COMPUTER IMPLEMENTS SYSTEM AND METHOD FOR ASSISTING THE DESIGN OF MANUFACTURED COMPONENTS REQUIRING POST-PROCESSING

A system for assisting the design of manufactured components requiring post-processing and comprising an input module receiving original design data relative to engineering requirements of a component and manufacturing and post-processing data relative to the component. The system also comprising a compensation determination module receiving the original design data and the manufacturing and post-processing data from the input module, predicting the geometrical deviations created by the at least one post-processing procedure and generating dimension compensation data defining compensations for each one of the features of the component using at least one machine learning model generated by a machine learning algorithm trained using a training dataset. The system further comprises a compensated model generation module configured to receive the compensation data and to generate a compensated Computer Assisted Design (CAD) model therefrom. A computer implemented method for assisting the design of manufactured components requiring post-processing is also provided.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the field of industrial manufacturing of components. More particularly, it relates to a system and a method for assisting the design of manufactured components (or parts) requiring post-processing. In an embodiment, the system and method allow prediction of geometrical deviations between an original 3D model of a component to be fabricated and the final post-processed component and the corresponding adjustments of the 3D model of the component, in order to compensate for dimensional changes which will occur during the manufacturing and post-processing phases.

BACKGROUND

In the field of industrial component manufacturing, it is often required to manufacture components having specific engineering requirements such as, for example and without being limitative, dimensional characteristics, surface roughness, tolerances, etc.

In recent years, a common method used to perform such manufacturing is additive manufacturing, where material is joined to make objects from 3D model data, for example, layer upon layer. This process can however cause deviations, distortions or deformations during the manufacturing process, which leads to components not meeting the geometrical precision requirements. For example and without being limitative, one problem associated with additive manufacturing is associated to part shrinkage which occurs during manufacturing and can lead to the manufactured parts being outside of the accepted dimensional tolerances.

In order to alleviate, this issue, different methods for predicting deviations during the additive manufacturing process and compensating for the predicted deviation have been used. Such methods include 3D printing shrinkage compensation, compensating 3D shape deviations, distortion prediction, minimization for additive manufacturing, etc.

For example, U.S. Pat. No. 9,886,526B2 describes a system and method for compensation of the dimensions for anticipated shrinkage calculated based on the shrinkage information that is associated with a selected shape from a database of different shapes and their associated shrinkage values, which most closely corresponds to the shape of a layer to be printed. The document also describes modifying the information indicative of the shape of the layer to be printed based on the calculated compensation, to minimize errors cause by shrinkage.

US patent application no. US20160320771A1 describes another possible method for predicting deformation errors and compensating for shape deviation in additive manufacturing. In this document, the technology described encompasses scanning a model component manufactured using a specific additive manufacturing machine and generating a point cloud file for the manufactured model component; and comparing the scanned model to the original design (i.e. the CAD model) to generate compensations to be performed from the original design to compensate for deformation during the manufacture of the component by the additive manufacturing machine.

U.S. Pat. No. 9,950,476B2 describes yet another possible method for minimizing distortions in a workpiece manufactured by additive manufacturing. The method described therein uses finite element analysis on a finite element model of the component to conduct a thermomechanical analysis for predicting warping and post-printing deformation. The predicted deformation is used for introducing alterations to the workpiece prior to or during fabrication of the component, to compensate for the predicted deviations.

Other documents describing solutions for minimizing deviations in a workpiece occurring during the manufacture of the workpiece includes, for example, US patent application no. 2018/0341248A1, which describes the use of machine learning-based methods and systems for automated object defect classification of a design geometry of an object (i.e. an original 3D Computer Assisted Design (CAD) model) and adaptive, real-time control of additive manufacturing and/or welding processes for this object.

In view of the above, solutions for compensating for deviations occurring during the manufacturing process of a component (e.g. in the course of an additive manufacturing process) have been proposed. As can be seen, the above-mentioned solutions are, however, all concerned with deviations produced during the additive manufacturing process.

However, an additional concern with manufactured components (e.g. components manufactured using additive manufacturing techniques or other manufacturing techniques such as casting or subtractive manufacturing) is that the components often suffer from poor surface finish. Hence, in many cases, components are required to go through post-processing procedures in order to meet initial engineering dimensional and surface finish requirements. For example, and without being limitative, post-processing can be performed through subtraction (i.e. by removing material), or by addition (i.e. by adding material such as coatings). Often, the post-processing procedures, can unfortunately result in the component being altered to fall out of dimensional specification and/or surface specification requirements. In fact, in many cases, the order of magnitude of geometrical deviations produced during manufacturing is significantly smaller than the geometrical deviation from the manufactured part to the final post-processed part. When post-processing is performed, it is therefore required for the design dimensions of the component to take into account the required post-processing procedures, in order to evaluate the material removal/addition which will occur after manufacturing (i.e. during the post-processing procedures) and compensate for deviations performed during these specific post-processing procedures, such that the final component meets both the surface characteristics requirements and the geometrical precision requirements.

One skilled in the art would understand that there are major differences between the process of predicting the geometrical deviation of a manufactured component manufactured using additive manufacturing and the process of predicting the geometrical deviation of a manufactured component following a surface finishing post-processing, which are completely different by their nature. Where one predicts the deviation from a building/shaping process from an additive (multi-layer welding) manufacturing method for a desired nominal geometry, the other predicts the deviation from a material removal process and/or a coating process from a surface treatment post-processing.

