WO2022097161A1

WO2022097161A1 - A system and a method to monitor and improve performance of cnc machines

Info

Publication number: WO2022097161A1
Application number: PCT/IN2021/050375
Authority: WO
Inventors: Gaurav Sarup; Siddhant Sarup
Original assignee: SARUP Gaurav
Priority date: 2020-11-04
Filing date: 2021-04-15
Publication date: 2022-05-12

Abstract

Embodiments of the present invention provide a system and a related method for monitoring and improving performance of a CNC machine. The system may include an auto-gauging device that may determine defective articles; machine sensors that may determine machine sensor data to predict accidental prone machine data, such as deviated vibration, noise or temperature, etc.; and an edge device that may predict future actions that may be implemented on the machine, based on extracted raw machine and related environment data. The system may further include a universal machine monitoring device that may communicate with each of auto-gauging device, the machine sensors and the edge device for receiving data information from them and determine one or more machine performance characteristics that may reflect and improve the performance of the machine, based on the received data information from each of the devices included in the system.

Description

A SYSTEM AND A METHOD TO MONITOR AND IMPROVE PERFORMANCE

OF CNC MACHINES

FIELD OF THE INVENTION

[001] The present invention is generally related to CNC machines. More particularly, the present invention relates to system and method for monitoring and improving performance of CNC machines.

BACKGROUND OF INVENTION

[002] For improvements in performances of machines, a number of factors would be counted, such as including productivity, quality of articles produced by them, machine accidents, quality and condition of the machine parts, skills of the operators, surrounding environment conditions, and the like. Several methods and systems have been developed to monitor functioning of the machine and collect statistics/data related thereto to analyze performance of the machine. Technologies have been provided for automatically collecting data related to the machines, which are later presented to the human operators, shop floor engineers and remote managers for them to analyze the data manually. Depending on the analysis of the operators/Engineers/Managers, the machines are regularly checked for any faults, alterations which can be made in the functions for improving quality of the end products, implementing steps or actions to minimize defects in quality of machine production, or any necessary action or preventive measures can be calculated manually by the operator and the like. “Machine production” refers to articles/parts produced by a machine, in the foregoing description. [003] Machine monitoring systems have been developed to monitor the working of machines in minute detail and record the working cycles. Further, machine monitoring systems may be implemented to record other factors such as the quality of the articles produced, and defect in the machine and its parts, or environment factors, etc, which may be helpful in analyzing performance of the machine and improving it.

[004] The manufacturing machines, such as CNC machines require skilled operators to operate. There may be times, such as financial epidemic, inflation, unemployment, labor strikes, natural pandemic, etc. where an organization may face acute shortage of labors, especially skilled labors. The time required to skill a fresh person may be long and the facilities available in such crisis times may be insufficient.

[005] Further, if unskilled/semi-skilled person may be employed and it may be attempted to train the person at work (may be over a period of 3-6 months), several problems may arise such as including poor productivity, poor quality in production, machine accidents/mishappening, etc.

[006] Therefore, there is a need of a system and method that may overcome the above- mentioned problems.

OBJECTIVES OF THE INVENTION

[007] It is an objective of the present invention to provide a system and a method that may allow even an unskilled labor to operate a manufacturing machine such as a CNC machine with even minimal skills

[008] It is an objective of the present invention to also ensure a jump in machine productivity, quality and machine safety. [009] It is another objective of the present invention to minimize defects in the articles produced by the machines operated by the unskilled labors.

[0010] It is yet another objective of the present invention to provide automatic (Compulsory) inspection of the articles produced by the machines operated by the unskilled labors by implementing gauging and automatically correcting errors in geometric tolerances of an article.

[0011] It is an objective of the present invention to detect accidental prone factors in the machine such as vibrations and CNC drive currents to avoid accidents. It is also an objective to detect occurrence of accidents and inform designated persons.

[0012] It is also objective of the present invention to provide a Cloud-based system for monitoring and improving performance (i.e. productivity) of the CNC machines.

[0013] To further clarify advantages and features of the present invention, a more elaborate description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope.

SUMMARY OF THE INVENTION

[0014] An aspect of the invention may provide a system for a CNC machine to monitor and improve productivity comprising: an auto-gauging device, integrated within the machine, for analysing one or more dimensional / geometric measurements of articles produced by the machine for determining one or more defective dimensional / geometric measurements, the auto-gauging device including a data analytical module to compare measured one or more dimensional / geometric measurements with standard/desired one or more dimensional / geometric tolerances to determine the one or more defective dimensional / geometric measurements and thereby a defective article produced by the machine;; one or more machine sensors, integrated within the machine, for sensing machine operation parameters, to determine accidental prone parameters of the machine based on sensed machine operation parameters for rectifying and avoiding said accidental prone parameters; a data transmission device, integrated within the machine, for autonomously determining one or more future actions to be implemented on the machine, by executing one or more closed loop analytics algorithms on extracted machine related data, the one or more future actions including one or more necessary corrective measures or actions to be implemented on the machine to optimize quality of articles produced by the machine, and the extracted machine related data includes any data or information related to machine; and a monitoring device, integrated within the machine, to receive machine data information from the auto-gauging device, the one or more machine sensors and the data transmission device, the machine data information including information at least related, at least in part, to the one or more geometric tolerances of articles, the one or more defective geometric tolerances, the sensed machine operation parameters, the extracted machine related data, the one or more future actions, individually or in combination, and where the monitoring device computes the machine data information together by applying one or more smart computing algorithms to determine one or more machine performance characteristics of the machine reflecting productivity and performance of the machine, and wherein the auto-gauging device, the one or more machine sensors, the data transmission device, and the monitoring device integrated within the machine communicate with each other to send and/or receive any required machine data information, while being integrated within the machine.

[0015] An aspect of the invention may provide the system further comprising a user interface, integrated within the machine, for communicating the machine data information and the machine performance characteristics to an operator; and wherein the user interface includes at least one of display screen, a touch sensitive display screen, a keypad, a touch keypad, a speaker, a microphone, a monitor, individually or in any combination.

[0016] An aspect of the invention provides that the machine data information and the one or more machine performance characteristics are communicated to at least a cloud server or on-premise servers, individually or in combination in order to store them and also to communicate to remote users or remote devices.

[0017] An aspect of the invention may provide the system further comprising one or more conventional measurement devices for measuring the one or more geometric tolerances and communicating them to the auto-gauging device for determining the one or more defective geometric tolerances; and wherein the conventional measurement devices include at least one of air gauges, one or more analog/digital probes or sensors, individually or in any combination.

[0018] An embodiment of the invention may provide the auto-gauging device sends information related to the one or more geometric tolerances in form of output digital signals to the monitoring device via a digital controller.

[0019] An embodiment of the invention may provide the monitoring device includes one or more computing devices communicating with the auto gauging device, the one or more machine sensors and the data transmission device to receive the machine data information; one or more monitoring devices, communicating with the computing device, to continuously monitor the machine, the articles and machine related environment, and wherein the monitoring device implements the computing algorithms including closed loop smart analytics algorithms to provide contextual insights based on the computation of the machine data information, and wherein the contextual insights include contextual information related to the machine and/or machine related environment or a machine operation cycle that has produced a defective article.

