the pressure change requirements.

The one or more processors <NUM> may be one or more software processing modules and/or hardware processors. In an embodiment, the hardware processors can 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 processor(s) is configured to fetch and execute computer-readable instructions stored in the memory <NUM>.

The memory <NUM> includes suitable logic, circuitry, and/or interfaces to store a set of computer readable instructions for performing operations. Examples of the memory <NUM> include a random-access memory (RAM), a read-only memory (ROM), a removable storage drive, a hard disk drive (HDD), and the like. It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to realizing the memory <NUM> in the PCRES <NUM>, as described herein. In another embodiment, the memory <NUM> may be realized in the form of a database server or a cloud storage working in conjunction with the PCRES <NUM>, without departing from the scope of the present disclosure.

The one or more processors <NUM> are operatively coupled to the communication interface <NUM> such that the one or more processors <NUM> communicate with a remote device <NUM> such as, the machine <NUM>, or communicated with any entity (for e.g., a machine learning/deep learning model) connected to the network <NUM>. Further, the one or more processors <NUM> are operatively coupled to the user interface <NUM> for interacting with users, such as the user/operator of the machine <NUM> who is responsible for changing the pressure applied on the rotary cutter.

It is noted that the PCRES <NUM> as illustrated and hereinafter described is merely illustrative of an apparatus that could benefit from embodiments of the present disclosure and, therefore, should not be taken to limit the scope of the present disclosure. It is noted that the PCRES <NUM> may include fewer or more components than those depicted in <FIG>.

In one embodiment, the one or more processors <NUM> includes a first trained model <NUM>, a continuous monitoring module <NUM>, a second trained model <NUM> and a pressure update calculation module <NUM>.

The first trained model <NUM> includes suitable logic and/or interfaces for determining minimum and maximum time the rotary cutter can be used if it is operating at a particular pressure value. Basically, the first trained model <NUM> is trained to learn from the past usage of cutter i.e., from the historical rotary cutter usage data. The learning along with the real-time pressure value of the rotary cutter is then used by the first trained model <NUM> to estimate a minimum usage limit and a maximum usage limit for the rotary cutter at the real-time pressure value. In an embodiment, the first trained model <NUM> establishes a statistical range defining a minimum usage limit and a maximum usage limit of the rotary cutter at any given pressure value using the historical rotary cutter usage data. The historical rotary cutter usage data presents usage of the rotary cutter for each pressure value by the multiple values of parameters defining the usage of cutter. The statistical range is then used to determine the minimum usage limit and a maximum usage limit for the rotary cutter at the real-time pressure value.

In an embodiment, the continuous monitoring module <NUM> is in communication with the first trained model <NUM> and includes suitable logic and/or interfaces for monitoring real-time rotary cutter usage data received in real-time from the machine <NUM> to determine time when the minimum usage limit is reached for the rotary cutter. The continuous monitoring module <NUM> is also configured to determine time when the maximum usage limit is reached for the rotary cutter. In one embodiment, the continuous monitoring module <NUM> is configured to generate a message upon determining that the maximum usage limit is reached for the rotary cutter. The message may be displayed to the user of the machine <NUM> through the user interface <NUM>. The message includes a notification to change the pressure applied on the rotary cutter within the estimated time.

The second trained model <NUM> is in communication with the first trained model <NUM> and the continuous monitoring module <NUM>. The second trained model <NUM> includes suitable logic and/or interfaces for estimating a time for a next pressure change based on one or more physical parameters of the rotary cutter. The physical parameters can be quantified based on the real-time rotary cutter usage data that is gathered from the sensors defining operation related parameters such as current, speed etc..

The pressure update calculation module <NUM> is in communication with the second trained model <NUM> and the continuous monitoring module <NUM>. The pressure update calculation module <NUM> includes suitable logic and/or interfaces for determining a rotary cutter usage index based on the historical rotary cutter usage data associated with the rotary cutter. In an embodiment, the historical rotary cutter usage data may include regrinding, number of times the rotary cutter has been used, manufacturer information, time details etc., which dictates the required change in pressure at any point of time. Mathematically, the rotary cutter usage index can be presented as: <MAT>.

The pressure update calculation module <NUM> is also configured to calculate an amount of pressure to be changed (also referred as delta pressure) based on the rotary cutter usage index and the real-time pressure value. The calculated amount of pressure to be changed is then used by the pressure update calculation module <NUM> along with the real-time pressure value to calculate a next pressure value for the rotary cutter using a pre-defined pressure calculation formula represented as: <MAT>.

