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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

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Modern Applied Statistics With S

Thermal issues in machine tools

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

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

Despite continuous technological improvements in metal additive manufacturing (AM) systems, process stability is still affected by several possible sources of defects especially in the presence of challenging materials. Thus, both the research community and the major AM system developers have focused an increasing attention on in situ sensing and monitoring tools in the last years. However, there is still a lack of statistical methods to automatically detect the onset of a defect and signal an alarm during the part's layer-wise production. This study contributes to this framework with two levels of novelty. First, it presents an in situ monitoring method that integrates the acquisition of infrared images with a data mining approach for feature extraction and a statistical process monitoring technique to design a data-driven and automated alarm rule. Second, the method is aimed at monitoring powder bed fusion processes for difficult-to-process materials like zinc and its alloys, which impose several challenges to the process stability and quality because of their low melting and boiling points. To this aim, the proposed approach analyzes the byproducts generated by the interaction between the energy source and the material. In particular, it detects unstable behaviors by analyzing the salient properties of the process plume to detect unstable melting conditions. This case study entails an SLM process on zinc powder, where different sets of process parameters were tested leading either to in-control or out-of-control quality conditions. A comparison analysis highlights the effectiveness of plume-based stability monitoring.

In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume

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.

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

The possibility of using a variety of sensor signals acquired during metal powder bed fusion processes, to support part and process qualification and for the early detection of anomalies and defects, has been continuously attracting an increasing interest. The number of research studies in this field has been characterised by significant growth in the last few years, with several advances and new solutions compared with first seminal works. Moreover, industrial powder bed fusion (PBF) systems are increasingly equipped with sensors and toolkits for data collection, visualisation and, in some cases, embedded in-process analysis. Many new methods have been proposed and defect detection capabilities have been demonstrated. Nevertheless, several challenges and open issues still need to be tackled to bridge the gap between methods proposed in the literature and actual industrial implementation. This paper presents an updated review of the literature on in-situ sensing, measurement and monitoring for metal PBF processes, with a classification of methods and a comparison of enabled performances. The study summarises the types and sizes of defects that are practically detectable while the part is being produced and the research areas where additional technological advances are currently needed.

https://iopscience.iop.org/article/10.1088/1361-6501/ac0b6b/pdf

Marco Grasso

Papers

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

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

In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume

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

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review