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

Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

https://iopscience.iop.org/article/10.1088/1361-6501/aa5c4f/pdf

Process defects and in situ monitoring methods in metal powder bed fusion: a review

Revisiting fundamental welding concepts to improve additive manufacturing: From theory to practice

Fluid and particle dynamics in laser powder bed fusion

Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

Machine learning in additive manufacturing: State-of-the-art and perspectives

In-process Monitoring of Selective Laser Melting: Spatial Detection of Defects via Image Data Analysis

In-situ monitoring of metal additive manufacturing (AM) processes is a key issue to determine the quality and stability of the process during the layer-wise production of the part. The quantities that can be measured via in-situ sensing can be referred to as “process signatures”, and can represent the source of information to detect possible defects. Most of the literature on in-situ monitoring of Laser Power Bed Fusion (LPBF) processes focuses on the melt-pool, laser track and layer image as source of information to detect the onset of possible defects. Up to our knowledge, this paper represents a first attempt to investigate the suitability of including spatter-related information to characterize the LPBF process quality. High-speed image acquisition, coupled with image segmentation and feature extraction, is used to estimate different statistical descriptors of the spattering behaviour along the laser scan path. A logistic regression model is developed to determine the ability of spatter-related descriptors to classify different energy density conditions corresponding to different quality states. The results show that by including spatters as process signature driver, a significant increase of the capability to detect under-melting and over-melting conditions is observed. This is why future research on spatter signature analysis and modelling is highly encouraged to improve the effectiveness of in-situ monitoring tools.

Vittorio Laguzza

Papers

In-process Monitoring of Selective Laser Melting: Spatial Detection of Defects via Image Data Analysis

On the use of spatter signature for in-situ monitoring of Laser Powder Bed Fusion