In one embodiment, the user interface <NUM> is configured to display the message to the user / operator of the machine <NUM>. The user interface <NUM> is also configured to display the next pressure value for the rotary cutter to the user / operator.

<FIG>, with reference to <FIG> and <FIG>, illustrates a schematic block diagram representation <NUM> of the first trained model <NUM> associated with the system <NUM> of <FIG> or the PCRES of <FIG> for real time estimation of pressure change requirements for rotary cutters, in accordance with an embodiment of the present disclosure. In an embodiment, the first trained model <NUM> includes a data pre-processing module <NUM>, an auto identification module <NUM>, and a usage limit estimation module <NUM>.

The data pre-processing module <NUM> includes suitable logic and/or interfaces for receiving the historical rotary cutter usage data associated with the rotary cutter. The historical rotary cutter usage data includes one or more parameters such as discrete variables signifying object quantity produced and/or continuous variables such as time-wise pressure data. In one embodiment, the data pre-processing module <NUM> is configured to categorize the parameters signifying the cutter usage over time. The data pre-processing module <NUM> is also configured to filter out noise (i.e., irrelevant data) associated with the parameters to provide the pre-processed historical rotary cutter usage data.

The auto identification module <NUM> includes suitable logic and/or interfaces for identifying one or more events on which the pressure value applied to the rotary cutter is changed based on the parameters included in the pre-processed historical rotary cutter usage data and the pressure values at which the rotary cutter is operated.

The usage limit estimation module <NUM> includes suitable logic and/or interfaces for establishing the statistical range defining a minimum usage limit and a maximum usage limit of the rotary cutter at any given pressure value using the pre-processed historical rotary cutter usage data and the one or more events. The established statistical range is then further used by the usage limit estimation module <NUM> to determine the minimum usage limit and a maximum usage limit for the rotary cutter at the real-time pressure value.

<FIG>, with reference to <FIG> and <FIG>, illustrates a schematic block diagram representation <NUM> of the second trained model <NUM> associated with the system <NUM> of <FIG> or the PCRES of <FIG> for real time estimation of pressure change requirements for rotary cutters, in accordance with an embodiment of the present disclosure. In an embodiment, the second trained model <NUM> includes a data pre-processing module <NUM>, an auto identification module <NUM>, a confidence-interval building module <NUM>, a similarity score estimation module <NUM>, and a time estimation module <NUM>.

The data pre-processing module <NUM> is similar to the data pre-processing module <NUM> discussed with reference to <FIG>. The data pre-processing module <NUM> includes suitable logic and/or interfaces for extracting useful information i.e., estimate of pressure change from the real-time rotary cutter usage data associated with the rotary cutter. Basically, the real-time rotary cutter usage data includes the sensor signals that are received from the machine <NUM>. The sensor signals are not directly usable as physical characteristic changes are usually minuscule and often not representative in their direct form. Further, the sensor data contains undesired noise, so actual pressure signal data fluctuates around the underlying applied pressure. So, to extract useful information, the sensor signals are filtered for further analysis. In an embodiment, the data pre-processing module <NUM> may use any of the known filtering techniques, such as LOESS algorithm, STL etc., for performing filtering of the sensor data.

In an embodiment, the data pre-processing module <NUM> is also configured to eliminate noise from the historical data associated with each rotary cutter of a plurality of rotary cutters to obtain pre-processed historical data.

The auto identification module <NUM> is in communication with the data pre-processing module <NUM>. The auto identification module <NUM> includes suitable logic and/or interfaces for automatically identifying events in which change in pressure is triggered by the user from the pre-processed historical data. Basically, the historical data is used by the system <NUM> to understand underlying trends that signify loss of cutting property of each rotary cutter of the plurality of rotary cutters and hence requires change in pressure. Hence, to understand the trend, the auto identification module <NUM> identifies/marks the events which corresponds to the change in pressure automatically. As the auto identification module <NUM> works on clean historical data i.e., the pre-processed historical data, the auto identification module <NUM> can capture the actual change in pressure that is further marked as an event. Further, parameters of the machine changes with every step change in pressure. To compensate for the same, historical data of every equi-pressure window has to be extracted seperately. So, the auto identification module <NUM> performs pressure wise seggregation of the pre-processed historical data for the complete cycle of each rotary cutter with respect to multi-pressure values to obtain the segregated data for the respective rotary cutter. For example, the pressure of the rotary cutter is changed to a new pressure, say, from <NUM> bar to <NUM> bar and the pressure is further changed from the new pressure to next new pressure, say, <NUM> bar to <NUM> bar. Thus, by knowing these two events, the data for desired pressure, say <NUM> bar can be gathered for all cutters in the database by the auto identification module <NUM>. It should be noted that the auto identification module <NUM> uses few other machine signals also such as machine ramp up/down events along with variable thresholds for identifying the events.

