THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

/pdf/a-review-on-machine-learning-in-3d-printing-applications-1fsr2i1b6p.pdf

A review on machine learning in 3D printing: applications, potential, and challenges

Summary Increasing demand for the fabrication of components with complex designs has spurred a revolution in manufacturing methods. Additive manufacturing stands out as a promising technology when it comes to prototyping multi-functional and multi-material designs. However, challenges still exist in the additive manufacturing process, such as mismatched material properties, lack of build consistency, and pervasive imperfections in the printed part. These inherent challenges can be avoided by implementing algorithms to detect imperfections and modulate printing parameters in real time. In this paper, several algorithms, with a focus on machine learning methods, are reviewed and explored to systematically tackle the three main stages of the additive manufacturing process: geometrical design, process parameter configuration, and in situ anomaly detection. Current challenges and future opportunities for algorithmically driven additive manufacturing processes, as well as potential applications to other manufacturing methods, are also discussed.

Machine Learning for Advanced Additive Manufacturing

Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm

In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of...

Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Knowledge based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modeling

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Knowledge based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modeling