Referring toFIG.1(prior art), a typical design and manufacturing cycle for 3D manufactured components, using additive manufacturing, is shown. In the embodiment shown, the process involves the initial design phase of defining the engineering requirements, including, for example and without being limitative, the dimensions, surface finish characteristics (i.e. surface roughness conditions), tolerances and materials for the manufacture of the component (step101). These engineering requirements are the basis on which the original model of the component (or CAD model) is subsequently generated (step102).

Once the original model has been generated, the CAD data associated to the original model (i.e. the data of the original model of the component (CAD model)) can be transferred to a slicer software (step103) generating machine instructions for an associated additive manufacturing apparatus (or 3D printer). At this stage, the additive manufacturing parameters of the component such as, for example and without being limitative, the layer thickness, the print speed, the orientation of the component in the printer while the component is manufactured, etc., are defined. One skilled in the art will understand that the parameters which are defined at this stage have a high impact on the potential deviations, the quality of the manufactured components and the characteristics thereof, such as surface finish characteristics or the like. Indeed, a large layer thickness, for example and without being limitative, can result in a coarse surface finish. In addition, the printing orientation greatly influence the finishing of the different side surfaces of each component. Furthermore, the selected material, the printing technology, and the geometry of the component are additional factors which impact the quality of the manufactured components and the characteristics thereof. For example, small holes with dimensions approaching the printer resolution are most likely to have inaccurate dimensions.

Once the machine instructions for the associated additive manufacturing apparatus (3D printer) are generated by the slicer program, a 3D printer can perform the additive manufacturing process (3D printing) (step104). However, due to the above-mentioned bias which can occur depending on the selected printing parameters and the multiple variables involved in the process, components manufactured using additive manufacturing technologies commonly require subsequent post-processing procedures, in order to make the component usable for the intended purpose. Hence, in order to meet the engineering requirements, the manufactured component often requires an additional aging or heat treatment procedure (step105) and/or surface post-processing and surface treatment procedures (step106).

The step of performing the surface post-processing and finishing procedures (step106) on the component commonly entails removing material from specific surfaces (for example using cutting, drilling, machining, abrasive flow machining, electrochemical polishing, automatic lapping, tumble finishing or the like) until the surface finish is within finishing requirement specifications. In many cases, this creates a discrepancy between the dimensions of the finished component and those of the original design (i.e. the dimensions of the original CAD model as specified in the engineering requirements). Indeed, while removing material to achieve the required surface roughness and quality, the geometrical dimensions of the component can deviate from the specified tolerance values, thereby resulting in non-compliance of the component with the dimensional engineering requirements and can lead to discarding of the component. One skilled in the art will understand that the rate at which the material is removed from the component during post-processing, depends on several factors such as the procedure used, the component material, the geometry and features of the component being processed, etc. For example, and without being limitative, a small diameter hole will lose material at a different rate from a curved outer surface of a component. One skilled in the art will also understand that, in alternative embodiments, the post-processing and finishing procedures on component could also entail adding material (for example a coating being applied) on specific surfaces or features of the component.

Hence, one skilled in the art will easily understand that, predicting how much material will be lost through different post-processing and/or finishing processes, for different component materials and geometries would provide several advantages and can save lots of time on design iterations and materials. It will also increase the repeatability and precision of components manufactured, for example, using additive manufacturing. It will also be understood that the same could be said for predicting how much material would be added through a post-processing procedure and/or finishing processes, when treating different component materials and geometries.

In view of the above, there is a need for an improved system and method for assisting the design of manufactured components requiring post-processing, which would be able to overcome or at least minimize some of the above-discussed prior art concerns.

SUMMARY OF THE INVENTION

In accordance with a first general aspect, there is provided a system for assisting the design of manufactured components requiring post-processing, through prediction of geometrical deviations created by at least one post-processing procedure to be performed on a component following a manufacture thereof. The system comprises an input module receiving original design data relative to a component to be manufactured and requiring post processing. The original design data define engineering requirements of the component including a material of the component, a geometry of the component defining features and dimensional accuracy thereof and a surface finish. The input module further receives manufacturing and post-processing data including the at least one post-processing procedure to be performed on the manufactured component and specific parameters of the at least one post-processing procedure. The system also comprises a compensation determination module receiving the original design data and the manufacturing and post-processing data from the input module. The compensation determination module is configured to predict the geometrical deviations created by the at least one post-processing procedure and generate dimension compensation data defining compensations for each one of the features of the component. The dimension compensation data is generated using at least one machine learning model generated by a machine learning algorithm that has been trained using a training dataset. The system further comprises a compensated model generation module configured to receive the compensation data from the compensation determination module and to generate a compensated Computer Assisted Design (CAD) model including the compensations for each one of the features of the component, to counterbalance the deviation to be caused by the at least one post-processing procedure.

In an embodiment, the original design data comprises an original CAD model of the component responding to the engineering requirements.

In an embodiment, the manufacturing and post-processing data comprises data relative to the identification of the manufacturing process, the identification of the specific apparatus used for the manufacturing process and the material used in the manufacturing process.