In an embodiment, the confidence-interval building module <NUM> is in communication with the data pre-processing module <NUM> and the auto identification module <NUM>. The confidence-interval building module <NUM> includes suitable logic and/or interfaces for determining upper limit confidence interval and lower limit confidence interval for each instance of each rotary cutter of the plurality of rotary cutters based on the segregated data obtained for the respective rotary cutter using a statistical technique. In parallel, the confidence-interval building module <NUM> is also configured to determine upper limit confidence interval and lower limit confidence interval for each instance of the rotary cutter based on the real-time rotary cutter usage data using the same statistical technique. In an embodiment, the statistical measures that may be used for determining the upper limit confidence interval and the lower limit confidence interval includes, but are not limited to, standard deviation, interquartile range (IQR) etc. In one embodiment, the confidence-interval building module <NUM> uses a moving window of 'n' time stamps for each rotary cutter and then determines the upper limit confidence interval and the lower limit confidence interval for that window. In an embodiment, 'n' can be any value such <NUM>, <NUM>, <NUM> etc. The confidence-interval building module <NUM> may also perform smoothening of the upper limit confidence interval and the lower limit confidence interval for better results using a smoothening technique.

Thereafter, the confidence-interval building module <NUM> is configured to create a polygon for each rotary cutter of the plurality of rotary cutters to create a library of polygons and a rotary cutter polygon for the rotary cutter using a polygon building algorithm, such as a polygon building algorithm. As the rotary cutters may have lasted different time durations due to various operation or conditional factors historically at any given pressure value, creating the library of such different rotary cutters may really be helpful in estimating the pressure change requirement of the rotary cutter being used in real-time in the machine <NUM>. In particular, the polygon building algorithm connects the upper limit and the lower limit confidence intervals determined for each rotary cutter in time instance wise manner to obtain connected upper limit confidence intervals and connected lower limit confidence intervals for each rotary cutter of the plurality of rotary cutters and the rotary cutter present in the machine <NUM>. Further, the confidence-interval building module <NUM> is configured to enclose the connected upper limit confidence intervals and the connected lower limit confidence intervals obtained for each rotary cutter to create the polygon for the respective rotary cutter. The connected upper limit confidence intervals and the connected lower limit confidence intervals obtained for the rotary cutter are enclosed to create the rotary cutter polygon for the rotary cutter.

The similarity score estimation module <NUM> is in communication with the confidence-interval building module <NUM>. The similarity score estimation module <NUM> includes suitable logic and/or interfaces for comparing the rotary cutter polygon with each polygon present in the library of polygons to obtain a similarity score for the respective polygon. In particular, each polygon that is created from the historical data is compared with the rotary cutter polygon created using the real-time rotary cutter usage data. Further, the similarity score is calculated between the respective polygon and the rotary cutter polygon based upon similarity measure calculated and based on union and interesection of the polygon and the rotary cutter polygon, in one example embodiment. Such similarity score computation shall not be construed as limiting the scope of the present disclosure, and there could be other approaches of computing the same. So, higher the similarity score greater the similarity between the polygon and the rotary cutter polygon and hence the remaining usefule life (RUL)/time to change the pressure will likely be similar to the historical rotary cutter associated with the polygon. Thus, it can be concluded that the similarity score measure is thus a quantification of 'distance' between two polygons. The higher similrity score means closer is the distance.

The similarity score estimation module <NUM> is further configured to select at least one polygon from the library of polygons based on the similarity score. In particular, at least one polygon that has the highest similarity score is selected by the similarity score estimation module <NUM>. The selection createria for the polygins can be thought as the nearest neighbour apporach. The closest neighbors datasets can now be used for final estimation of time to change pressure/RUL of the rotary cutter.

In an embodiment, the time estimation module <NUM> is in communication with the similarity score estimation module <NUM>. The time estimation module <NUM> includes suitable logic and/or interfaces for accessing the pre-processed historical data associated with the at least one selected polygon. The time estimation module <NUM> is further configured to estimate the time for the next pressure change based on the pre-processed historical data associated with the at least one selected polygon. Additionally, the time estimation module <NUM> is configured to compare the estimated time with the estimated maximum usage limit determined for the rotary cutter. If the estimated time is with the estimated maximum usage limit, the time estimation module <NUM> is configured to communicate the same to the continuous monitoring module <NUM>. Otherwise, the one or more processors <NUM> performs the next round of calculation for estimation of time for pressure change.

