Research Trends in Sustainable Manufacturing: A Review and Future Perspective based on Research Databases

Lee, Kang-In; Song, Jae Bok; Min, Soo‐Hong; Lee, Hye-Sung; Song, Ki-Young; Chu, Chong Nam; Ahn, Sung‐Hoon

doi:10.1007/s40684-019-00113-5

Cited by 54 publications

(28 citation statements)

References 63 publications

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“…In this respect, AM technologies can provide parts in less time compared with traditional production methods by producing less waste with a shorter processing step and will be very important for future industry applications because of these features. [ 1–5 ]…”

Section: Introductionmentioning

confidence: 99%

A Study on the Effects of Different Heat‐Treatment Parameters on Microstructure–Mechanical Properties and Corrosion Behavior of Maraging Steel Produced by Direct Metal Laser Sintering

Özer

Karaaslan

2020

steel research int.

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Additive manufacturing (AM) is the most remarkable manufacturing technology developed in recent years. With this technology, it can produce products directly from a computeraided design model. In this respect, AM technologies can provide parts in less time compared with traditional production methods by producing less waste with a shorter processing step and will be very important for future industry applications because of these features. [1-5] Selective laser sintering (SLS) is a process of bonding (sintering) powder particles to each other by exceeding the melting temperature utilizing a laser beam. SLS machines can be given different names. EOS GmbH (Germany) has described this process as direct metal laser sintering (DMLS). [6,7] DMLS is an advanced technology that enables the rapid production of complex-shaped 3D parts using metal powders. [8] DMLS has a unique ultrafine and metastable microstructure, thanks to its fast solidification rate (10 3-10 8 K s À1). [9] This rapid solidification results in high thermal gradients and short interaction time. Thus, the microstructures of DMLS-produced materials are different than those produced by conventional methods. Tool steels produced by AM methods also have microstructures that are quite different from traditional cast-tool steels. Due to the advantages of the SLS method, it is widely used in aerospace, medical, defense, and automotive industries. AlSi10Mg, titanium, stainless steels, composites, tool steels, and Ni-based alloys can be produced by the DMLS method. [10-15] The name "Maraging" comes from the combination of martensite and aging. [16] Maraging steels (MS1) are valuable materials due to their excellent formability, weldability, toughness, and high strength. Therefore, it is preferred in critical applications such as submarine bodies, gears, rocket engine cases, aircraft components, pressure vessels. [17-22] 18Ni-300 MS1 is used in the AM processes. MS1 is a steel that can be aged, and forms precipitates such as Ni 3 Mo, Ni 3 Al, Ni 3 Ti, Fe 2 Mo, etc. [23-30]. MS1 is a fascinating steel for engineering applications because of its good weldability, machinability, and surface treatment. MS1 produced with DMLS show low strength in as-built condition. Various heat treatments are applied to this material to increase strength. These heat treatments are directly aged from the as-built state (aged) and solution and after aging (SHT) heat treatments. [31,32] In the literature, MS1 studies that were produced by the SLM method have been done before, but these are limited. Also, no research has been done about MS1 produced by the DMLS

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Section: Introductionmentioning

confidence: 99%

A Study on the Effects of Different Heat‐Treatment Parameters on Microstructure–Mechanical Properties and Corrosion Behavior of Maraging Steel Produced by Direct Metal Laser Sintering

Özer

Karaaslan

2020

steel research int.

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“…If the cluster and C candidate have common data, the common data are placed into C anomaly , and the system issues a warning. 4 Figure 4 illustrates the key steps in the APMC. After the optimal grouping phase, the processed data set is divided into three clusters.…”

Section: B Semi-svm-based Pattern Matchingmentioning

confidence: 99%

“…The result of this step shows that the first cluster is closest to pattern 3, the second cluster is closest to pattern 2, and the last cluster is closest to pattern 0. Finally, the semi-SVM model is used to verify the results of 4 threshold G is a preset parameter, which is determined based on the loss of classification from an investigation. In this study, threshold G is set to 0.2. the aforementioned stages; the model indicates that all three clusters match a labeled pattern (3, 2, and 0, respectively).…”