In an embodiment, the compensation determination module is configured to generate the dimension compensation data defining different compensations for each one of the features of the component. The compensation corresponding to each one of the features of the component is adapted to the positioning and configuration of the corresponding feature.

In an embodiment, the compensation determination module is further configured to generate the compensation data using calculated deviations from theoretical modeling of the deviations for each one of the features in combination with the at least one machine learning model.

In an embodiment, the theoretical modeling includes a computational fluid dynamics (CFD) analysis relative to the features defining the geometry of the component, the material of the component, an abrasive media used as fluid for the at least one post processing procedures and the specific parameters of the post-processing procedure.

In an embodiment, the system further comprises a manufacturing and post-processing unit comprising a manufacturing apparatus configured to receive the compensated CAD model and to manufacture the component according to parameters of the compensated CAD model and a post-processing apparatus configured to perform the at least one post-processing procedure on the component manufactured according to the compensated CAD model.

In an embodiment, the manufacturing apparatus is a 3D printer manufacturing the component using additive manufacturing.

In an embodiment, the system further comprises a machine learning module including an artificial intelligence unit implementing the machine learning algorithm trained using the training dataset, a manufactured component measurement module configured to acquire dimensional data relative to the features of a manufactured component after the component has been manufactured and a post-processed component measurement module configured to acquire dimensional data relative to the features of the component after the component has gone through the at least one post-processing procedure. The data acquired by the manufactured component measurement module and the post-processed component measurement module is correlated with the engineering requirements of the component and information from the manufacturing and post-processing data to populate the training dataset.

In accordance with another general aspect, there is further provided a system for assisting the design of manufactured components requiring post-processing. The system comprises an input module receiving original design data relative to a component to be manufactured and requiring post processing. The original design data include an original CAD model of the component respecting engineering requirements including a material of the component, a geometry of the component defining features and dimensional accuracy thereof and a surface finish. The input module further receives manufacturing and post-processing data including the at least one post-processing procedure to be performed on the manufactured component and specific parameters of the at least one post-processing procedure. The system also comprises a compensation determination module receiving the original design data and the manufacturing and post-processing data from the input module. The compensation determination module is configured to predict the geometrical deviations created by the at least one post-processing procedure and to generate dimension compensation data defining compensations to be applied on the original CAD model for each one of the features of the component. The dimension compensation data is generated using a combination of empirical analysis performed by at least one machine learning model generated by a machine learning algorithm trained using a training dataset and theoretical modeling of the deviations for each one of the features using at least one theoretical model. The system also includes a compensated model generation module configured to receive the compensation data from the compensation determination module and to generate a compensated CAD model based on the original CAD model further including the compensations for each one of the features of the component defined therein, to counterbalance the deviation to be caused by the at least one post-processing procedure.

In an embodiment, the manufacturing and post-processing data comprises data relative to the identification of the manufacturing process, the identification of the specific apparatus used for the manufacturing process and the material used in the manufacturing process.

In an embodiment, the compensation determination module is configured to generate dimension compensation data defining different compensations for each one of the features of the component of the original CAD model. The compensation corresponding to each one of the features of the component being adapted to the positioning and configuration of the corresponding feature.

In an embodiment, the theoretical modeling includes a computational fluid dynamics (CFD) analysis relative to the features defining the geometry of the component, the material of the component, an abrasive media used as fluid for the at least one post processing procedures and the specific parameters of the post-processing procedure.

In an embodiment, the system further comprises a manufacturing and post-processing unit comprising a manufacturing apparatus configured to receive the compensated CAD model and to manufacture the component according to parameters of the compensated CAD model and a post-processing apparatus configured to perform the at least one post-processing procedure on the component manufactured according to the compensated CAD model.

In an embodiment, the manufacturing apparatus is a 3D printer manufacturing the component using additive manufacturing.

In an embodiment, the system further comprises a machine learning module including an artificial intelligence unit implementing the machine learning algorithm trained using the training dataset, a manufactured component measurement module configured to acquire dimensional data relative to the features of a manufactured component after the component has been manufactured and a post-processed component measurement module configured to acquire dimensional data relative to the features of the component after the component has gone through the at least one post-processing procedure. The data acquired by the manufactured component measurement module and the post-processed component measurement module is correlated with the engineering requirements of the component and information from the manufacturing and post-processing data to populate the training dataset.

In accordance with another general aspect, there is further provided a computer implemented method for assisting the design of manufactured components requiring post-processing, through prediction of geometrical deviations created by at least one post-processing procedure to be performed on a component following a manufacture thereof. The method comprises the steps of: acquiring original design parameters relative to the component to be manufactured according to engineering requirements, the engineering requirements including the material of the component, the geometry of the component defining features and dimensional accuracy thereof and the surface finish; acquiring manufacturing and post-processing input parameters regarding the component including the at least one post-processing procedure to be performed on the manufactured component and specific parameters of the at least one post-processing procedure to generate a finished component; providing a training dataset, wherein the training dataset comprises measurement data relative to geometric deviations caused by different post-processing procedures and for different component features, component geometries and component materials, which are similar or different from the engineering requirements of the component; predicting compensations for each one of the features of the component corresponding to deviations to occur subsequently during the at least one post-processing procedure such that the finished component respects the engineering requirements regarding the dimensional accuracy and the surface finish and generating dimension compensation data for the features of the component, wherein the prediction of the compensations are determined using a machine learning model generated by a machine learning algorithm trained using the training dataset; and generating a compensated CAD model including compensations for the predicted deviations to occur to the corresponding features of the component during the at least one post-processing procedure based on the dimension compensation data, the compensated CAD model including specific compensations for each one of the features of the component.