<FIG>, with reference to <FIG>, <FIG> and <FIG>, illustrates an exemplary flow diagram of a method <NUM> for real time estimation of pressure change requirements for rotary cutters, in accordance with an embodiment of the present disclosure. The method <NUM> may use the system <NUM> of <FIG> and the pressure change requirement estimation system (PCRES) <NUM> of <FIG> for execution. In an embodiment, the system(s) <NUM> comprises one or more data storage devices or the memory <NUM> operatively coupled to the one or more hardware processors <NUM> and is configured to store instructions for execution of steps of the method <NUM> by the one or more hardware processors <NUM>. The sequence of steps of the flow diagram may not be necessarily executed in the same order as they are presented. Further, one or more steps may be grouped together and performed in form of a single step, or one step may have several sub-steps that may be performed in parallel or in sequential manner. The steps of the method <NUM> of the present disclosure will now be explained with reference to the components of the system <NUM> as depicted in <FIG>, and the PCRES <NUM> of <FIG>.

In an embodiment of the present disclosure, at step <NUM>, the one or more hardware processors <NUM> of the pressure change requirement estimation system (PCRES) <NUM> receive (a) historical rotary cutter usage data associated with a rotary cutter, (b) a real-time pressure value applied on the rotary cutter and (c) historical data associated with each rotary cutter of a plurality of rotary cutters. In an embodiment, the historical rotary cutter usage data includes discrete variables signifying object, such as sanitary pad quantity produced or continuous variables such as time wise pressure data, regrinding, number of times the rotary cutter is being used, manufacturer information etc..

At step <NUM> of the present disclosure, the one or more hardware processors <NUM> of the PCRES <NUM> estimate a minimum usage limit and a maximum usage limit for the rotary cutter at the real-time pressure value based, at least in part, on the historical rotary cutter usage data and the real-time pressure value using a first trained model.

As already explained previously, the auto identification module <NUM> of the first trained model <NUM> automatically identifies events on which the pressure value applied to the rotary cutter is changed. The events information is further utilized by the first trained model <NUM> to establish a statistical range defining a minimum usage limit and a maximum usage limit of the rotary cutter at any given pressure value. Further, the first trained model <NUM> utilizes the statistical range to estimate the minimum usage limit and the maximum usage limit for the rotary cutter at the real-time pressure value.

At step <NUM> of the present disclosure, the one or more hardware processors <NUM> of the PCRES <NUM> monitor real-time rotary cutter usage data to determine whether the minimum usage limit is reached for the rotary cutter. As discussed, the continuous monitoring module <NUM> monitors the real-time rotary cutter usage data received in real-time from the machine <NUM> to determine time when the minimum usage limit is reached for the rotary cutter. As soon as the minimum usage limit is reached for the rotary cutter, step <NUM> is performed.

At step <NUM> of the present disclosure, the one or more hardware processors <NUM> of the PCRES <NUM> estimate a time for a next pressure change based on one or more physical parameters of the rotary cutter using a second trained model upon determining that the minimum usage limit is reached for the rotary cutter. In an embodiment, the one or more physical parameters are determined based on real-time rotary cutter usage data collected in real-time from the one or more sensors (present in the machine <NUM>) defining operation related parameters such as current, speed etc. The process of estimating the time for the next pressure change using the second trained model <NUM> is explained in detail with reference to <FIG> and the description is not reiterated herein for the sake of brevity.

In an embodiment, at step <NUM> of the present disclosure, the one or more hardware processors <NUM> of the PCRES <NUM> compare the estimated time with the estimated maximum usage limit for the rotary cutter. Basically, the hardware processors <NUM> compare the estimated time with a pre-defined threshold that is the estimated maximum usage limit found for the rotary cutter to check whether the estimated time is within the pre-defined threshold. If the estimated time is found to be within the pre-defined threshold, the hardware processors <NUM> of the PCRES <NUM> performs next round of calculation for estimation of time for the next pressure change else a flag to change the rotary cutter pressure is raised (explained with respect to step <NUM>).

At step <NUM> of the present disclosure, the one or more hardware processors <NUM> of the PCRES <NUM> displays a message to a user of the machine based on the comparison. If the estimated time is found to be crossing the pre-defined threshold, the flag to change the rotary cutter pressure is raised by displaying the message to the user. The message includes a notification to change the pressure applied on the rotary cutter within the estimated time.

