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Machine learning in manufacturing towards industry 4.0: from ‘for now’ to ‘four-know’.

1. Introduction

• RQ1: How does ML benefit manufacturing, and what are the typical ML application cases?
• RQ2: How are ML-based solutions developed for problems in manufacturing engineering?
• RQ3: What are the challenges and opportunities in applying ML in manufacturing contexts?

2. Overview of Machine Learning in Manufacturing

2.1. introduction of four-know and four-level.

• Know-what deals with understanding of the current states of machines, processes, or production systems, which can help in rapid decision-making. It should be noted that Know-what goes beyond visualization of real-time data. Instead, data should be processed, analyzed, and distilled into information which enables decision-making. For instance, typical examples of Know-what in manufacturing are defect detection in quality control [ 17 , 18 ], fault detection in process/machine monitoring [ 19 , 20 ], and soft sensor modelling [ 21 , 22 ].
• Know-why , based on the information from Know-what, aims to identify inner patterns from historical data, thereby discovering the reasons for a thing happening. Know-why includes the identification of interactions among different variables [ 23 ] and the discovery of cause-effect relationship between an event and other variables [ 24 , 25 ]. On one hand, Know-why can indicate most important factors for understanding Know-what. On the other hand, Know-why is the prerequisite for Know-when, as the reliability of predictions is heavily dependent upon the quality of casual inference.
• Know-when , built on Know-why, involves timely predictions of events or prediction of key variables based on historical data, allowing the decision-maker can take actions at early stages. For instance, Know-when in manufacturing includes quality prediction based on relevant variables [ 26 , 27 ], predictive maintenance via detection of incipient anomalies before break-down [ 28 , 29 ], and predicting Remaining Useful Life (RUL) [ 30 , 31 ].
• Know-how , on the foundation of Know-when, can recommend decisions that help adapt to expected disturbance and can aid in self-optimization. Examples in manufacturing include prediction-based process control [ 27 , 32 ], scheduling of predictive maintenance tasks [ 33 , 34 ], dynamic scheduling in the flexible production [ 35 , 36 ], and inventory control [ 34 ].

2.2. Literature Review Methodology

• The study dealt with the context of manufacturing;
• The study dealt with ML applications in specific fields.

3. Pipeline of Applying Machine Learning in Manufacturing

3.1. data collection.

• Image data , matrices of pixels with two or more dimensions, such as gray-scale images or colored images. Image data can acquired by with vision systems, through data transformations such as simple concatenation of several one-dimensional vectors with same length, or by the transformation of images from the spatial domain to the frequency domain.
• Tabular data organized in a table, where normally one axis represents attributes and another axis represents observations. Tabular data are typically observed in production data, where the attributes of events of interest are collected. Though tabular data share a similar data structure with image data, the latter are more focused on one-dimensional interaction among attributes, while image data typically stress spatial interactions in both dimensions.
• Time series data , sequences of one or more attributes over time, with the former corresponding to univariate time series and the latter multivariate time series. In manufacturing, time series data are normally acquired with sensors whenever there is a need for monitoring time flow changes of data.
• Text data , including written documents with words, sentences or paragraphs. Examples of text data in manufacturing include maintenance reports on machines and descriptions of unexpected disturbances or events in production.

3.2. Data Cleaning

3.3. data transformation, 3.4. model training, 3.5. model analysis, 3.6. model push, 4. machine learning methods and applications, 4.1. supervised learning methods, 4.2. unsupervised learning methods.