In an embodiment, the further comprises the step of manufacturing the component according to the compensated CAD model.

In an embodiment, the further comprises the step of performing the at least one post-processing procedure on the manufactured component, to generate the finished component.

In an embodiment, the further comprises the steps of: measuring the component after the component has been manufactured in accordance with the compensated CAD model; measuring the component after the at least one post processing procedure has been performed on the manufactured component; and updating the training dataset using the data relative to measurement differences between the measurements after the component has been manufactured and after the at least one post processing procedure has been performed on the manufactured component correlated with the engineering requirements of the component and the manufacturing and post-processing input parameters relative thereto.

In an embodiment, the step of predicting compensations for each one of the features of the component comprises the substeps of analyzing an original CAD model and identifying features of the component defining the geometry of the component and calculating the deviation to be caused by the post-processing procedures for each one of the identified features and including compensation for each one of the identified features in the generated compensation data.

In an embodiment, the step of calculating the deviation to be caused by the post-processing procedures for each one of the identified features can further include generating deviation data using theoretical modeling of the deviations for each one of the features from at least one theoretical model.

DETAILED DESCRIPTION

In the following description, the same numerical references refer to similar elements. The embodiments, geometrical configurations, materials mentioned and/or dimensions shown in the figures or described in the present description are embodiments only, given solely for exemplification purposes.

Moreover, although the embodiments of the system for assisting the design of manufactured components requiring post-processing and corresponding parts thereof consist of certain components as explained and illustrated herein, not all these components are essential and thus should not be taken in their restrictive sense. It is to be understood, as also apparent to a person skilled in the art, that other suitable components and cooperation thereinbetween may be used for the system for assisting the design of manufactured components requiring post-processing, as will be briefly explained herein and as can be easily inferred herefrom by a person skilled in the art. Moreover, although the associated method includes steps as explained and illustrated herein, not all these steps are essential and thus should not be taken in their restrictive sense. It will be appreciated that the steps of the method for assisting the design of manufactured components requiring post-processing described herein may be performed in the described order, or in any suitable order.

To provide a more concise description, some of the quantitative and qualitative expressions given herein may be qualified with the terms “about” and “substantially”. It is understood that whether the terms “about” and “substantially” are used explicitly or not, every quantity or qualification given herein is meant to refer to an actual given value or qualification, and it is also meant to refer to the approximation to such given value or qualification that would reasonably be inferred based on the ordinary skill in the art, including approximations due to the experimental and/or measurement conditions for such given value.

In the present description, the term “computing device” encompasses computers, servers and/or specialized electronic devices which receive, process and/or transmit data. “Computing devices” are generally part of “systems” and include processing means, such as microcontrollers and/or microprocessors, CPUs or are implemented on FPGAs, as examples only. The processing means are used in combination with storage medium, also referred to as “memory” or “storage means”. Storage medium can store instructions, algorithms, rules and/or trading data to be processed. Storage medium encompasses volatile or non-volatile/persistent memory, such as registers, cache, RAM, flash memory, ROM, as examples only. The type of memory is of course chosen according to the desired use, whether it should retain instructions, or temporarily store, retain or update data.

As can be appreciated, one skilled in the art will understand that the modules described below can be implemented via programmable computer components, such as one or more physical or virtual computing device comprising a processor and memory. It is appreciated, however, that other configurations are also possible.

Moreover, the modules and data sources described herein can be in data communication through direct communication such as a wired connection or via a network allowing data communication between computing devices or components of a network capable of receiving or sending data, which includes publicly accessible networks of linked networks, possibly operated by various distinct parties, such as the Internet, private networks (PN), personal area networks (PAN), local area networks (LAN), wide area networks (WAN), cable networks, satellite networks, cellular telephone networks, etc. or combination thereof.

Referring generally toFIGS.2and3, an embodiment of the system10for assisting the design of manufactured components requiring will be described in more details below. The system10is designed and configured to accurately predict the geometrical deviations to occur during the post-processing procedures following the manufacturing process and to adjust the original CAD model26and to compensate such deviations. The system10can therefore generate an adjusted CAD model32, which can subsequently be used to manufacture components which will comply with dimensional engineering requirements, following the post-processing procedures.

In the course of the present application, the expression «post-processing procedures» is used to refer to all conventional or special surface finishing processes which impact on the external or internal surfaces of the component (i.e. add or subtract material from the surface) and result in the surfaces of the component meeting the surface finish engineering requirements. One skilled in the art will understand that the post-processing procedures can be a chemical procedure and/or a mechanical procedure or can combine both a chemical procedure and a mechanical procedure. For example, and without being limitative post-processing procedures can include cutting, drilling, machining, abrasive flow machining, electrochemical polishing, automatic lapping, tumble finishing, etc.

In view of the above, one skilled in the art will understand that, in most cases, the geometrical deviations created by the post-processing procedures correspond to material loss caused by the required removal of material to meet the surface finish requirements and therefore require compensation by addition of material to the features of the component defined in the original CAD model26. One skilled in the art will however understand that, in alternative embodiments, the deviation could also be the result of addition of material, which could, for instance, occur as a result of a coating being applied to selected features or to a section of the component. In such embodiments. Compensation are performed by subtraction of material from the features of the component defined in the original CAD model26.