<FIG>, with reference to <FIG>, illustrates an example representation of comparison of the real-time rotary cutter usage data with the historical data associated with two rotary cutters for pressure change detection, in accordance with an embodiment of the present disclosure.

As seen in the <FIG>, for real-time pressure value of 'P', two historical profiles were available with TH1 and TH2 marking respective end of the rotary cutter life. It should be noted that the TH1 represents the time for historic knife "<NUM>" and TH1 represents the time for historic knife '<NUM>'. The rotary cutter can be seen at operating at time "t1" when the second trained model <NUM> is triggered. The real-time rotary cutter usage data for operational rotary cutter till time t1 is extracted and further compared with historical rotary cutters and based on the highest similarity between the usage data, the time for the next pressure change is estimated for the operational rotary cutter based on TH1 and TH2.

<FIG>, with reference to <FIG>, illustrates an example graphical representation of a polygon created for a rotary cutter, in accordance with an embodiment of the present disclosure.

As seen in the <FIG>, the graphical representation of the polygon shows that the upper limit confidence intervals and the lower limit confidence intervals that are obtained for the rotary cutter are enclosed and connected to create the polygon for the rotary cutter.

As discussed earlier, rotary cutters tend to lose material and sharpness over their usage cycle. So, to make it work for a longer time, a cylinder is used to impart a vertical force. The pressure that is to be applied to a rotary cutter is changed manually by an operator based on his/her experience. Most of the times, the pressure change is applied once the machine stops automatically as the rotary cutter cannot execute the cutting operation, thereby leading to wastage of operational time as well as the material which has to be discarded. Moreover, because of inefficient utilization of the rotary cutter, the rotary cutter loses its cutting property early that further leads to increase in cost as the rotary cutter needs to be changed frequently for proper functioning of the machine. To overcome these disadvantages, embodiments of the present disclosure provide methods and systems for real time estimation of pressure change requirements for rotary cutters. More specifically, the system identifies pressure change requirement while considering both time of usage as well as physics-based signals. Once the pressure change requirement is identified, the system automatically verifies and calculates the exact pressure change requirement, thereby guiding the system to automatically update the pressure applied on the rotary cutters in real-time. Basically, the system automatically predicts the next rotary cutter pressure change time based on historical rotary cutter usage data and physical parameters of the rotary cutter and alerts a user/operator of the machine to change the pressure when it is due, thereby mitigating the need for human judgement that further helps in ensuring optimal utilization of the rotary cutters and higher productivity while reducing the wastage of the operational time and the material.

Claim 1:
A processor implemented method (<NUM>), comprising:
receiving, by a pressure change requirement estimation system (PCRES) via one or more hardware processors,
(a) historical rotary cutter usage data associated with a rotary cutter, wherein the rotary cutter usage data comprises:
one or more discrete variables signifying an object that is cut by the rotary cutter, and
one or more continuous variables associated with the rotary cutter, wherein the one or more continuous variables comprises one or more of time-wise pressure data, a number of times the rotary cutter has been used, and manufacturer information associated with rotary cutter, and wherein the time-wise pressure data comprises time details when a pressure applied to the rotary cutter was changed,
(b) a real-time pressure value applied on the rotary cutter, and
(c) historical data associated with each rotary cutter of a plurality of rotary cutters, wherein the historical trends data comprises historical trends signifying loss of cutting property of each rotary cutter (<NUM>); estimating, by the PCRES via the one or more hardware processors, a minimum usage limit and a maximum usage limit for the rotary cutter at the real-time pressure value based, at least in part, on the historical rotary cutter usage data and the real-time pressure value using a first trained model (<NUM>);
monitoring, by the PCRES via the one or more hardware processors, real-time rotary cutter usage data to determine whether the minimum usage limit has reached for the rotary cutter, wherein the real-time rotary cutter usage data is received in real-time from a machine comprising the rotary cutter (<NUM>);
upon determining that the minimum usage limit has reached for the rotary cutter, estimating, by the PCRES via the one or more hardware processors, a time for a next pressure change based on one or more physical parameters of the rotary cutter using a second trained model, wherein the one or more physical parameters are determined based on the real-time rotary cutter usage data (<NUM>);
comparing, by the PCRES via the one or more hardware processors, the estimated time with the estimated maximum usage limit for the rotary cutter (<NUM>); and
displaying, by the PCRES via the one or more hardware processors, a message to a user of the machine based on the comparison, wherein the message comprises a notification to change the pressure applied on the rotary cutter within the estimated time (<NUM>).