Author :- Arunish Garg
Introduction
In additive manufacturing research, machine learning which is a type of artificial intelligence is becoming extremely popular. The production of complicated geometries is made easier using additive manufacturing. Its range has fastly grown first from manufacture of pre-production visualization models to the production of finished components, necessitating the required for improved optimally produced component quality assurance. One of the potential approaches for accomplishing this objective is machine learning. The application and overview of supervised and unsupervised machine learning characteristics as well as problem of over fitting and under fitting issues of addictive manufacturing components is currently being written in this sector.
How Machine Learning helps in addictive manufacturing
Machine Learning is characterized as PC programming to improve an exhibition rule utilizing model information or past experience. Machine Learning in AM is being investigated in new and inventive ways to cope with incorporating ML and AI techniques into AM, aside from the usual usage of producing forecasts through data fitting.
AM experts are using Machine Learning calculations, applications, and stages to enhance item quality, advance assembling measures, and lower costs. The ML models use information from the preparation set and generate inference based on it when they have a reliable preparing dataset.
On the one hand, well-prepared machine learning models can generate accurate forecasts and find the best management bounds. However, it can likewise constantly handle in situ information for imperfection identification. By and large, machine learning applications might be considered as a type of information control. Because of this capacities, machine learning is a basic segment of Industry 4.0.
Machine learning is a branch of artificial intelligence (AI) dedicated to the advancement of human knowledge through technology. Machine learning helps systems to deal with new scenarios by analyzing, self-teaching, observing, and learning from previous experiences. Machine learning allows for the continual improvement of technology by exposing it to new conditions, evaluating it, and adapting it, all using trend and pattern recognition to make better judgments in the future.
Overview of Machine Learning techniques used in Addictive Manufacturing
In several instances, data collecting and labelling come at a significant expense in terms of computing, manpower and experimentation. The creation and analysis of technologies which can enable learning patterns from data is referred to as machine learning. Forecasting, enforce boosting, fault observation, regression, classification and prediction may all be done with ML models. The data which are used to train the machine learning models are the most important aspect in adjudge its success. The quality of machine learning model is only as effective as the data used to train them. To produce precise forecasts, a machine learning model needs enough data. The amount of training data need grows linearly as the number of inputs attributes. There are two types of machine learning techniques which are supervised learning and unsupervised learning
1. SUPERVISED LEARNING :-
All input data is labelled with an output in supervised learning. A labelled collection of training data gives instances of input values as well as the matching accurate output in supervised learning. By using labelled dataset, the machine learning methods develops the system, implying the practical link connecting input and output regions. Both regression and classification may be done with supervised learning.
1) Support Vector Machine :-
Support Vector Machine is a supervised classification approach that uses hyperplanes to segregate data. In Supervised machine learning technique Support Vector Machine may be applied for both classification and regression. This approach is used in classification issues to discover a decision boundary that can appropriately segregate unseen data by using training data into two or more groups .
Fig 1 :- Figure of linear classifiers
A hyperplane of (n-1) dimensions is constructed for linear classification of n-dimensional data into two groups. The linear categorization of two-dimensional data is shown in Figure 1.
The equation for hyperplane is wTx+b = 0 where, where w is vectors(W0,W1,W2,W3……Wm) , b is biased term (W0) and x is variables. The hyperplane must be at the greatest distance from each of the classes' nearest data points in order to decrease the possibility of data separation inaccuracy.
The Applications of SVM in Additive Manufacturing are in defect detection , fault diagnosis , process maps . In defect detection both finalization failure flaws, such as printing stopping or filament running out in the middle of a job, and architectural or geometric errors may be detected using the SVM approach. In fault diagnosis 3D printer faults may be diagnose effectively by Support Vector Machine with accuracy of 94.44%. In process maps this SVM approach determines the procedure window with a limited quantity of information and may be used to optimize process framework.
a) Random Forests :-
For classification and regression issues machine learning technique's random forest is used for predictive framework. To come at the outstanding possible response, the random forest method employs an collection of randomized decision trees. In addictive manufacturing the random forest applications are surface roughness prediction and detection of attacks on cyber manufacturing system. In surface roughness prediction this approach is used to train a framework which forecasts the surface roughness of FDM-produced components and in detection of attacks on cyber manufacturing system Efforts to utilize a vision-based technology with the random forests method to identify purposeful attacks on 3d printers.
2.UNSUPERVISED LEARNING :-
There is no labelled training data set accessible in unsupervised learning. Rather, by combining variables in the dataset and designating training sets, the machine learning method seeks to automatically split the training examples into discrete groups. Unsupervised learning is beneficial for spotting unusual circumstances, for example.
a) Clustering analysis :-
Clustering analysis divides all items into clusters depending on how similar they are. A big dataset is generally required for a clustering study. In addictive manufacturing area the size of dataset is typically low, making clustering method difficult to use. As a result, hardly some current implementation of cluster algorithm in addictive manufacturing have been described.
b) Principal component analysis in AM :-
When image is an data type of input , the set of instances in a dataset might be rather enormous. In this situation, Principal component analysis (PCA) is typically used as a data pre-processing method in addictive manufacturing to minimize the amount of characteristics and clarify the data in order to avoid the problem becoming too difficult. Derived from the images employing 33 features of input, the perfection of SVM improves by help of Principal component analysis (PCA) from 89.6 percent to 90.1 percent in operation by using input data of situ images. Principal component analysis is an excellent way to clarify data when working with challenges of images.