As will be described in more details below, in an embodiment, the system10for assisting the design of manufactured components requiring post-processing uses the engineering requirements for the component to be manufactured as well as other parameters regarding the manufacture of the component as inputs in order to predict the deviations which will occur during the post-processing process. In an embodiment the system10can also use the engineering requirements for the component to be manufactured as well as the other parameters regarding the manufacture of the component, to predict the deviations which will occur during the manufacturing thereof. For example and without being limitative, in an embodiment, the parameters used can include data regarding the initial design dimensions of the component, the manufacturing process, the material of the component, the geometry of the component and the post-processing process being applied to the component following the manufacture thereof. Considering the input parameters, the system10can generate dimension compensations for the different features of the component (i.e. generate specific compensations for different segments (or portions) of the component). These compensation values can therefore be added or subtracted from the original CAD model26of the component for each feature, therefore generating an adjusted CAD model32.

In view of the above, it will be understood that, using the present system10, once the adjusted CAD model32has been generated, the component can be manufactured according to the adjusted CAD model32and be post-processed to meet the surface finish requirements. The manufactured and post-processed components based on the adjusted CAD model32can therefore meet both the dimensional and surface finish requirements specified in the engineering requirements of the original design data22for the component (i.e. be post-processed without falling out of spec due to the deviations generated during the post-processing procedures).

As will be described in more details below, in an embodiment, the system10can use machine learning in order to predict accurate compensation values for the component, based on the original design data22. In such an embodiment, the system10therefore encompasses artificial intelligence algorithms generating deviation prediction models stored in a memory of a computing device, using a training dataset58a. In an embodiment, the training dataset58acan be updated, using data obtained from manufactured and post-processes components over time. Hence, in such an embodiment, the Artificial Intelligence capability of the system10will cooperate with the other modules of the system10, to allow the system10to become more precise and versatile over time.

In an embodiment, the training dataset58acomprises measurement data relative to geometric deviations caused by different post-processing procedures and for different component features, component geometries and component materials. In an embodiment, the training dataset58acan be stored in a deviation data database58, with the data of the deviation data database58being repeatedly updated by the system10, based on data acquired or generated therefrom.

Therefore, in an embodiment the training dataset58aincludes data gathered following instances of post-processing procedures of components and identifying the variation in the measurement of the different features of the components caused by the specific post-processing procedure, for the specific component material and component features of each instance and stored in the deviation data database58. In other words, the data included in the training dataset58acan be generated by experimentation using an array of post-processing and finishing procedures, finishing materials, component materials, and component geometries and features generated through operation of the system10, as will be described in more details below. Each component used in the experimentations can be designed using specific engineering requirements and defined by an original CAD model26. The components can subsequently be manufactured, post-processed and precisely measured, to assess the geometrical deviations (e.g. the material loss) for each one of the different component features, in the associated post-processing procedure, with the data generated being stored in the deviation data database58.

Referring specifically toFIG.2, an embodiment of the system10for assisting the design of manufactured components requiring post-processing is shown. In the embodiment shown inFIG.2, the system10includes one or more computing device13having a memory for storing instructions and a processor for executing the instructions.

In the embodiment shown, the system10includes an input module15receiving original design data22relative to a component to be manufactured (and requiring post processing) and allowing data acquisition and transfer thereof. In an embodiment, the original design data are relative to initial engineering requirements for a component and include at least the component dimensions, the geometry of the component defining features and dimensional accuracy (or dimensional tolerances) thereof, the material and the surface roughness (or surface finish). In an embodiment, the original design data22include the original CAD model26of the component, defining the component designed using a CAD software and meeting the above-mentioned engineering requirements. One skilled in the art will understand that the original design data22can be received from one or more internal or external and public or private data sources, such as databases, repositories, data stores, etc. In an embodiment, the input module15can receive portion of original design data22from different data sources, and merge and/or format the acquired data to form the original design data22.

In an embodiment, the input module15also receives manufacturing and post-processing data24and allows data acquisition and transfer thereof. For example and without being limitative, in an embodiment the manufacturing and post-processing input data24can include information relative to the identification of the manufacturing process, the identification of the specific apparatus used for the manufacturing process (e.g. the make and model of the 3D printer), the material used in the manufacturing process, the post-processing procedures which will be performed on the manufactured component, and the specific parameters of the post-processing procedures (e.g. the description of the post-processing process, the media being used, the time interval of the prost-processing process, the required finish, etc.). Once again, one skilled in the art will understand that the manufacturing and post-processing data24can be received from one or more internal or external and public or private data sources, such as databases, repositories, data stores, etc. In an embodiment, the input module can receive portion of the manufacturing and post-processing data24from different data sources, and merge and/or format the acquired data to form the manufacturing and post-processing data24.

The system10further comprises a compensation determination module20receiving data from the input module15and generating compensation data31corresponding to the compensations to be applied to each one of the features of the component having the characteristics defined in the original design data22and corresponding to the original 3D CAD model26.