• Say we wish to condense d features in our data matrix X to k features. The first step is to standardize the input data: z = x − μ / σ where μ is the mean and σ is the standard deviation.
• Next, it is necessary to find the covariance matrix of the standardized input data. The covariance of variables X and Y can be written as follows: cov ( X , Y ) = 1 n − 1 ∑ i = 1 n ( X i − x → ) ( Y i − y ¯ ) . (1)
• The third steps is to find all of the eigenvalues and eigenvectors of the covariance matrix: A v → = λ v → (2) A v → − λ v → = 0 (3) v → ( A − λ I ) = 0 . (4)
• Then, the eigenvector corresponding to the largest eigenvalue is the direction with the maximum variance, the eigenvector corresponding to second-largest eigenvalue is the direction with the second maximum variance, etc.
• To obtain k features, it is necessary to multiply the original data matrix by the matrix of eigenvectors corresponding to the k largest eigenvalues.
• Quality improvement (Know-why, product level): by analyzing the variations of a product’s features, PCA can be used to identify the causes of product defects [ 122 ].
• Machine monitoring (Know-why, machine level): by analyzing sensor data from a machine, PCA can be used to detect incipient patterns in the data that indicate potential issues with the machinery, such as wear and tear [ 123 ].
• Process optimization (Know-why, process level): by analyzing variations in the process data, PCA can be used to identify the most important factors that affect the process, allowing the manufacturer to optimize the process and thereby reduce costs [ 124 ].
• Anomaly detection (Know-what): an AE can be trained to reconstruct normal data and detect abnormal data by measuring the reconstruction error, which allows the manufacturer to detect and address issues such as product defects [ 124 ] and machinery failure [ 127 ].
• Feature selection (Know-why): an AE can be used to identify the most important features in the data and remove the noise and irrelevant information, which can be used for diagnosis of product defects or to detect events of interests [ 128 ].
• Dimensionality reduction: an AE can be used to reduce the dimensionality of large and complex datasets, making it easier to identify patterns and trends [ 129 ].

4.3. Semi-Supervised Learning Methods

4.4. reinforcement learning methods, 5. challenges and future directions.

• Lack of data . Preparing the data used for ML is not a simple task, as the scale and the quality of data can greatly affect the performance of ML models. The most common challenge involves preparing a large amount of organized input data, and ensuring high-quality labels if labels are needed. Despite manufacturing data becoming increasingly more accessible due to the development of sensors and the Internet of Things, gathering meaningful data is time-consuming and costly in many cases, for example, fault detection and RUL prediction. This issue might be alleviated by the Synthetic Minority Over-sampling Technique (SMOTE) [ 162 ]. However, SMOTE cannot capture complex representative data, as it often relies on interpolation [ 163 ]. Data augmentation [ 139 , 164 ] or transfer learning [ 165 ] may address this problem. The aim of data augmentation is to enlarge dataset by means of transforming data [ 139 ], by transforming both data and labels, as with MixUp [ 166 ], or by generating synthetic data using generative models [ 167 , 168 ]. On the contrary, instead of focusing on expanding data, transfer learning aims to leverage knowledge from similar external datasets. A typically used method in transfer learning is parameter transfer, where a pretrained model from a similar dataset is employed for initialization [ 165 ]. Another situation involving lack of data is that certain data cannot be shared due to data privacy and security issues. In confronting this problem, Federated Learning (FL) [ 169 ] might be a potential opportunity to enable model training across multiple decentralized devices while holding local data privately.
• Limited computing resources . The high performance of ML models always comes with high computational complexity. In particular, obtaining high accuracy with a neural network requires on millions or even billions of parameters [ 170 ]. However, limited computing resources in industries makes it a challenge to deploy heavy ML models in real-time industrial environments. Possible approaches include model compression via pruning and sharing of model parameters [ 171 ] and knowledge distillation [ 172 ]. Parameter pruning aims to reduce the number of model parameters by removing redundant parameters without any effect on model performance. By contrast, seeking the same goal, knowledge distillation focuses on distilling knowledge from a cumbersome neural network to a lightweight network to allow it to be deployed more easily with limited computing resources.
• Changing circumstances . Most ML applications in manufacturing focus only on model development and verification in off-line environments. However, when deploying these models in running production, their performance may be degraded due to changing circumstances, leading to changes in data distribution, that is, drift [ 173 , 174 ]. Therefore, manual model adjustment over time, which is time-consuming, is usually unavoidable [ 175 ]. However, this could be addressed in the future by automatic model adaption [ 174 ], in which data drifts are automatically detected and handled with less resources.
• Interpretability of results . Many expectations have been placed on ML to overcome all types of problems without the need for prior knowledge. In particular, ML models are expected to directly learn higher level knowledge such as Know-when and Know-how, which is difficult for human beings to obtain in manufacturing. However, without the foundations of early-stage knowledge and an understanding of the data, the results inferred from big data by black-box ML models are meaningless and unreliable. For instance, predictions blindly obtained from all data, including both relevant and irrelevant data, might even degrade performance due to the GIGO (garbage in, garbage out) phenomenon [ 176 ]. To overcome this problem, future directions within ML development might include incorporating physical models into ML models [ 177 ] or obtaining Four-know knowledge successively.
• Uncertainty of results . Related to the challenge of interpretability is the challenge of uncertain results. The success of manufacturing depends heavily on the quality of the resulting products. As every manufacturing process has a degree of variability, almost all industrial manufacturers use statistical process control (SPC) to ensure a stable and defined quality of products [ 178 ]. A central element of statistical process control is the determination and handling of statistical uncertainty. The uncertainty of ML results often cannot be quantified reliably and efficiently, even with today’s state-of-the-art [ 179 , 180 , 181 ]. Furthermore, model complexity and severe non-linearity in ML can hinder the evaluation of uncertainty [ 182 ]. Although there are promising approaches, e.g., Gaussian mixture models for NN [ 183 , 184 ] and Probabilistic Neural Network (PNN) [ 184 ], or the use of Baysian Networks [ 180 ], there are several limitations limiting potential applications, such as high computational cost and simplified assumptions [ 184 ]. Therefore, future research needs to make progress on the general theory of integrating uncertainty into ML methods to allow manufacturing in order to ensure high quality and stability in production.

6. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Chen, T.; Sampath, V.; May, M.C.; Shan, S.; Jorg, O.J.; Aguilar Martín, J.J.; Stamer, F.; Fantoni, G.; Tosello, G.; Calaon, M. Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’. Appl. Sci. 2023 , 13 , 1903. https://doi.org/10.3390/app13031903

Chen T, Sampath V, May MC, Shan S, Jorg OJ, Aguilar Martín JJ, Stamer F, Fantoni G, Tosello G, Calaon M. Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’. Applied Sciences . 2023; 13(3):1903. https://doi.org/10.3390/app13031903

Chen, Tingting, Vignesh Sampath, Marvin Carl May, Shuo Shan, Oliver Jonas Jorg, Juan José Aguilar Martín, Florian Stamer, Gualtiero Fantoni, Guido Tosello, and Matteo Calaon. 2023. "Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’" Applied Sciences 13, no. 3: 1903. https://doi.org/10.3390/app13031903

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• DOI: 10.1080/21693277.2016.1192517
• Corpus ID: 52037185

Machine learning in manufacturing: advantages, challenges, and applications

• Thorsten Wuest , Daniel Weimer , +1 author K. Thoben
• Published 1 January 2016
• Engineering, Computer Science
• Production & Manufacturing Research

897 Citations

Analysis of non-traditional machining processes using machine learning, identification and characterization of challenges in the future of manufacturing for the application of machine learning, applications of artificial intelligence in engineering and manufacturing: a systematic review.

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An Example of Machine Learning Applied in Additive Manufacturing

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• 11 Excerpts

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Machine learning in precision manufacturing: a collaborative computer and mechanical engineering perspective, machine learning for industrial applications: a comprehensive literature review, machine learning in manufacturing towards industry 4.0: from ‘for now’ to ‘four-know’, 119 references, machine learning approaches to manufacturing, machine-learning techniques and their applications in manufacturing.

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Machine Learning Algorithms in Heavy Process Manufacturing

Recognizing yield patterns through hybrid applications of machine learning techniques, ai and machine learning techniques for managing complexity, changes and uncertainties in manufacturing, using inductive machine learning to support decision making in machining processes, an approach to monitoring quality in manufacturing using supervised machine learning on product state data, predicting defects in disk drive manufacturing: a case study in high-dimensional classification, a step towards intelligent manufacturing: modelling and monitoring of manufacturing processes through artificial neural networks, hybrid ai approaches to intelligent manufacturing, related papers.

