Additive manufacturing (AM) is a new paradigm for the design and production of high-performance components for aerospace, medical, energy, and automotive applications. This review will exclusively cover directed energy deposition (DED)-AM, with a focus on the deposition of powder-feed based metal and alloy systems. This paper provides a comprehensive review on the classification of DED systems, process variables, process physics, modelling efforts, common defects, mechanical properties of DED parts, and quality control methods. To provide a practical framework to print different materials using DED, a process map using the linear heat input and powder feed rate as variables is constructed. Based on the process map, three different areas that are not optimized for DED are identified. These areas correspond to the formation of a lack of fusion, keyholing, and mixed mode porosity in the printed parts. In the final part of the paper, emerging applications of DED from repairing damaged parts to bulk combinatorial alloys design are discussed. This paper concludes with recommendations for future research in order to transform the technology from “form” to “function,” which can provide significant potential benefits to different industries.

https://www.mdpi.com/2079-6412/9/7/418/pdf

State of the Art in Directed Energy Deposition: From Additive Manufacturing to Materials Design

Manufacturing organizations need to use different kinds of techniques and tools in order to fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining (DM) techniques and tools could be very helpful for dealing with challenges in manufacturing. Therefore, in this paper, a comprehensive literature review is presented to provide an overview of how machine learning techniques can be applied to realize manufacturing mechanisms with intelligent actions. Furthermore, it points to several significant research questions that are unanswered in the recent literature having the same target. Our survey aims to provide researchers with a solid understanding of the main approaches and algorithms used to improve manufacturing processes over the past two decades. It presents the previous ML studies and recent advances in manufacturing by grouping them under four main subjects: scheduling, monitoring, quality, and failure. It comprehensively discusses existing solutions in manufacturing according to various aspects, including tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machine, neural network), learning types (i.e., ensemble learning, deep learning), and performance metrics (i.e., accuracy, mean absolute error). Furthermore, the main steps of knowledge discovery in databases (KDD) process to be followed in manufacturing applications are explained in detail. In addition, some statistics about the current state are also given from different perspectives. Besides, it explains the advantages of using machine learning techniques in manufacturing, expresses the ways to overcome certain challenges, and offers some possible further research directions.

Machine learning and data mining in manufacturing

Laser additive manufacturing is attractive for the production of complex, three-dimensional parts from metallic powder using a computer-aided design model1–3 The approach enables the digital control of the processing parameters and thus the resulting alloy’s microstructure, for example, by using high cooling rates and cyclic re-heating4–10 We recently showed that this cyclic re-heating, the so-called intrinsic heat treatment, can trigger nickel-aluminium precipitation in an iron–nickel–aluminium alloy in situ during laser additive manufacturing9 Here we report a Fe19Ni5Ti (weight per cent) steel tailor-designed for laser additive manufacturing This steel is hardened in situ by nickel-titanium nanoprecipitation, and martensite is also formed in situ, starting at a readily accessible temperature of 200 degrees Celsius Local control of both the nanoprecipitation and the martensitic transformation during the fabrication leads to complex microstructure hierarchies across multiple length scales, from approximately 100-micrometre-thick layers down to nanoscale precipitates Inspired by ancient Damascus steels11–14—which have hard and soft layers, originally introduced via the folding and forging techniques of skilled blacksmiths—we produced a material consisting of alternating soft and hard layers Our material has a tensile strength of 1,300 megapascals and 10 per cent elongation, showing superior mechanical properties to those of ancient Damascus steel12 The principles of in situ precipitation strengthening and local microstructure control used here can be applied to a wide range of precipitation-hardened alloys and different additive manufacturing processes A Damascus-like steel consisting of alternating hard and soft layers is created by using a laser additive manufacturing technique and digital control of the processing parameters

High-strength Damascus steel by additive manufacturing

Directed energy deposition (DED) additive manufacturing: Physical characteristics, defects, challenges and applications

Laser cladding and surface alloying are surface modification techniques employed to fabricate thin coating/layer with improved surface properties or to refurbish surface defects by forming highly resistant gradient coatings/layers on the substrate. High energy density and cooling rates make these techniques suitable to process a wide range of materials. In recent years, due to the development of high power lasers, improved controlling and delivery mechanisms have attracted extensive research in laser surface treatment. Researchers have analyzed various process factors to improve process performance. The experimental and theoretical studies show that the performance of laser cladding and surface alloying techniques can be enhanced significantly by the proper selection of input process parameters. This paper summarizes various research works carried out so far in the area of laser cladding and surface alloying of different materials and their applications. It reports the research outcomes of experimental and theoretical studies conducted to improve the process performance. A brief introduction of various laser surface treatment processes is also included. Besides these various problems, their solutions and trend for future works have also been discussed.