[0020] Another embodiment of the invention may provide the auto-gauging device further includes a data analytical module for analyzing the one or more geometric tolerances of the article to convert them from analog to digital; process them to detect trends of production; and sends auto-corrected one or more defective geometric tolerances to a correction unit; and wherein the data analytical module also determines whether the one or more defective geometric tolerances are re-workable or should be rejected completely; and wherein the auto-gauging device further includes a correction unit that comprises one or more microprocessors and processing algorithms for auto -correcting one or more geometric tolerance values in respect of the article based on identified deviation, when the deviation exists within a predetermined tolerance range in the one or more geometric tolerance values in respect of the article based on the comparison, such that corrected one or more geometric tolerance values in respect of the article is equivalent to one or more desired/standard geometric tolerances, and wherein based on the deviation, machine tool offsets, which may be producing or likely to produce in future defective articles, are corrected. [0021] A yet another embodiment of the invention may provide the one or more machine sensors are either digital or analog, individually or in combination, and include at least one of vibration sensors, noise sensors, temperature sensors, machine driving currents sensors individually or in combination to sense at least one of vibration, noise, temperature, machine driving currents of the machine, individually or in any combination; and wherein the one or more machine sensors also generate alerts in case of detection of accidental prone parameters.

[0022] An embodiment of the invention may provide the data transmission device includes a data extraction module for extracting the machine related data including raw and/or functioning machine data related to the machine; and a data analytics module to determine contextual insights related to the machine based on the extracted the machine related data, by implementing the one or more closed loop analytics algorithms on the extracted machine related data, the contextual insights including at least the one or more future actions.

[0023] An embodiment of the invention may provide the extracted machine related data that is extracted by the data transmission device includes, at least in part, readings depicting operations of the machine, including RPM, alignment of a lathe table, rotating angle, angle of operation of a tool, or quality /geometric measurements of end products produced by the machine, any data of the machine that depicts functioning and quality of produce of the machine, and the contextual insights provided by the data transmission device and by the monitoring device includes at least one of the one or more machine operations or conditions that are performed immediately prior to and/or during the machine cycle that has produced the defective article, one or more measured geometric tolerances of the article that are deviated from the one or more standard/desired geometric tolerances, along with whether the deviated one or more measured geometric tolerances of the article is either rejected or re-workable, other machine operations that may have produced the defective article, one or more actions for implementation at the machine or machine related environment to minimize production of defective articles, stipulated time in which the one or more actions should be implemented, or automatic alerts based on a predefined threshold in production of defective articles for alerting human resources, automatic alerts if the one or more actions are not implemented within the stipulated time, the quarantine digital signal indicating about the quarantined articles, information about the at least one auto-gauging station being mastered, or one or more measured geometric tolerances of the article that are extremely far deviated, individually or in any combination.

[0024] An embodiment of the invention may provide the data extraction module communicates with the one or more machine sensors to receive the sensed machine data of the machine and machine’s environment such as ambient temperature, vacuum, lighting condition to include in the extracted machine related data.

[0025] An embodiment of the invention may provide the one or more future actions include at least one of one or more necessary corrective measures to be implemented by the machine, one or more operating actions necessary to be implemented by the machine to optimize quality of products produced by machine tools, to improve tool life, to safeguard machine health and improve productivity, including changing angle of rotation of the cutting tool, or speed of rotation, or changing alignment of a lathe tool, individually or in any combination, the data transmission device also determines a time period and/or machine operating condition when a particular future action is to be employed at/by the machine. [0026] Another embodiment of the invention may provide the cloud server and/or onpremise servers store the closed loop analytics algorithms for being executed on the machine data information for determining the one or more machine performance characteristics.

[0027] An aspect of the invention may provide further includes a communicating module, and wherein the cloud server and/or on-premise servers communicate with the autogauging device, the one or more machine sensors, the data transmission device and the machine monitoring device via the communicating module to send or receive any machine related information from them; and wherein the communicating module is at least one or wired or wireless communication module, individually or in combination.

[0028] An embodiment of the invention may provide the machine performance characteristics include at least one of machine sensor accidental data including vibration sensor data, noise data, temperature data, driving forces data, the one or more geometric tolerances of the produced articles to determine defective articles, the one or more defective geometric tolerances, the one or more future actions to be implemented at the machine depending on one or more machine operating factors including machine operations, articles produced, raw machine data related to the machine, a safe time period to implement the future actions, machine operating cycles that may have produced the defective articles, the alerts in case of detection of accidental prone parameters, the contextual insights generated by the data transmission device including at least when a defective article is produced, which geometric tolerance in the defective article is deviated, and whether the deviated geometric tolerance is re-workable or completely rejected, reasons of production of the defective article, individually or in any combination. [0029] An aspect of the invention may provide the system further includes a validation module that further includes a camera installed at the machine, and where either the monitoring device or the validation module generates a validation code associated with the machine performance characteristics including the one or more future actions, and wherein validation of implementation and completion of the one or more future actions at/by the one or more machines and/or the machine related environment is validated by at least one of the camera included in the validation module, or a camera included in the monitoring device, or by inputting the validation code using the user interface at the machine, individually or in any combination.

[0030] An aspect of the present invention may provide a method for a CNC machine to monitor and improve productivity implementing the above system and one or more embodiments of the above system as described.

BRIEF DESCRIPTION OF DRAWINGS

[0031] For a better understanding of the embodiments of the systems and methods described herein, and to show more clearly how they may be carried into effect, references will now be made, by way of example, to the accompanying drawings, wherein like reference numerals represent like elements/components throughout and wherein:

[0032] FIG. 1 illustrates an exemplary block diagram of a system for monitoring and improving performance of a machine that may be operated by an unskilled operation, in accordance with an embodiment of the present invention; and [0033] FIG. 2 illustrates an exemplary flow diagram of a method for monitoring and improving performance of a machine that may be operated by an unskilled operation, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF INVENTION

[0034] This patent describes the subject matter for patenting with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. The principles described herein may be embodied in many different forms.

[0035] Illustrative embodiments of the invention now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout.

[0036] The present invention provides a system and a method for monitoring and improving performance of a machine which may be operated by an unskilled labor/operator. The system may comprise an auto gauging device, one or more machine sensors and an edge device (or a data transmission device) that may be either integrated into a machine or installed within a machine. The auto-gauging device may also be referred to as auto inspection device that may automatically inspect or gauge articles produced by the machine to check any defects in the articles. The auto-gauging device may include an analytical module or may communicate with an analytical module for receiving measurement attributes of the produced articles manually from the operators for analyzing the measurement attributes for any defects. The auto-gauging device with the analytical module may be integrated within the machine only. One or more conventionally used existing instruments of an operator for measuring the articles may be interfaced with the auto-gauging device including the analytical module via an interfacing module. Thus, the measurement attributes of the produced articles measured using the conventionally used existing measuring instruments of the operator may be communicated to the analytical module of the auto gauging device via the interfacing module, where the analytical module may further determine any production defects in the measurement attributes of the produced articles. The result of such analysis may be communicated to the operator via an interactive, easily operated user interface, which may or may not be integrated within the machine.