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Study examines how machine learning boosts manufacturing

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Which companies deploy machine intelligence (MI) and data analytics successfully for manufacturing and operations? Why are those leading adopters so far ahead — and what can others learn from them?

MIT Machine Intelligence for Manufacturing and Operations (MIMO) and McKinsey and Company have the answer, revealed in a first-of-its-kind  Harvard Business Review article. The piece chronicles how MIMO and McKinsey partnered for a sweeping 100-company survey to explain how high-performing companies successfully wield machine learning technologies (and where others could improve).

Created by the MIT Leaders for Global Operations (LGO) program, MIMO is a research and educational program designed to boost industrial competitiveness by accelerating machine intelligence’s deployment and understanding. The goal is to “find the shortest path from data to impact,” says managing director Bruce Lawler SM ’92.

As such, the McKinsey project encapsulates MIMO’s mission of demystifying effective machine-learning use. The survey studied companies across sectors, probing their digital, data analytics, and MI tech usage; goals (ranging from efficiency to customer experience to environmental impact); and tracking. Respondents were drawn from MIT and McKinsey’s wide-ranging networks.

“The study is probably the broadest that anybody has done in the space: 100 companies and 21 performance indicators,” says Vijay D’Silva SM ’92, a senior partner at McKinsey and Company who collaborated with MIMO on the project.

Overall, those who extracted the biggest gains from digital technologies had strong governance, deployment, partnerships, MI-trained employees, and data availability. They also spent up to 60 percent more on machine learning than their competitors.

One standout company is biopharmaceutical giant Amgen, which uses deep-learning image-augmentation to maximize efficiency of visual inspection systems. This technique pays off by increasing particle detection by 70 percent and reduces the need for manual inspections. AJ Tan PhD ’19, MBA ’21, SM ’21 was instrumental in the effort: He wrote his LGO thesis about the project, winning last year’s Best Thesis Award at graduation.

Lawler says Tan’s work exemplifies MIMO’s mission of bridging the gap between machine learning and manufacturing before it’s too late.

“We saw a need to bring these powerful new technologies into manufacturing more quickly. In the next 20 to 30 years, we’re going to add another 3 billion people to the globe, and they're going to want the lifestyles that you and I enjoy. Those typically require manufactured things. How do we get better at translating natural resources into human well-being? One of the big vehicles for doing that is manufacturing, and one of the newest tools is AI and machine learning,” he says.

For the survey, MIMO issued each company a 30-page playbook analyzing how they compared against other companies across a range of categories and metrics, from strategy to governance to data execution. This will help them to target areas of opportunity or where to invest. Lawler hopes that this will be a longitudinal study with a wider scope and playbook each year — a vast but impactful undertaking with LGO brainpower as the driving engine.

“MIT was hugely important and critical to the piece of work and an amazing partner for us. We had talented MIT students on the team who did most of the analysis jointly with McKinsey, which improved the quality of the work as a result,” says D’Silva.

This collaborative approach is central to MIMO’s philosophy as an information convener and partner for the private sector. The goal is drive “an effective transformation in industries that achieve not just technical goals, but also business goals and social goals,” says Duane Boning, engineering faculty director at MIT LGO, and faculty lead at MIMO.

This fusion of research and collaboration is the logical next step for LGO, he says, because it’s always been at the forefront of problem-solving for global operations. Machine learning is definitely the latest big knowledge gap for many businesses, but not the first, and MIMO can teach companies how to apply it.

“[I liken] it to 30 years ago when LGO got started, when it was all about lean manufacturing principles. About 15 years ago, it was the supply chain idea. That sparked us to think — not just for our LGO students, but for the benefit of industry more broadly — for understanding this big change, for facilitating it, for doing research and getting connections into other actual research activities, we need some effort to catalyze this,” Boning says. “That’s [MIMO’s] real excitement: What are ideas that work? What are methodologies that work? What are technologies that work? And LGO students, in some sense, are the perfect vehicle to discover some of that.”