In an embodiment, the compensation determination module20is configured to predict post-processing outcomes (i.e. predict deviations which will be generated by the post-processing procedures for each feature defining the geometry of the component). In an embodiment, the compensation determination module20is configured to initially analyze the original 3D CAD model26and identify the features of the component which define the geometry of the original design. Based on the identified features, the compensation determination module20is further configured to calculate (or estimate) the deviation to be caused by the post-processing procedures for each one of the identified features.

In an embodiment, the compensation determination module20is configured to predict the deviation to be caused by the post-processing procedures using a combination of statistical analysis of variations of corresponding features (defining different geometries) in previous evaluations, taking into account the material and post-processing procedures used in the previous evaluations and the current material and post-processing procedure to be performed (i.e. empirical analysis) and calculated deviations using theoretical modeling of the deviations for each one of the features based on at least one theoretical model. One skilled in the art will understand that, in alternative embodiments, the prediction of the deviation to be caused by the post-processing procedures could be performed using only an empirical analysis.

In an embodiment where an abrasive media is used for the post-processing procedures, the theoretical modeling can be performed using numerical analysis for the impact of the abrasive media used for the post-processing procedures on the features defining the geometry of the component. For example and without being limitative, in an embodiment, a computational fluid dynamics (CFD) analysis relative to the features defining the geometry of the component, the material of the component, the abrasive media used as fluid for the post-processing procedures, and the specific parameters of the post-processing procedure (time, required finish, etc.) can be performed to model the removal of material which will occur during the post-processing procedure

Referring toFIG.3, in an embodiment, the compensation determination module20can include a machine learning module50for generating the machine learning model52aperforming the empirical analysis. The machine learning module50is thereby a submodule of the compensation determination module20.

In an embodiment, the machine learning module50includes an artificial intelligence unit (Al unit)52implementing an artificial intelligence algorithm (Al algorithm) configured to learn and generate the machine learning models52a, using the generated training datasets58a, thereby continuously improving the quality of the output (or predictions) provided by the compensation determination module20. Indeed, as previously mentioned, in an embodiment, the training dataset58ais repeatedly updated using data obtained regarding post-processing of components having different geometries and material and being post-processed according to different procedures and post-processing parameters, such that the Al algorithm implemented by the Al unit52, can repeatedly use the latest version of the training dataset, in order to generate an updated and improved machine learning model52a.

One skilled in the art will understand that several different machine learning algorithms could be used to implement the Al algorithm of the artificial intelligence unit52. For example, and without being limitative, this could include, artificial neural network algorithms, Gaussian process regression algorithms, fuzzy logic-based algorithms, decision tree algorithms, etc. Moreover, in an embodiment, more than one Al algorithm could be used such that hybrid machine learning algorithms including features and properties drawn from more than one type of machine learning algorithms could be used to implement the disclosed Al unit52and machine learning module50of the system10.

In an embodiment, the machine learning module50includes a manufactured component measurement module54configured to acquire dimensional data relative to the features of a manufactured component (i.e. the dimensions of the features of the component after the component has been manufactured, for example by 3D printing using a 3D printer). In an embodiment, the dimensional data relative to the features of a manufactured component acquired by the manufactured component measurement module54allows the comparison of the dimensions of the manufactured component with those of, for example, the compensated CAD model32used for the manufacture thereof. In an embodiment, the dimensional data acquired by the manufactured component measurement module54include high-precision measurement data concerning the geometrical dimension and surface properties of the manufactured component and its features. For example and without being limitative, the high-precision measurement data can be acquired from measurement components, using surface profilometry, non-contact profilometry, coordinate measuring devices, 3D scanning, hand metrology or the like.

In an embodiment, the dimensional data relative to the features of a manufactured component can be transmitted to the deviation data database58and stored thereon.

In the embodiment shown, the machine learning module50further includes a post-processed component measurement module56configured to acquire dimensional data57relative to the features of the component after the component has gone through the post-processing procedures. In an embodiment, the dimensional data relative to the features of the component after the component has gone through the post-processing procedures acquired by the post-processed component measurement module56allows the comparison of the dimensions of the post-processed component with those of the previously measured manufactured component (i.e. the dimensional data acquired by the manufactured component measurement module54). Once again, in an embodiment, the dimensional data relative to the features of the component after the component has gone through the post-processing procedures acquired by the post-processed component measurement module56includes high-precision measurement data concerning the geometrical dimension and surface properties of the post-processed component and its features. Once again, for example and without being limitative, the high-precision measurement data can be obtained from measurement instruments using surface profilometry, non-contact profilometry, coordinate measuring devices, 3D scanning, hand metrology or the like.

In an embodiment, the dimensional data relative to the features of the component after the component has gone through the post-processing procedures can be transmitted to the deviation data database58and stored thereon. In an alternative embodiment the dimensional data relative to the features of a manufactured component and/or the dimensional data relative to the features of the component after the component has gone through the post-processing procedures can be processed before being stored in the deviation data database58. For example and without being limitative, in an embodiment, only the deviation (or dimensional measurement differences) between the dimensional data relative to the features of a manufactured component and/or the dimensional data relative to the features of the component after the component has gone through the post-processing procedures can be stored in the deviation data database58. One skilled in the art will understand that, in an embodiment, the dimensional data relative to the features of a manufactured component and/or the dimensional data relative to the features of the component after the component has gone through the post-processing procedures (or the derived processed data) can be stored on the deviation data database58and correlated with the engineering requirements of the corresponding component, including, for example the specific component material and component features specifications and the information of the manufacturing and post-processing data.