[0037] Further, the system includes one or more machine sensors installed or equipped with the machines. The one or more machine sensors may sense one or more machine operating attributes, such as vibration, noise, temperature, machine driving currents, and the like. The sensed data may be further analyzed to detect any accidental prone machine operating attributes. The sensors may also detect occurrence of accidents/collisions/tool breakages. Such machine sensors data may be communicated to the operator via an interactive, easily operated user interface, which may or may not be integrated within the machine. The sensed data may also be communicated to designated persons through Cloud or other communication systems, in an embodiment.

[0038] Furthermore, the system includes an edge device (and/or a digital data transmission device) that may be either integrated into a machine or installed within a machine. The edge device may determine autonomously one or more future actions to be implemented on the machine depending on one or more operating factors such as including and not limited to machine operations, articles produced, raw machine data related to the machine, and the like. Additionally, the edge device may also determine a safe time period within which a particular future action must be implemented. The operator may be instantly and quickly informed about any necessary future actions to be implemented on the machine via the edge device, which is at the machine only, thus avoiding any delay in the communication of such necessary future actions.

[0039] The system may also comprise a machine monitoring device that may be communicating with the machine and the devices installed within or at the machine, such as including and not limited to the auto-gauging device with the analytical module, the one or more machine sensors and the edge device. The machine monitoring device may be implemented for automatically generating and providing machine contextual insights related to machine operations, such as including and not limited to defective articles produced in a machine cycle in order to minimizing those defects in future production, one or more machine sensor data which may be offset, one or more future actions to be implemented on the machine, and the like.

[0040] The machine monitoring device may be able to communicate with people who are locally situated around the machine along with people are remotely situated. So, the machine monitoring device may provide machine contextual insights in real time to people globally, such as personnel on floor or managers at remote places. In an embodiment, the machine monitoring system may also be Cloud based, where any data can be communicated to and stored at a Cloud server and/or on-premise server from the machine monitoring device for access to anyone present globally. [0041] Further, the universal machine monitoring may be a closed loop autonomous system so that the system of the present invention may attain to achieve near zero or zero defective quality in machine production, after an unsatisfactory result is achieved from implementation of a corrective measure to correct the defective article.

[0042] FIG. 1 illustrates an exemplary block diagram of a system for monitoring and improving performance of a machine that may be operated by an unskilled operation, in accordance with an embodiment of the present invention. The system 100 may comprise a machine 102, such as a CNC machine, an auto-gauging device 104 installed at the machine 102, one or more machine sensors 106 installed within or at the machine 102, an edge device 108 installed within or at the machine 102, and a universal machine monitoring (may be referred to as “UMM”) device 110 that may be continuously monitoring and communicating with the machine 102 and its components. In an embodiment, all of the components of the system 100, the auto-gauging device 104, the one or more machine sensors 106, the edge device 108, and the UMM device 110 may be integrated within the machine 102, and may perform their functions. Also, the components of the system 100, the auto-gauging device 104, the one or more machine sensors 106, the edge device 108, and the UMM device 110 may be integrated within the machine 102 may also communicate with each other to communicate any required machine data to/from one component to another, while being integrated within the machine 102.

[0043] Further, the system 100 may also comprise a quarantining system or apparatus 112, placed next to the auto-gauging device 104, for quarantining defective articles as soon as they are detected by the auto-gauging device 104. The quarantine system 112 includes a quarantine bin that collects the defective articles. The quarantine system 112 also includes one or more sensors that confirm that the defective article is safely quarantined. These sensors may be termed as “quarantine sensors”. The quarantine sensors communicate with the UMM device 110 to send out a quarantine digital signal to the UMM device 110 for confirmation that the defective or non-conforming article is safely quarantined.

[0044] The system 100 may be cloud-based system. The UMM device 110, along with other components of the system 100 such as the machine 102, the auto-gauging device 104, the one or more machine sensors 106, the edge device 108, and the quarantining system 112 may communicate with remote devices and/or servers 114 via a cloud server 116. In an embodiment, users of the remote devices may receive emails as notifications from the components of the system 100 via the cloud server 116. Additionally, any data information from the components of the system including the machine 102, the auto-gauging device 104, the one or more machine sensors 106, the edge device 108, the UMM device 110 and the quarantining system 112 may be stored at the cloud server 116 (or in an On-Premise Server).

[0045] The components of the system 100 including the machine 102, the auto-gauging device 104, the one or more machine sensors 106, the edge device 108, the UMM device 110 and the quarantining system 112 may automatically operate and determine one or more machine performance characteristics of the machine 102 that may reflect and improve the performance of the machine 102. The machine performance characteristics, thus, may include and are not limited to machine sensor accidental data e.g. vibration sensor data, noise data, temperature data, driving forces data, etc., measurement properties of the produced articles to determine defective articles, one or more future actions that are needed to be implemented at the machine depending on one or more machine operating factors such as including and not limited to machine operations, articles produced, raw machine data related to the machine, etc., safe time period to implement these actions including other machine performance characteristics, machine operating cycles that may have produced the defective articles, the contextual insights generated by the edge device including when a defective article is produced, which geometric tolerance in the defective article is deviated, and whether the deviated geometric tolerance is re-workable or completely rejected, reasons of production of the defective article and the like.

[0046] The machine performance characteristics may also include the contextual insights, determined by the edge device, that includes at least one of the one or more machine operations or conditions that are performed immediately prior to and/or during the machine cycle that has produced the defective article, one or more measured geometric tolerances of the article that are deviated from the one or more standard/desired geometric tolerances, along with whether the deviated one or more measured geometric tolerances of the article is either rejected or re-workable, other machine operations that may have produced the defective article, one or more actions for implementation at the machine or machine related environment to minimize production of defective articles, stipulated time in which the one or more actions should be implemented, or automatic alerts based on a predefined threshold in production of defective articles for alerting human resources, automatic alerts if the one or more actions are not implemented within the stipulated time, the quarantine digital signal indicating about the quarantined articles, information about the at least one auto-gauging station being mastered, or one or more measured geometric tolerances of the article that are extremely far deviated, individually or in combination. [0047] Additionally, the machine 102 may be installed with a user interface 118 that may be interactively used by the operator. Any data information, such as the machine performance characteristics, from the machine 102, the auto-gauging device 104, the one or more machine sensors 106, the edge device 108, the UMM device 110 and the quarantining system 112 may be communicated to the operator via the user interface 118. The user interface 118 may include and is not limited to a display screen, a touch sensitive display screen, a keypad, a touch keypad, a speaker, a microphone, a monitor and the like, using which the operator may be informed about the functioning of the machine, future actions to be taken within the safe time period, and may also be able to input a required machine data, or a required instruction for the machine 102 to follow. The user interface 118 may be user friendly such that even an unskilled operator can interact.

[0048] An unskilled operator with minimal skills may easily operate the machine 102 for being informed about the important machine performance characteristics which may be determined using the auto-gauging device 104, machine sensors 106, the edge device 108 and the UMM device 110. All these components are automatic in nature and thus, require no or minimum skills from an external operator to operate and determine necessary machine performance characteristics which may be important to monitor and improve the performance of the machine.

[0049] The machine 102 may operate and produce articles. Produced articles may be measured to determine defects in the production. In an embodiment, the unskilled operator may use one or more conventional measurement devices for measuring one or more measurement properties, such as one or more geometric tolerances of the produced articles.

