Thomas G. Spears

Bio: Thomas G. Spears is an academic researcher from GE Aviation. The author has contributed to research in topics: Process control & Fusion. The author has an hindex of 1, co-authored 1 publications receiving 270 citations.

In-process sensing in selective laser melting (SLM) additive manufacturing

Abstract: Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing

Abstract: We developed and applied a novel approach for shape agnostic detection of multiscale flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ sensor data. Flaws in LPBF range from porosity at the micro-scale (< 100 µm), layer related inconsistencies at the meso-scale (100 µm to 1 mm) and geometry-related flaws at the macroscale (> 1 mm). Existing data-driven models are primarily focused on detecting a specific type of LPBF flaw using signals from one type of sensor. Such approaches, which are trained on data from simple cuboid and cylindrical-shaped coupons, have met limited success when used for detecting multiscale flaws in complex LPBF parts. The objective of this work is to develop a heterogenous sensor data fusion approach capable of detecting multiscale flaws across different LPBF part geometries and build conditions. Accordingly, data from an infrared camera, spatter imaging camera, and optical powder bed imaging camera were acquired across separate builds with differing part geometries and orientations (Inconel 718). Spectral graph-based process signatures were extracted from this heterogeneous thermo-optical sensor data and used as inputs to simple machine learning models. The approach detected porosity, layer-level distortion, and geometry-related flaws with statistical fidelity exceeding 93% (F-score).

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

Abstract: 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.

Melt pool temperature and cooling rates in laser powder bed fusion

Abstract: In laser powder bed fusion, melt pool dynamics and stability are driven by the temperature field in the melt pool. If the temperature field is unfavourable defects are likely to form. The localised and rapid heating and cooling in the process presents a challenge for the experimental methods used to measure temperature. As a result, understanding of these process fundamentals is limited. In this paper a method is developed that uses coaxial imaging with high-speed cameras to give both the spatial and temporal resolution necessary to resolve the surface temperature of the melt pool. A two wavelength imaging setup is used to account for changes in emissivity. Temperature fields are captured at 100 kHz with a resolution of 20 μm during the processing of a simple Ti6Al4V component. Thermal gradients in the range 5–20 K/μm and cooling rates in range 1–40 K/μs are measured. The results presented give new insight into the effect of parameters, geometry and scan path on the melt pool temperature and cooling rates. The method developed here provides a new tool to assist in optimising scan strategies and parameters, identifying the causes of defect prone locations and controlling cooling rates for local microstructure development.

An overview of residual stresses in metal powder bed fusion

Abstract: Metal additive manufacturing (AM) has garnered tremendous research and industrial interest in recent years; in the field, powder bed fusion (PBF) processing is the most common technique, with selective laser melting (SLM) dominating the landscape followed by electron beam melting (EBM). Through continued process improvements, these methods are now often capable of producing high strength parts with static strengths exceeding their conventionally manufactured counterparts. However, PBF processing also results in large and anisotropic residual stresses (RS) that can severely affect fatigue properties and result in geometric distortion. The dependence of RS formation on processing variables, material properties and part geometry has made it difficult to predict efficiently and has hindered widespread acceptance of AM techniques. Substantial investigations have been conducted with regards to RS in PBF processing, which have illuminated a number of important relationships, yet a review encompassing this information has not been available. In this review, we survey and assemble the knowledge existing in the literature regarding RS in PBF processes. A discussion of background mechanics for RS development in AM is provided along with methods of measurement, highlighting the anisotropic nature of the stress fields. We then review modeling efforts and in-process experimental measurements made to advance process understanding, followed by a thorough analysis and summary of the known relationships of both material properties and processing variables to resulting RS. The current state of knowledge and future research needs for the field are discussed.

Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm

Abstract: Despite the rapid adoption of laser powder bed fusion (LPBF) Additive Manufacturing by industry, current processes remain largely open-loop, with limited real-time monitoring capabilities. While some machines offer powder bed visualization during builds, they lack automated analysis capability. This work presents an approach for in-situ monitoring and analysis of powder bed images with the potential to become a component of a real-time control system in an LPBF machine. Specifically, a computer vision algorithm is used to automatically detect and classify anomalies that occur during the powder spreading stage of the process. Anomaly detection and classification are implemented using an unsupervised machine learning algorithm, operating on a moderately-sized training database of image patches. The performance of the final algorithm is evaluated, and its usefulness as a standalone software package is demonstrated with several case studies.

Apparatuses, systems and methods for three-dimensional printing

Abstract: The present disclosure provides three-dimensional (3D) objects, 3D printing processes, as well as methods, apparatuses and systems for the production of a 3D object. Methods, apparatuses and systems of the present disclosure may reduce or eliminate the need for auxiliary supports. The present disclosure provides three dimensional (3D) objects printed utilizing the printing processes, methods, apparatuses and systems described herein.