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Condensed Matter > Materials Science

Title: consistent machine learning for topology optimization with microstructure-dependent neural network material models.

Abstract: Additive manufacturing methods together with topology optimization have enabled the creation of multiscale structures with controlled spatially-varying material microstructure. However, topology optimization or inverse design of such structures in the presence of nonlinearities remains a challenge due to the expense of computational homogenization methods and the complexity of differentiably parameterizing the microstructural response. A solution to this challenge lies in machine learning techniques that offer efficient, differentiable mappings between the material response and its microstructural descriptors. This work presents a framework for designing multiscale heterogeneous structures with spatially varying microstructures by merging a homogenization-based topology optimization strategy with a consistent machine learning approach grounded in hyperelasticity theory. We leverage neural architectures that adhere to critical physical principles such as polyconvexity, objectivity, material symmetry, and thermodynamic consistency to supply the framework with a reliable constitutive model that is dependent on material microstructural descriptors. Our findings highlight the potential of integrating consistent machine learning models with density-based topology optimization for enhancing design optimization of heterogeneous hyperelastic structures under finite deformations.

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Machine learning-assisted prediction modeling for anisotropic flexural strength variations in fused filament fabrication of graphene reinforced poly-lactic acid composites

• Full Research Article
• Published: 26 August 2024

Cite this article

• Tapish Raj   ORCID: orcid.org/0009-0005-6844-9749 1 ,
• Amrit Tiwary 1 ,
• Akash Jain   ORCID: orcid.org/0000-0003-0003-6567 1 ,
• Gaurang Swarup Sharma 2 ,
• Prem Prakash Vuppuluri 2 ,
• Ankit Sahai   ORCID: orcid.org/0000-0001-7333-8967 1 &
• Rahul Swarup Sharma   ORCID: orcid.org/0000-0001-8874-4685 1  

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The objective of this study is to conduct a comparative analysis of various machine learning algorithms on the flexural properties of graphene-reinforced poly-lactic acid fabricated through fused filament fabrication. The selected process parameters include raster orientation (0°, 45°, 90°), layer thickness (0.05 mm, 0.1 mm, 0.15 mm, 0.2 mm, 0.25 mm, 0.3 mm), and feed rate (20 mm/s, 40 mm/s, 60 mm/s). The fabricated specimens underwent flexural testing, and fractography was performed after the testing. The flexural strength variations depicted maximum and minimum values of 129.511 MPa and 59.959 MPa, respectively. Further, the flexural testing data is used for the evaluation of the effectiveness of various machine learning algorithms, namely linear regression, random forest regression, gradient boosting regression, extreme gradient boosting regression, voting regression algorithms, and artificial neural networks. The results show that linear regression outperforms all other alternatives, obtaining a coefficient of determination of 98.9%, as compared to 95.7%, 96.8%, 97.3%, and 97.8%, respectively, for random forest regression, gradient boosting regression, extreme gradient boosting regression, and artificial neural network. Consistently superior performance is observed for linear regression over other performance metrics as well, i.e., mean absolute error and root mean square error. By demonstrating the efficacy of machine learning in conjunction with optimization techniques inspired by nature, this study advances predictive methodology in material science, especially in the field of additive manufacturing.

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Acknowledgements

The authors acknowledge the help from ACMS, IIT Kanpur, India for allowing to perform flexural testing. Support from FESEM lab, Department of Chemistry, DEI is highly appreciated for fractography analysis.

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3D Printing and Additive Manufacturing Lab, Department of Mechanical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, India

Tapish Raj, Amrit Tiwary, Akash Jain, Ankit Sahai & Rahul Swarup Sharma

Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, India

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Raj, T., Tiwary, A., Jain, A. et al. Machine learning-assisted prediction modeling for anisotropic flexural strength variations in fused filament fabrication of graphene reinforced poly-lactic acid composites. Prog Addit Manuf (2024). https://doi.org/10.1007/s40964-024-00768-w

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