The measured properties of the articles may then be communicated to the auto -gauging device 104, which is integrated in the machine 102, for determining any defects in the measured properties of the articles. The conventional measurement devices may be connected with the auto-gauging device 104 for receiving the measured properties of the article. The auto-gauging device 104 may include a data analytical module 105 for analyzing the received measured properties of the article to convert them from analog to digital and then may process the measurements and may auto-correct the defective measurements as and when required.

[0050] Whenever a new job is to be machined, new air gauges may be connected to inbuilt auto-gauging device 104 and the measurement limits are set in the auto-gauging device 104 (which can be retrieved the next time same job is done). Unskilled Person does not need to know anything about machine screens or input buttons. He has to merely be taught how to unload and load job on machine workholding fixture and check the machined job with the conventional measurement devices provided to him.

[0051] In an embodiment, the data analytical module 105 may analyze the received measured properties of the article by comparing them to a standard or desired one or more geometric tolerances, and a deviation from the standard or desired one or more geometric tolerances in the measured properties may be determined. Based on the deviation, the auto- gauging device 104 may determine defective measured properties, and thus defective article and additionally, may further determine whether the measured properties of the article are re-workable or should be rejected completely.

[0052] The conventional measurement devices may include one or more probes or sensors for measuring measured properties of the article. In an embodiment, the one or more probes are digital probes. In another embodiment, the one or more probes are analogous probes. [0053] In an embodiment, the conventional measurement devices of the operator may be communicating with the auto gauging device 104 installed at the machine 102 via wired or wireless connections, and the measured properties of the article measured by the conventional measurement devices of the operator may be communicated to the auto gauging device 104 including the data analytical module 105 via the wired or wireless connections.

[0054] In an embodiment, the measured properties of the article measured by the conventional measurement devices of the operator may be manually fed into the machine via the user interface 118 at the machine 102. These manually fed measured properties of the article may be communicated to the auto gauging device 104 including the data analytical module 105.

[0055] In an embodiment, the auto gauging device 104 may automatically measure the properties of the articles produced.

[0056] Further, the defective measure properties of the article may be interactively communicated to the unskilled operator via the user interface 118. Thus, the unskilled operator may be instantly informed about the defective measure properties of the article.

[0057] All the information determined by the auto-gauging device 104, as explained above, may be collectively referred to as “inspected information”. It may be apparent to a person skilled in the art that the auto-gauging device 104 may also inspect and determine any other information related to machine, articles produced, the related environment, and others to include in the “inspected information”, without deviating from the meaning and scope of the present invention. [0058] In an embodiment, the auto-gauging device 104 may send such “inspected information”, including whether the defective article is re-workable or should be rejected completely, in form of output digital signals to the UMM device 110 via a digital controller. The UMM device 110 may include one or more computing devices communicating with the auto gauging device 104 via the digital controller to receive the output digital signals. The computing device may be an Internet of Things Box e.g. a laptop, that may store and execute one or more closed loop smart analytics algorithms for processing the output digital signals received from the auto gauging device 104 or the digital controller. Further, the UMM device 110 may include one or more monitoring devices, communicating with the computing device, to continuously monitor machine, article and the related environment; and extract data or information related to at least the machine, article and the related environment. The monitoring device may be a camera. The monitoring device may be an integrated or internal part of the computing device or may be an external part of the computing device.

[0059] Further, the UMM device 110 may implement the smart analytics algorithm to process the output digital signals in order to extract the “inspected information” from the output digital signals, related to the defective articles and the machine operations. The UMM device 110 may provide contextual insights based on the processing of the output digital signals and in addition, based on the continuously monitored data that is related to at least the machine or machine related environment or machine cycle that has produced the defective article. The contextual insights may include and is not limited to contextual information about the machine or machine related environment or the machine cycle that has produced the defective article. [0060] Since the UMM device 110 is continuously monitoring the whole machine production operations of the machine 102, the UMM device 110 may be able to continuously extract information about the machine 102, machine cycles, machine related environment, articles, work pieces or tools, and any other necessary machine related information. Hence, after receiving the output digital signals from the auto gauging device 104 about the defective articles and after processing the digital signals, the UMM device 110 may be able to determine one or more machine conditions or operations that may be performed immediately prior to and/or during the machine cycle that has produced the defective article. In an embodiment, such one or more machine conditions or operations may include and are not limited to machine start-up, tool change, or the machine re-started after a stoppage (e.g. a break of X minutes)/Lunch Break/Shift Change, or job changeover (New Job Set), and the like operations which may lead to production of defective articles. Such one or more machine conditions or operations which are determined by the UMM device 110 to identify the machine conditions or operations which are performed just before the production of the defective article may be collectively referred to as “monitored defective data” leading to production of the defective articles. It may be apparent to a person skilled in the art that the UMM device 110 may also monitor and determine any other information related to the machine operations/conditions that may lead to production of the defective articles to include in the “monitored defective data”, without deviating from the meaning and scope of the present invention.

[0061] The UMM device 110 may then compute or combine, using the smart analytics algorithm, the “monitored defective data” related to the one or more machine operations that may have led to production of defective articles with the “inspected information” extracted from the digital signals received from digital controller and the auto-gauging device 104. The UMM device 110 may then provide contextual insights based on the computation of the “monitored defective data” with the “inspected information”. The contextual insights may provide each and every possible information about production of the defective articles, such as including and not limited to when a defective article is produced, which geometric tolerance in the defective article is deviated, and whether the deviated geometric tolerance is re-workable or completely rejected, reasons of production of the defective article and the like. The contextual insights further may help in in-depth analysis and improvements of machine production. In an embodiment, the contextual insights are analysed by concerned people to evaluate one or more actions which can be implemented to improve the quality in machine production by minimizing defects. In an embodiment, the contextual insights are automatically analysed by the smart analytics algorithm to evaluate one or more actions which can be implemented to improve the quality in machine production by minimizing defects.

[0062] Such contextual insights about the production of defective articles, that includes at least one or more machine operations which may lead to production of defective articles, not only provides which article is defected or which geometric tolerance of the article is defected or at what time the defective article is detected or produced or other data, but also provides the context including the reasons based on the whole machine and machine related environment that may have led to the production of such defective articles.

[0063] Further, the contextual insights may also be used to determine one or more actions, such as corrective measures to be implemented for minimizing defects, and the time into which such actions should be taken. Furthermore, the contextual insights may also be used to generate alerts based on one or more pre-set rules/guidelines. The rules may be written by users to generate alerts in case of crossing of pre-set thresholds. Such alerts can be sent immediately to concerned people, whether locally or remotely situated.

[0064] Also, the auto-gauging device 104 may further include a correction unit that comprises one or more microprocessors and processing algorithms for auto -correcting one or more geometric measurements or tolerance values in respect of the article based on the identified deviation, when the deviation exists within a predetermined tolerance range in the one or more geometric tolerance values in respect of the article based on the comparison, such that corrected one or more geometric measurements or tolerance values in respect of the article is equivalent to stored one or more desired/standard geometric tolerances. Based on the deviation, the CNC tool offsets which may be producing defective articles may be corrected by the operator or automatically by the machine 102 or the system 100.