In view of the above, one skilled in the art will understand that the data acquired by the manufactured component measurement module54and the post-processed component measurement module56and correlated with the engineering requirements of the corresponding component and information of the manufacturing and post-processing data thereof can be used to update (or populate) the training dataset58a(for example stored in the deviation data database58) used to train the Al algorithm of the Al unit52, using data from the manufactured component measurement module54and the post-processed component measurement module56for several instances of manufactured components.

Hence, it will be understood that the larger the training dataset58aof different post-processing parameters and outcomes becomes, the more accurate the machine learning model52agenerated by the Al algorithm of the Al unit52, which can be used by the compensation determination module20of the system10, can become in predicting the required dimension compensation.

Referring back toFIG.2, in the embodiment shown, the system10further includes a compensated model generation module30, receiving the compensation data31from the compensation determination module20and generating a compensated design (i.e. a compensated 3D CAD model32) based on the original CAD model26and including compensations for the predicted deviations to occur during the post-processing procedures, as determined by the compensation determination module20. In an embodiment, the compensation data31can also include deviation to occur during the manufacturing of the component and the compensated 3D CAD model32can therefore be based on the original CAD model26and including compensations for the predicted deviations to occur for the features of the component, during the combination of the manufacturing and the post-processing procedures.

Once the compensated 3D CAD model32is generated by the compensated model generation module30, the compensated model data relative to the compensated 3D CAD model32can be transferred to a manufacturing and post-processing unit40including the specific apparatuses used for the manufacturing process and the specific post-processing procedures as defined in the manufacturing and post-processing data24received by the input module15and used by the compensation determination module20.

The manufacturing and post-processing unit40can include any apparatus (or combination of apparatuses) for manufacturing a component and performing the post-processing procedures in conformity with the information of the manufacturing and post-processing data24. For example, in an embodiment where the manufacturing unit is a 3D printer, the manufacturing and post-processing unit40can operate to transfer the data relative to the compensated CAD model32generated by the compensated model generation module30to a slicer software, which generates machine instructions for the associated 3D printer that subsequently performs the additive manufacturing process, the aging and/or the heat treatment procedure. In an embodiment, the manufacturing and post-processing unit40can further include a machining apparatus performing the post-processing and finishing procedures. One skilled in the art will however understand that, in alternative embodiments, the manufacturing and post-processing unit40could rather include apparatuses performing one of cutting, drilling, machining, abrasive flow machining, electrochemical polishing, automatic lapping, tumble finishing, or the like required for the post-processing and finishing procedures.

One skilled in the art will also understand that, in an embodiment, each one of the compensation determination module20, the compensated model generation module30, and the machine learning module50can be implemented via software running on one or more computing device having a memory capable of storing instructions and a processor for processing the instructions and capable of implementing these modules.

One skilled in the art will understand that each one of the compensation determination module20, the compensated model generation module30, and the machine learning module50can be partially or entirely embodied on the same computing device or on remote computing device(s) communicating with one another over a network, such as, for example and without being limitative, a local area network (LAN), a wide area network (WAN) such as the Internet, or the like. Moreover, the training dataset used by the above described compensation determination module20and the machine learning module50can be hosted directly on the same computing device or on a remote computing device(s) communicating therewith over a network.

Now turning toFIGS.4and5, the Figures show an exemplary embodiment of a component60for which the above described system10for assisting the design of manufactured components requiring post-processing can be used. InFIG.4, the component60includes a U-channel62with an internal vane64for fluid flow regulation, which is a typical example of a component produced using additive manufacturing. In such a component60, characteristics such as inlet61and outlet63radii, vane dimensions, surface finish, and all geometry profiles are critical to the optimal flow of fluid therein and should therefore be within the tolerance thresholds as specified in engineering requirements thereof. However, producing the component60according to the required surface finish, requires post-processing, often performed through material removal. As mentioned above, such material removal can cause the component60to be outside of the predetermined geometrical tolerances for this specific component60, following post-processing, therefore negatively impacting the fluid flow therein or the required strength or resistance of the component.

Hence, to ensure that the component60remains within the geometrical tolerances, the system10for assisting the design of manufactured components requiring post-processing can predict the deviation to be created by the post-processing procedures and compensate the dimensions of specific features of the component60with extra material, as seen inFIG.5. The compensation of the dimensions of the specific features are provided by generating the compensated CAD model32, using the compensated model generation module30and based on the compensation data generated by the compensation determination module20. The hatching inFIG.5shows the material added to the different features of the component60to compensate for the loss which will occur during post-processing procedures. As will be easily understood and as can be seen inFIG.5, different features of the component are compensated differently (i.e. are compensated through addition of a different thickness of material) depending on the multiple parameters of the post-processing procedures (e.g. the type of post-processing, the length of the post-processing procedures, etc.). As described above, in an embodiment, the different features can also be compensated differently depending on the multiple parameters of the manufacturing process (i.e. in this embodiment depending on the 3D printing process, the 3D printer used, the 3D printing parameters, the material, etc.).

For exemplification purposes,FIG.5shows basic compensation for the inner diameter of the channel62as well as the outer surface of the vane64. Factors such as the thickness of each one of the compensation layers66, the length of each compensation layer (i.e. the position67of the extremities of the compensation layers), the thickness variations of different portions of the each compensation layers, etc. are determined by the compensation determination module20and defined in the compensation data.