Recent trends in laser cladding and surface alloying

Influence of energy density on macro/micro structures and mechanical properties of as-deposited Inconel 718 parts fabricated by laser engineered net shaping

This paper explores the specific energy consumption (SEC) and environmental impacts for typical additive manufacturing processes. Also, the paper examines the possibility that ensure the product quality while reducing energy consumption with experimental analysis. The results show that (1) the SEC of additive manufacturing processes is related not only to material characteristics but also to the process input parameters; (2) it is possible to increase the energy efficiency without reducing product quality by adjusting the process rate or selecting different materials; and (3) the global warming potential (GWP) result of AM processes indicates that the GWP is brought about principally by the energy production process. The information provided by this project can also be of benefit to life cycle assessment and other environmental impact assessment related to AM processes.

Investigation of Energy Requirements and Environmental Performance for Additive Manufacturing Processes

Data mining (DM) with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the service stage. A comprehensive analysis of DM with Big Data and a review of its application in the stages of its lifecycle will not only benefit researchers to develop strong research themes and identify gaps in the field but also help practitioners for DM application system development. In this paper, a brief clarification of DM-related topics is presented first. A flowchart of DM and the main content of the flowchart steps are given in which commonly used data preparation and preprocessing approaches, DM functions and techniques, and performances indicators are summarized. Then, a comprehensive review covering 105 articles from 2007 to 2017 on DM or Big Data applications in the electronics industry is provided according to the flowchart from various points of view such as data handling, applications of DM, or Big Data at different lifecycle stages, and the software used in the applications. On this basis, a diagram of data content for different knowledge areas and a framework for DM and Big Data applications in the electronics industry are established. Finally, conclusions and future research directions are given.

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

As a prospective candidate material for surface coating and repair applications, nickel-based superalloy Inconel 718 (IN718) was deposited on American Iron and Steel Institute (AISI) 4140 alloy steel substrate by laser engineered net shaping (LENS) to investigate the compatibility between two dissimilar materials with a focus on interface bonding and fracture behavior of the hybrid specimens. The results show that the interface between the two dissimilar materials exhibits good metallurgical bonding. Through the tensile test, all the fractures occurred in the as-deposited IN718 section rather than the interface or the substrate, implying that the as-deposited interlayer bond strength is weaker than the interfacial bond strength. From the fractography using scanning electron microscopy (SEM) and energy disperse X-ray spectrometry (EDS), three major factors affecting the tensile fracture failure of the as-deposited part are (i) metallurgical defects such as incompletely melted powder particles, lack-of-fusion porosity, and micropores; (ii) elemental segregation and Laves phase, and (iii) oxide formation. The fracture failure mechanism is a combination of all these factors which are detrimental to the mechanical properties and structural integrity by causing premature fracture failure of the as-deposited IN718.

Laser Engineered Net Shaping of Nickel-Based Superalloy Inconel 718 Powders onto AISI 4140 Alloy Steel Substrates: Interface Bond and Fracture Failure Mechanism.

AISI 4140 powder was directly deposited on AISI 4140 wrought substrate using laser engineered net shaping (LENS) to investigate the compatibility of a LENS-deposited part with the substrate. Tensile testing at room temperature was performed to evaluate the interface bond performance and fracture behavior of the test specimens. All the samples failed within the as-deposited zone, indicating that the interfacial bond is stronger than the interlayer bond inside the deposit. The fracture surfaces were analyzed using scanning electron microscopy (SEM) and energy disperse X-ray spectrometry (EDS). Results show that the tensile fracture failure of the as-deposited part is primarily affected by lack-of-fusion defects, carbide precipitation, and oxide particles inclusions, which causes premature failure of the deposit by deteriorating the mechanical properties and structural integrity.

Hoyeol Kim

Papers

Influence of energy density on macro/micro structures and mechanical properties of as-deposited Inconel 718 parts fabricated by laser engineered net shaping

Investigation of Energy Requirements and Environmental Performance for Additive Manufacturing Processes

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Laser Engineered Net Shaping of Nickel-Based Superalloy Inconel 718 Powders onto AISI 4140 Alloy Steel Substrates: Interface Bond and Fracture Failure Mechanism.

Tensile Fracture Behavior and Failure Mechanism of Additively-Manufactured AISI 4140 Low Alloy Steel by Laser Engineered Net Shaping.