[0065] Therefore, the system 100 may provide real time contextualized quality information and automatic alerts to local operators, via the user interface 118, and also to remote people 114 via cloud server 116, such as floor managers, operators, or remote managers or buyers etc. for improving the quality in the machine production to achieve near zero or zero defective quality. Thus, the system 100 may ensure good machine productivity by detecting and correcting defective articles, using the auto gauging device 104 with the UMM device 110.

[0066] Further, the system 100 may also include one or more machine sensors 106, installed at or in the machine 102, for sensing machine operation parameters, such as including and not limited to vibration, noise, temperature, machine driving currents and the like. The one or more machine sensors 106 may include and are not limited to vibration sensors, noise sensors, temperature sensors, machine driving currents sensors and the like. These sensed machine operation parameters may be important in determining any accidental prone parameters of the machines. The sensed machine operation parameters may be communicated to the UMM device 110 for the UMM device 110 to determine the accidental prone parameters of the machine based on the sensed machine operation parameters. In an embodiment, the UMM device 110 may include the sensed machine operation parameters to determine monitored defective data. Thus, the machine sensors 106 may ensure machine safety by predicting any accidental prone machine parameters from the sensed machine operation parameters. The sensed machine operation parameters may also be communicated to the operator via the user interface 118, in an embodiment.

[0067] In an embodiment, the machine sensors 106 may also alert the operator or Supervisor/Manager the moment some mishap happens. This has benefits including it may allow inspection of machine to see that key alignments are intact and that process capability is not affected; and unskilled person may be taught what to do and what not to do so that mishap is not repeated; and unskilled person may operate machine very carefully (strictly follows instructions) because he/she knows that mishap cannot be hidden.

[0068] Furthermore, the system 100 may also include the edge device 108 installed at/in the machine 102. The edge device 108 may further include a data extraction module 109A, a data analytics module 109B executing one or more data analytics algorithms, and a feedback module.

[0069] The edge device 108 may implement the data extraction module 109 A for extracting all raw and/or functioning machine data related to the machine 102. In an embodiment, the data analytics module 109B may also extract the machine related data. Machine data that is extracted by the edge device 108 using the data extraction module 109A may be related to and not limited to readings depicting operations of the machine, such as RPM, alignment of a lathe table, rotating angle, angle of operation of a tool, or quality/measurements of end products produced by the machine 102. Any data of the machine that depicts functioning and quality of produce of the machine may be extracted by the edge device 108. Further, in an embodiment, the data extraction module 109A may also communicate with the machine sensors 106 to extract data of the machine and machine’s environment such as ambient temperature, vacuum, lighting condition, etc. using devices like room thermometer, etc.

[0070] Further, the data extraction module may provide all the extracted machine data to the data analytics module 109B of the edge device 108 for evaluation of the machine data. The data analytics module 109B may execute and implement one or more closed loop analytics algorithms on the machine data for obtaining insights of the machine 102 and the machine’s environment. Further, the data analytics module 109B may also evaluate the machine data for autonomously determining or predicting one or more future actions for the machine 102 to employ, depending on the extracted machine data and/or the data related to the machine’s environment. The future actions autonomously predicted by the edge device 108 may include and not limited to one or more necessary corrective measures that should be taken by the machine, one or more operating actions necessary to be implemented by the machine to optimize quality of products produced by machine tools, to improve tool life, to safeguard machine health and improve productivity, for example changing angle of rotation of the cutting tool, or speed of rotation, or changing alignment of a lathe tool, etc. The data analytics module 109B may employ one or more closed loop data analytics algorithms to arrive at certain conclusions and insights which can then be used by human experts to take necessary decisions.

[0071] The data analytics module 109B of the edge device 108 may not only autonomously determines future actions to be employed at/by the machine 102, but also instructs the machine 102 and/or the operators to initiate those future actions. This means, once the data analytics module 109 determines one or more future actions; the data analytics module 109B may communicate these future actions to the machine 102 and may instruct it to employ these actions. The future actions may also be communicated to the operator, via the user interface 118, in situations where operator’s assistance or supervision is needed, following which the operator implements the actions on the machines. The data analytics module 109B may also determine a time period and/or machine operating condition when a particular future action should be employed at/by the machine 102.

[0072] In an embodiment, all the machine information and data and/or the data related to machine environment, extracted by the edge device 108, may be transmitted to a cloudbased server 116, and may be stored in the cloud server 116. The cloud server 116 may store the closed loop analytics algorithms for being executed on the extracted data. The cloud server 116 may be in communication with the edge device 108 via a communicating module, and may receive all the extracted data from the edge device 108. Receiving the extracted machine data from the edge device 108, the cloud-based server 116 may run or execute the closed loop analytics algorithms on the extracted machine data for determining one or more future actions to be implemented on the machines. The cloud-based server 116 may also store the one or more future actions. The edge device 108 may remotely communicate with human resources and global offices, via the cloud-based server 116, to communicate the machine information the machine data and the machine related data and the one or more future actions through a wireless connection over a network such the Internet.

[0073] In an embodiment, the edge device 108 may also communicate the extracted machine data and the machine environment related data to the UMM device 110 also. The UMM device 110 may then include the extracted machine data and the machine environment related data into the inspected information to determine monitored data. Thus, along with receiving inspected information from the auto-gauging device 104 and sensed machine parameters from the one or more machine sensors 106, the UMM device 110 may also receive extracted machine data and the machine environment related data from the edge device 108, and may compute the received data together by applying the computing algorithms to determine the one or more machine performance characteristics of the machine 102 that may reflect and improve the performance of the machine 102. The machine performance characteristics, thus, may include and are not limited to machine sensor accidental data e.g. vibration sensor data, noise data, temperature data, driving forces data, etc., measurement properties of the produced articles to determine defective articles, one or more future actions that are needed to be implemented at the machine depending on one or more machine operating factors such as including and not limited to machine operations, articles produced, raw machine data related to the machine, etc., safe time period to implement these actions including other machine performance characteristics and the like, that may include any data information regarding the machine

102 and its related environment. [0074] In another embodiment, the system 100 may also generate a validation code, which is generated along with determining a particular corrective action. Therefore, every corrective action may be provided with a validation code. The validation code is provided to the operator, by the system 100 via the user interface 118, which the operator may use to inform the system 100 about execution of the particular corrective action. Therefore, after or while executing a particular corrective action at the machine 102 within the safe time period, or before initiating a particular corrective action at the machine 102, the operator must input a corresponding generated validation code into the system 100 via the user interface 118 to inform the system 100 that the particular corrective action has been executed. In an embodiment, the validation code is generated by a validation module 120. In an embodiment, the validation code is generated by the UMM device 110. The validation module 120 ensures providing a feedback or a validation to the system 100, while a corrective action is implemented at the machine 102 within the safe time period. In another embodiment, the validation code is generated using the data analytics module of the UMM device 110 or the edge device 108. This way, the system 100 is regularly informed about execution of the predicted corrective actions, and hence, the system 100 can effectively ensure satisfactory results by the machine 102 by monitoring and analyzing the functioning of the machine 102 and production of quality products.