Machine Learning Application in Addictive Manufacturing
a) Processing Parameter Optimization and Property Prediction:-
For manufacturers, the grade of a component created with a certain set of processing settings will be unknown until it is created. As a result, a number of measures must be taken to assure dimensional accuracy, like printing several prototypes and evaluating their performance, making the design process costly, dynamically and time-consuming. A direct link among operating conditions and component quality is highly desired in this respect. Simulations and experimentation are helpful ways for establishing such a connection, but when numerous input characteristics are involved, by using the two approaches to getting optimum process parameter is difficult. On the other hand, machine learning models may be used as surrogate models to help in prediction. By using machine learning regression models a process map may be created by discrete data points , different combinations of inputs which are processing parameters, with a set of output which is property of interest. In process optimization and property prediction to achieve efficiently in process map designers are enabled.
b) Defect Detection, Quality Prediction, and Closed-Loop Control:-
The advancement of in situ monitoring devices allows for the collection of actual data for closed loop and fault identification addictive manufacturing. ML models may use pictures , computed tomography , real time data like as spectra in a variety of ways like first is Label this data with deformity or not by exploratory outcomes or human data, and afterward utilize label information to constantly construct supervised learning models for imperfection identification and performance forecasting, which is a common use of machine learning classification model and second is By unsupervised learning models performs cluster analysis to group aberrant data without need of labeling to discover imperfection and third is To tune real-time controllable processing parameters, develop the machine learning regression models using data from certain actual adjustable processing parameters.
c) Geometric Deviation Control:-
Addictive manufacturing components commonly have low geometric precision and surface integrity. These structural flaws obstruct addictive manufacturing's use in a variety of sectors, including aircraft and medical. In this case, machine learning models can detect geometrical flaws, measure the geometrical deviation, and provide recommendations on how to compensate for geometric errors. To perform error compensation, the train machine learning model forecasts deformation, which is subsequently fed back into the CAD model. The geometrical correctness of components produced using the adjusted CAD model will be considerably enhanced as a result of this method.
Problem of over fitting and underfitting
From training data sets machine learning models acquire understanding to create forecast. As a result, when training sets are applied for performance evaluation of ML frameworks, models prefer to produce flawless estimates in this training sets, that appears to be a positive thing but can lead to overfitting. The problem of overfitting occurs when the model adapts as such to match the training sets vey precisely. To put it another way, as the training error decreases, the prediction error for subsequent inspections tends to rise. In supervised learning it is a typical problem, and it should be sidestep as much as possible. The hold-out technique, k-fold cross-validation technique, and regularisation technique are three prominent approaches for detecting and avoiding overfitting in the addictive manufacturing domain.
a) Hold-out technique :-
It is also referred as data splitting, is basically simple way to keep track of overfitting. It divides the entire dataset into two groups: training and testing. For training the models training sets are used, whereas for evaluating model's performance testing sets are used. For training the models testing data sets will not be used in this way, but it will be helpful to assess the model's performance . The training set should typically be approximately 70% of the complete data sets.
b) K-fold cross-validation technique :-
It is an iterative technique for monitoring the overfitting problem and improving data usage. It divides the entire dataset into n sections of about equal size. Single subset is excluded as the testing set in each iteration, while for training the model remaining subsets are used. Repetition of iteration is continues until all subsets is removed. The n-fold cross-validation technique, also known as leave-one-out cross-validation, is a particular example of this approach with number of data points is k = n,n . The CV approach, as opposed to the standard hold-out approach, solves the problem in addictive manufacturing field of a restricted data set size.
c) Regularization technique :-
It is a method of preventing the machine learning models from becoming overly complicated by appending data throughout training. Minimizing loss function is the machine learning model's objective in general
E= accumulative error,
m = no of training data points,
Ek = Error at each training data point,
In training data if noise is present ,machine learning will use equation to understand about the noise and tends to overfit. The regularisation approach appends a word to the loss function to penalise the model's complexity to avoid this scenario.
Conclusion
Introduce about the how machine learning helps in addictive manufacturing with overview of techniques and algorithms which are used in, how supervised and unsupervised learning help to build 3d printing models. Discuss about the problem of over fitting and under fitting while using machine learning and what are the techniques used in for sort out these problems.
References
https://link.springer.com/article/10.1007/s11837-020-04155-y#:~:text=Machine%20learning%20(ML)%2C%20a,additive%20manufacturing%20(AM)%20research.&text=ML%20algorithms%2C%20applications%2C%20and%20platforms,manufacturing%20process%2C%20and%20reduce%20costs.
https://www.researchgate.net/publication/337516709_A_Review_of_Machine_Learning_Applications_in_Additive_Manufacturing
https://link.springer.com/article/10.1007/s10845-020-01567-0
https://apmonitor.com/pds/index.php/Main/AdditiveManufacturing
https://sigmalabsinc.com/machine-learning-a-game-changer-for-additive-manufacturing-quality-assurance/
https://blogs.3ds.com/simulia/category/additive-manufacturing/
https://www.metal-am.com/articles/machine-learning-and-additive-manufacturing-what-does-the-future-hold/
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