Section: B Semi-svm-based Pattern Matchingmentioning

confidence: 99%

“…Currently, an emphasis on environmentally friendly practices has prompted society to seek more sustainable energy practices, with ambitious targets being set by many countries in an effort to achieve significant energy savings [1]- [4]. Home power usage, along with that of the industrial and commercial sectors, is one of the major routes to reaching these targets.…”

Section: Introductionmentioning

confidence: 99%

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Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

Wang

Yang

2019

IEEE Access

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Anomaly detection in home power monitoring can be categorized into two main types: detection of electrical theft, leakage, or nontechnical loss and monitoring anomalies in the daily activities of residents. Focusing on the application and practicality of anomaly detection, we propose sample efficient home power anomaly detection (SEPAD) with improved monitoring performance in terms of electricity usage as well as changes in the daily living activities of residents via provision of detailed feedback. SEPAD consists of two classifiers: an appliance pattern matching classifier (APMC) and an energy consumption habit classifier (ECHC). The APMC uses a single-source separation framework based on a semi-supervised support vector machine (semi-SVM) model. This semi-supervised learning method requires only a small amount of labeled data to achieve high accuracy in near real time and is a sample efficient detection method. The hidden Markov model (HMM)-based ECHC improves the rationality of SEPAD by providing anomaly detection functionality with respect to the daily activities of householders, especially the elderly and residents in developing areas. When SEPAD detects the appearance of an unknown pattern or known patterns contrary to the household's electricity usage habits, it triggers an alarm. SEPAD was applied to monitor power consumption data from Mkalama, a rural area in Tanzania with 52 households containing nearly 150 occupants connected to a solar powered off-grid network. The results of the practical test demonstrate the high accuracy and practicality of the proposed method.

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“…Currently, industries are focusing on sustainable manufacturing to cope with environmental and climate changes [ 4 ]. In particular, additive manufacturing is attracting attention as a process for manufacturing lightweight, high-efficiency parts [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Properties of Tool Steels Printed by Directed Energy Deposition Process on S45C Base Metal

et al. 2020

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We present a fundamental study on the development of trimming dies at room temperature for the hot-stamping process using directed energy deposition. Specimens of G and F materials were fabricated by machining 3D-printed blocks. The hardness of G-layered specimens was slightly higher than that of F-layered specimens, reaching approximately 700 HV at the surface. The G-layered specimens consisted of columnar and equiaxed dendrites, whereas the F-layered specimens mainly consisted of equiaxed dendrites. Spherical pores were observed inside the layered cross section, whereas relatively large irregular-shaped cavities were observed in layered boundaries. The tensile strengths of the G-layered and F-layered specimens were approximately 1800 and 1650 MPa, respectively. During bonding strength tests on an area bonded with S45C base metal, a fracture occurred in one case because of the lack of fusion at the boundary, and the F-layered specimens showed a lower strength than the G-layered ones. During wear tests on a quenched 1.5 GPa-grade aluminized steel plate, the F-layered specimens showed lower wear loss. However, the G-layered specimens showed better wear resistance during wear tests on a 1.5 GPa-grade electrogalvanized steel plate. These findings serve as fundamental data for additive manufacturing processes using tool steels of high-strength materials with high melting points.

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Research Trends in Sustainable Manufacturing: A Review and Future Perspective based on Research Databases

Cited by 54 publications

References 63 publications

A Study on the Effects of Different Heat‐Treatment Parameters on Microstructure–Mechanical Properties and Corrosion Behavior of Maraging Steel Produced by Direct Metal Laser Sintering

A Study on the Effects of Different Heat‐Treatment Parameters on Microstructure–Mechanical Properties and Corrosion Behavior of Maraging Steel Produced by Direct Metal Laser Sintering

Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

Properties of Tool Steels Printed by Directed Energy Deposition Process on S45C Base Metal

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