[0075] Hence, the system 100 is able to determine that a particular corrective action has been executed or not by either keeping a check on the completion of the safe time period calculated corresponding to the particular corrective action or by being informed via input of the validation code by the operator. In situations where a validation code is not provided to the system 100 by the operator within the safe time period, the system 100 may take a number of measures, such as including and not limited to stopping the machine 102, or may predict a next possible future action to be implemented depending on current machine data and thereafter, or may alarm or notify the operators about the situation.

[0076] In an embodiment, the validation code is an OTP (one-time password), which may be provided to the operator via the user interface 118, or communicated to the operator at his user device via cloud servers or on-premise servers. In an embodiment, the system 100 may employ the UMM device 110 to ensure the implementation and completion of the corrective actions. The UMM device 110 may employ its one or more monitoring devices such as camera to ensure the implementation and completion of the corrective actions, where the camera may be installed at the machine 102.

[0077] FIG. 2 illustrates an exemplary flow chart of a method for monitoring and improving performance of a machine that may be operated by an unskilled operation, in accordance with an embodiment of the present invention. The method 200 should be read and understood in conjunction with the FIG. 1, and include at least one or more of the embodiments of the system described in the FIG. 1. Further, the method 200 may or may not follow a step flow as described by steps 202-210 in the method flowchart 200 in Fig.

2.

[0078] The method 200 includes a step 202 of measuring one or more measurement properties, such as one or more geometric tolerances, of an article produced the machine. Produced articles may be measured to determine defects in the production. In an embodiment, the unskilled operator may use one or more conventional measurement devices for measuring one or more measurement properties of the produced articles. The step 202 may further include communicating the measured properties of the articles to the auto-gauging device 104 for determining any defects in the measured properties of the articles. The conventional measurement devices may be connected with the auto-gauging device 104 for receiving the measured properties of the article. The auto-gauging device 104 may include a data analytical module 105 for analyzing the received measured properties of the article by comparing them to a standard or desired one or more geometric tolerances, and determining a deviation from the standard or desired one or more geometric tolerances in the measured properties. Based on the deviation, the auto-gauging device 104 may determine defective measured properties, and thus defective article and additionally, may further determine whether the measured properties of the article are re-workable or should be rejected completely. In an embodiment, the auto-gauging device may also use its algorithms to detect trend of any measured dimension to predict that it may move out of prescribed limits and thereby send correction value to CNC control to bring that dimension close to the mean of tolerance when the next article is produced.

[0079] All the information determined by the auto-gauging device 104, as explained above, may be collectively referred to as “inspected information”. In an embodiment, at a step 210, the auto-gauging device 104 may send such “inspected information”, including whether the defective article is re-workable or should be rejected completely, in form of output digital signals to the UMM device 110 via a digital controller.

[0080] The method 200 may further include a step 204 of sensing, by one or more machine sensors 106 installed at or in the machine 102, machine operation parameters, such as including and not limited to vibration, noise, temperature, machine driving currents and the like. These sensed machine operation parameters may be important in determining any accidental prone parameters of the machines. At a step 210, the sensed machine operation parameters may be communicated to the UMM device 110 for the UMM device 110 to determine the accidental prone parameters of the machine based on the sensed machine operation parameters.

[0081] The method 200 may also include a step 206 executing and implementing one or more closed loop analytics algorithms, by the edge device 108, on the extracted machine data for obtaining insights of the machine 102 and the machine’s environment. The extracted machine data may be extracted by a data extraction module 109 A of the edge device, where the extracted machine data may be related to and not limited to readings depicting operations of the machine, such as RPM, alignment of a lathe table, rotating angle, angle of operation of a tool, or quality/measurements of end products produced by the machine 102. Any data of the machine 102 that depicts functioning and quality of produce of the machine may be extracted by the edge device 108. Further, in an embodiment, the data extraction module 109 A may also communicate with the machine sensors 106 to extract data of the machine and machine’s environment such as ambient temperature, vacuum, lighting condition, etc. using devices like room thermometer, etc.

[0082] Further, the data analytics module 109B may also evaluate the machine data, at step 206, for autonomously determining or predicting one or more future actions for the machine 102 to employ, depending on the extracted machine data and/or the data related to the machine’s environment.

[0083] In an embodiment, the edge device 108, at step 210, may also communicate the extracted machine data and the machine environment related data to the UMM device 110 also. [0084] At a step 210, the UMM device 110 may implement the closed loop smart analytics algorithms to process all data information received from the auto-gauging device 104, one or more machine sensors 106 and the edge device 108. Thus, at step 210, the UMM 110 may receive inspected information from the auto-gauging device 104, the sensed machine parameters from the one or more machine sensors 106, and extracted machine data and the machine environment related data from the edge device 108, and then may compute the received data information together by applying the computing algorithms such as including closed loop smart analytics algorithms, to determine the one or more machine performance characteristics of the machine 102 that may reflect and improve the performance of the machine 102. The machine performance characteristics, thus, may include and are not limited to machine sensor accidental data e.g. vibration sensor data, noise data, temperature data, driving forces data, etc., measurement properties of the produced articles to determine defective articles, one or more future actions that are needed to be implemented at the machine depending on the one or more machine operating factors such as including and not limited to machine operations, articles produced, raw machine data related to the machine, etc., safe time period to implement these actions including other machine performance characteristics and the like, that may include any data information regarding the machine and its related environment.

[0085] Additionally, the defective measure properties of the article may be interactively communicated to the unskilled operator via the user interface 118, at step 208. Thus, the unskilled operator may be instantly informed about the defective measure properties of the article. Also, the future actions may also be communicated to the operator, via the user interface 118 at step 208, in situations where operator’s assistance or supervision is needed, following which the operator implements the actions on the machines. Additionally, the sensed machine data by the one or more sensors 106 may be provided to the operator, via the user interface 118, at step 208.

[0086] Thus, advantageously, the system 100 and the related method 200 may ensure good machine productivity and quality by implementing the auto-gauging device 104 and the edge device 108 along with the UMM device 110. Additionally, the systemlOO and the related method 200 may ensure safety of the machine 102 and also the operators and related environment using the machine sensors 106 along with the UMM 110. Further, the systemlOO and the related method 200 may provide the one or more machine performance characteristics of the machine 102 to the unskilled operator interactively via the user interface 118. Thus, even an unskilled operator operating the machine 102 may use only conventional measuring devices to measure the articles and may be informed, via the user interface 118, about the one or more machine performance characteristics of the machine 102, including the accidental probe data, defective measured properties of defective articles and future actions for the machine.

[0087] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. [0088] Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

[0089] The components of the system including the Devices and related technologies mentioned above are collectively used to improve performance of the CNC machine in three key areas of QUALITY of articles produced, PRODUCTIVITY of the machine and protection of machine HEALTH and are generally an INTEGRAL part of the CNC machine, making it a unique type of CNC machine.