One skilled in the art will understand that the exemplary compensations shown inFIG.5are not exhaustive but are rather shown solely for exemplification purposes. It will be understood that, for example and without being limitative, numerous additional variations of thickness in the compensation layers can be provided. In fact, the combinations of different compensation portions are practically unlimited.

The system10for assisting the design of manufactured components requiring post-processing having been described above, the associated computer implemented method100for assisting the design of manufactured components requiring post-processing will now be described in more details below.

Referring toFIG.6, in an embodiment, the method100includes the initial step110of acquiring original design parameters relative to the component to be manufactured (and requiring post processing) in accordance with engineering requirements including at least the component dimensions, the geometry of the component defining features and dimensional accuracy (or dimensional tolerances) thereof, the material and the surface roughness (or surface finish). In an embodiment, at this step, the original CAD model of the component, defining the component designed using a CAD software and meeting the above-mentioned engineering requirements can be acquired.

In an embodiment, the method100further comprises the step120of acquiring manufacturing and post-processing input parameters regarding the component. For example, and without being limitative, in an embodiment, the manufacturing and post-processing input parameters can include, input data regarding at least a subset of the manufacturing process, the apparatus used for the manufacturing process, the material of the component, the post-processing procedures and the specific parameters thereof.

In an embodiment, the method100further includes the step130of predicting compensations for each one of the features of the component corresponding to deviations to occur subsequently during the at least one post-processing procedure, such that the finished component respects the engineering requirements regarding the dimensional accuracy and the surface finish, and generating dimension compensation data for the features of the component. The dimension compensation data defines compensation parameters identifying modifications to be performed on the component to counterbalance the deviation to be caused by the post-processing procedures.

In an embodiment, the method further includes the step140of generating a compensated CAD model based on the dimension compensation data and compensating for the predicted deviations to occur during the manufacturing and the post-processing procedures. In an embodiment, the generated compensated CAD model includes compensations for the predicted deviations to occur on each corresponding features of the component.

In an embodiment, the method includes the further step150of transmitting the data representative of the compensated CAD model to a manufacturing and post-processing unit and manufacturing the component based on the compensated CAD model, and performing post-processing of the component in order to generate a finished component. In view of the above, it will be understood that after the compensated CAD model has been generated, the component has been manufactured according to the compensated CAD model, and the component has been post-processed, the manufactured and post-processed component meets the original engineering requirements regarding dimensions and surface finish of the features thereof.

In the embodiment shown, the step of predicting compensations for each one of the features of the component130comprises the substep131of analyzing the original CAD model and identifying features of the component defining the geometry of the component defined by the original design parameters and the further substep132of calculating (or estimating) the deviation to be caused by the post-processing procedures for each one of the identified features and including compensation for each one of the identified features in the generated compensation data.

In an embodiment, the step132of calculating (or estimating) the deviation to be caused by the post-processing procedures for each one of the identified features includes providing a training dataset132aand predicting the deviation to be caused by the post-processing procedures for each feature of the component using a machine learning algorithm trained using the training dataset132band generating a machine learning model.

In an embodiment, the step132of calculating the deviation to be caused by the post-processing procedures for each one of the identified features can further include generating deviation data using theoretical modeling of the deviations for each one of the features from at least one theoretical model. As mentioned above, in an embodiment, the theoretical modeling can be performed using numerical analysis for the impact of the abrasive media used for the post-processing procedures on the features defining the geometry of the component. For example and without being limitative, in an embodiment, a computational fluid dynamics (CFD) analysis relative to the features defining the geometry of the component, the material of the component, the abrasive media used as fluid for the post-processing procedures, and the specific parameters of the post-processing procedure (time, required finish, etc.) can be performed to model the removal of material which will occur during the post-processing procedure

Now referring toFIG.7a subprocess200for providing the training dataset and training the machine learning algorithm can be provided.

In an embodiment, the training dataset comprises measurement data relative to geometric deviations caused by different post-processing procedures and for different component features, component geometries and component materials. In an embodiment, the measurement data can be generated by experimentation using an array of post-processing procedures data, finishing material data, component materials data, and components features/geometries data, for a plurality of experimentation instances that are similar or different from the engineering parameters of the current component for which the dimension compensations are being generated. In the embodiment shown inFIG.7, the measurement data can be generated by the step233of manufacturing components, for example from the compensated CAD model, the step234of measuring the dimensional characteristics of the components following the manufacture thereof, the step235of performing post processing of the components according to predetermined post processing parameters, and the step236of measuring the dimensional characteristics of the components after the components have gone through the post-processing procedures. The measurement differences between the measurements from each instance of manufactured component and the corresponding post-processed component can then be used to generate the training dataset in combination with the corresponding post-processing procedures data, finishing materials data, component materials data, and components features/geometries data for each instance. The subprocess200can further include the step237of training the machine learning algorithm using the generated training dataset.

As can be seen, the subprocess200is iterative and the steps233,234,235and236can be repeated for numerous components in order to repeatedly update the training dataset, and the step237of training the machine learning algorithm using the generated training dataset can also be performed repeatedly to train the machine learning algorithm using the latest updated training dataset and generate an updated machine learning model.