[0090] Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

Claims

CLAIMS m for a CNC machine to monitor and improve productivity comprising: an auto-gauging device, integrated within the machine, for analysing one or more dimensional / geometric measurements of articles produced by the machine for determining one or more defective dimensional / geometric measurements, the auto-gauging device including a data analytical module to compare measured one or more dimensional / geometric measurements with standard/desired one or more dimensional / geometric tolerances to determine the one or more defective dimensional / geometric measurements and thereby a defective article produced by the machine; one or more machine sensors, integrated within the machine, for sensing machine operation parameters, to determine accidental prone parameters of the machine based on sensed machine operation parameters for rectifying and avoiding said accidental prone parameters; a data transmission device, integrated within the machine, for autonomously determining one or more future actions to be implemented on the machine, by executing one or more closed loop analytics algorithms on extracted machine related data, the one or more future actions including one or more necessary corrective measures or actions to be implemented on the machine to optimize quality of articles produced by the machine, and the extracted machine related data includes any data or information related to machine; and a monitoring device, integrated within the machine, to receive machine data information from the auto-gauging device, the one or more machine sensors and the data transmission device, the machine data information including information at least related, at least in part, to the one or more dimensional / geometric measurements of articles, the one or more defective dimensional / geometric measurements, the sensed machine operation parameters, the extracted machine related data, the one or more future actions, individually or in combination, and where the monitoring device computes the machine data

35 information together by applying one or more smart computing algorithms to determine one or more machine performance characteristics of the machine reflecting productivity and performance of the machine, and wherein the auto-gauging device, the one or more machine sensors, the data transmission device, and the monitoring device integrated within the machine communicate with each other to send and/or receive any required machine data information, while being integrated within the machine.

2. The system of claim 1 further comprising a user interface, integrated within the machine, for communicating the machine data information and the machine performance characteristics to an operator; and wherein the user interface includes at least one of display screen, a touch sensitive display screen, a keypad, a touch keypad, a speaker, a microphone, a monitor, individually or in any combination.

3. The system of claim 2, wherein the machine data information and the one or more machine performance characteristics are communicated to at least a cloud server or onpremise servers, individually or in combination in order to store them and also to communicate to remote users or remote devices.

4. The system of claim 2 further comprising one or more conventional measurement devices for measuring the one or more dimensional / geometric measurements and communicating them to the auto-gauging device for determining the one or more defective dimensional / geometric measurements; and wherein the conventional measurement devices include at least one of air gauges, one or more analog/digital probes or sensors, individually or in any combination.

5. The system of claim 1, wherein the auto-gauging device sends information related to the one or more dimensional / geometric measurements in form of output digital signals to the monitoring device via a digital controller.

6. The system of claim 1, wherein the monitoring device includes one or more computing devices communicating with the auto gauging device, the one or more machine sensors and the data transmission device to receive the machine data information; one or more monitoring devices, communicating with the computing device, to continuously monitor the machine, the articles and machine related environment, and

36 wherein the monitoring device implements the computing algorithms including closed loop smart analytics algorithms to provide contextual insights based on the computation of the machine data information, and wherein the contextual insights include contextual information related to the machine and/or machine related environment or a machine operation cycle that has produced a defective article. The system of claim 1, wherein the auto-gauging device further includes a data analytical module for analysing the one or more dimensional / geometric measurements of the article to convert them from analog to digital; process them to detect trends of production; and sends auto-corrected one or more defective dimensional / geometric measurements to a correction unit; and wherein the data analytical module also determines whether the one or more defective dimensional / geometric measurements are re- workable or should be rejected completely; and wherein the auto-gauging device further includes a correction unit that comprises one or more microprocessors and processing algorithms for auto-correcting one or more defective dimensional / geometric measurements values in respect of the article based on identified deviation, when the deviation exists within a predetermined tolerance range in the one or more geometric tolerance values in respect of the article based on the comparison, such that corrected one or more dimensional / geometric measurements in respect of the article is equivalent to one or more desired/standard geometric tolerances, and wherein based on the deviation, machine tool offsets, which may be producing or likely to produce in future defective articles, are corrected. The system of claim 1, wherein the one or more machine sensors are either digital or analog, individually or in combination, and include at least one of vibration sensors, noise sensors, temperature sensors, machine driving currents sensors individually or in combination to sense at least one of vibration, noise, temperature, machine driving currents of the machine, individually or in any combination; and wherein the one or more machine sensors also generate alerts in case of detection of accidental prone parameters. The system of claim 6, wherein the data transmission device includes a data extraction module for extracting the machine related data including raw and/or functioning machine data related to the machine; and a data analytics module to determine contextual insights related to the machine based on the extracted the machine related data, by implementing the one or more closed loop analytics algorithms on the extracted machine related data, the contextual insights including at least the one or more future actions.

10. The system of claim 9, wherein the extracted machine related data that is extracted by the data transmission device includes, at least in part, readings depicting operations of the machine, including RPM, alignment of a lathe table, rotating angle, angle of operation of a tool, or quality/geometric measurements of end products produced by the machine, any data of the machine that depicts functioning and quality of produce of the machine.

11. The system of claim 10, wherein the contextual insights provided by the data transmission device and by the monitoring device includes at least one of the one or more machine operations or conditions that are performed immediately prior to and/or during the machine cycle that has produced the defective article, one or more measured dimensional / geometric measurements of the article that are deviated from the one or more standard/desired geometric tolerances, along with whether the deviated one or more measured dimensional / geometric measurements of the article is either rejected or re-workable, other machine operations that may have produced the defective article, one or more actions for implementation at the machine or machine related environment to minimize production of defective articles, stipulated time in which the one or more actions should be implemented, or automatic alerts based on a predefined threshold in production of defective articles for alerting human resources, automatic alerts if the one or more actions are not implemented within the stipulated time, the quarantine digital signal indicating about the quarantined articles, information about the at least one autogauging station being mastered, or one or more measured dimensional / geometric measurements of the article that are extremely far deviated, individually or in any combination.

12. The system of claim 9, wherein the data extraction module communicates with the one or more machine sensors to receive the sensed machine data of the machine and machine’s environment such as ambient temperature, vacuum, lighting condition to include in the extracted machine related data.

13. The system of claim 1, wherein the one or more future actions include at least one of one or more necessary corrective measures to be implemented by the machine, one or more operating actions necessary to be implemented by the machine to optimize quality of products produced by machine tools, to improve tool life, to safeguard machine health and improve productivity, including changing angle of rotation of the cutting tool, or speed of rotation, or changing alignment of a lathe tool, individually or in any combination, the data transmission device also determines a time period and/or machine operating condition when a particular future action is to be employed at/by the machine.

14. The system of claim 3, wherein the cloud server and/or on-premise servers store the closed loop analytics algorithms for being executed on the machine data information for determining the one or more machine performance characteristics.

15. The system of claim 3 further includes a communicating module, and wherein the cloud server and/or on-premise servers communicate with the auto-gauging device, the one or more machine sensors, the data transmission device and the machine monitoring device via the communicating module to send or receive any machine related information from them; and wherein the communicating module is at least one or wired or wireless communication module, individually or in combination.

16. The system of claim 11, wherein the machine performance characteristics include at least one of machine sensor accidental data including vibration sensor data, noise data, temperature data, driving forces data, the one or more dimensional / geometric measurements of the produced articles to determine defective articles, the one or more defective dimensional / geometric measurements, the one or more future actions to be implemented at the machine depending on one or more machine operating factors including machine operations, articles produced, raw machine data related to the machine, a safe time period to implement the future actions, machine operating cycles that may have produced the defective articles, the alerts in case of detection of accidental prone parameters, the contextual insights generated by the data transmission device including at least when a defective article is produced, which geometric measurement in the defective article is deviated, and whether the deviated geometric measurement is re-workable or completely rejected, reasons of production of the defective article, individually or in any combination.

17. The system of claim 2 further includes a validation module that further includes a camera installed at the machine, and wherein either the monitoring device or the validation module generates a validation code associated with the machine performance characteristics including the one or more future actions, and wherein validation of implementation and completion of the one or more future actions at/by the one or more machines and/or the machine related environment is validated by at least one of the

39 camera included in the validation module, or a camera included in the monitoring device, or by inputting the validation code using the user interface at the machine, individually or in any combination.

18. A method for a CNC machine to monitor and improve productivity comprising: analysing one or more dimensional / geometric measurements of articles produced by the machine, by an auto-gauging device, for determining one or more defective dimensional / geometric measurements , the auto-gauging device including a data analytical module to compare measured one or more dimensional / geometric measurements with standard/desired one or more geometric tolerances to determine the one or more defective dimensional / geometric measurements and thereby a defective article produced by the machine; sensing machine operation parameters, by one or more machine sensors, to determine accidental prone parameters of the machine based on sensed machine operation parameters for rectifying and avoiding said accidental prone parameters; autonomously determining one or more future actions, by a data transmission device, to be implemented on the machine, by executing one or more closed loop analytics algorithms on extracted machine related data, the one or more future actions including one or more necessary corrective measures or actions to be implemented on the machine to optimize quality of articles produced by the machine, and the extracted machine related data includes any data or information related to machine; receiving machine data information, by a monitoring device from the autogauging device, the one or more machine sensors and the data transmission device, the machine data information including information at least related, at least in part, to the one or more dimensional / geometric measurements of articles, the one or more defective dimensional / geometric measurements, the

40 sensed machine operation parameters, the extracted machine related data, the one or more future actions, individually or in combination; and computing, by the monitoring device, the machine data information together by applying one or more smart computing algorithms to determine one or more machine performance characteristics of the machine reflecting productivity and performance of the machine, and wherein the auto-gauging device, the one or more machine sensors, the data transmission device, and the monitoring device are integrated within the machine, and communicate with each other to send and/or receive any required machine data information, while being integrated within the machine. The method of claim 18 further comprising communicating the machine data information to an operator via a user interface integrated within the machine, and wherein the user interface includes at least one of display screen, a touch sensitive display screen, a keypad, a touch keypad, a speaker, a camera, a microphone, a monitor, individually or in combination. The method of claim 19 further comprising communicating, to at least a cloud server or on-premise servers, individually or in combination, the machine data information and the one or more machine performance characteristics in order to store and communicate the machine data information and the one or more machine performance characteristics to remote users or remote devices. The method of claim 18, wherein the monitoring device includes one or more computing devices communicating with the auto gauging device, the one or more machine sensors and the data transmission device to receive the machine data information; one or more monitoring devices, communicating with the computing device, to continuously monitor the machine, the articles and machine related environment, and wherein the monitoring device implements the computing algorithms including closed loop smart analytics algorithms to provide contextual insights based on the computation of the machine data information, and wherein the contextual insights

41 include contextual information related to the machine and/or machine related environment or a machine operation cycle that has produced a defective article.

22. The method of claim 18, wherein the auto-gauging device further includes a data analytical module for analysing the one or more dimensional / geometric measurements of the article to convert them from analog to digital; process them to detect trends of production; and sends auto-corrected one or more defective dimensional / geometric measurements to a correction unit; and wherein the data analytical module also determines whether the one or more defective dimensional / geometric measurements are re- workable or should be rejected completely; and wherein the system further comprising auto-correcting one or more defective dimensional / geometric measurements in respect of the article based on identified deviation, by a correction unit included in the auto-gauging device, wherein the correction unit comprises one or more microprocessors and processing algorithms for auto-correcting, when the deviation exists within a predetermined tolerance range in the one or more geometric tolerance values in respect of the article based on the comparison, such that corrected one or more dimensional / geometric measurements in respect of the article is equivalent to one or more desired/standard geometric tolerances, and wherein based on the deviation, machine tool offsets, which may be producing defective articles or likely to produce in future, are corrected.

23. The method of claim 18, wherein the one or more machine sensors are either digital or analog, individually or in combination, and include at least one of vibration sensors, noise sensors, temperature sensors, machine driving currents sensors individually or in any combination to sense at least one of vibration, noise, temperature, machine driving currents of the machine, individually or in combination.

24. The method of claim 21, wherein the data transmission device includes a data extraction module for extracting the machine related data including raw and/or functioning machine data related to the machine; and a data analytics module to determine contextual insights related to the machine, by implementing the one or more closed loop analytics algorithms on the extracted machine related data, the contextual insights including at least the one or more future actions.

25. The method of claim 24, wherein the contextual insights provided by the data transmission device and by the monitoring device includes at least one of the one or more machine operations or conditions that are performed immediately prior to and/or

42 during the machine cycle that has produced the defective article, one or more measured dimensional / geometric measurements of the article that are deviated from the one or more standard/desired geometric tolerances, along with whether the deviated one or more measured dimensional / geometric measurements of the article is either rejected or re-workable, other machine operations that may have produced the defective article, one or more actions for implementation at the machine or machine related environment to minimize production of defective articles, stipulated time in which the one or more actions should be implemented, or automatic alerts based on a predefined threshold in production of defective articles for alerting human resources, automatic alerts if the one or more actions are not implemented within the stipulated time, the quarantine digital signal indicating about the quarantined articles, information about the at least one autogauging station being mastered, or one or more measured dimensional / geometric measurements of the article that are extremely far deviated, individually or in any combination. The method of claim 20 further includes a communicating module, and wherein the cloud server and/or on-premise servers communicate with the auto-gauging device, the one or more machine sensors, the data transmission device and the machine monitoring device via the communicating module to send or receive any machine related information from them; and wherein the communicating module is at least one or wired or wireless communication module, individually or in combination. The method of claim 22, wherein the machine performance characteristics include at least one of machine sensor accidental data including vibration sensor data, noise data, temperature data, driving forces data, the one or more dimensional / geometric measurements of the produced articles to determine defective articles, the one or more defective dimensional / geometric measurements, the one or more future actions to be implemented at the machine depending on one or more machine operating factors including machine operations, articles produced, raw machine data related to the machine, a safe time period to implement the future actions, machine operating cycles that may have produced the defective articles, the alerts in case of detection of accidental prone parameters, the contextual insights generated by the data transmission device including at least when a defective article is produced, which geometric tolerance in the defective article is deviated, and whether the deviated geometric

43 tolerance is re- workable or completely rejected, reasons of production of the defective article, individually or in any combination. The method of claim 19 includes generating a validation code, either by the monitoring device or a validation module with a camera installed at the machine, and wherein the validation code is associated, at least in part, with the machine performance characteristics including the one or more future actions, and wherein validation of implementation and completion of the one or more future actions at/by the one or more machines and/or the machine related environment is validated by at least one of the camera included in the validation module, or a camera included in the monitoring device, or by inputting the validation code using the user interface at the machine, individually or in any combination.

44