Anandakrishnan V., Sathish S., Duraiselvam Muthukannan, Dillibabu V. and Balamuralikrishnan N.
Aerospace and defence industries use the materials having better properties at elevated temperatures, and Inconel 718 is one of that. The complexity in realizing complex and…
Abstract
Purpose
Aerospace and defence industries use the materials having better properties at elevated temperatures, and Inconel 718 is one of that. The complexity in realizing complex and intricate shapes necessitate the product realization through additive manufacturing. This paper aims to investigate the wear behaviour of additive manufactured material.
Design/methodology/approach
The wear behaviour of additively manufactured Inconel 718 samples through direct metal laser sintering process at three different build orientations was experimentally investigated using a standard pin-on-disc wear tester.
Findings
Among the varied wear parameters, the load was identified as the most influencing parameter on the wear rate. In addition, the post-failure analysis of the worn surface of the pins under the scanning electron microscopy revealed the presence of various wear mechanisms.
Originality/value
Almost, the industries are now focussed on their production through additive manufacturing owing to its advantages. The present work displays the wear behaviour of the additive manufactured Inconel 718 and its associated wear mechanisms.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0322.
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Abdul Wahab Hashmi, Harlal Singh Mali and Anoj Meena
The purpose of this paper is to study the functionality of additively manufactured (AM) parts, mainly depending on their dimensional accuracy and surface finish. However, the…
Abstract
Purpose
The purpose of this paper is to study the functionality of additively manufactured (AM) parts, mainly depending on their dimensional accuracy and surface finish. However, the products manufactured using AM usually suffer from defects like roughness or uneven surfaces. This paper discusses the various surface quality improvement techniques, including how to reduce surface defects, surface roughness and dimensional accuracy of AM parts.
Design/methodology/approach
There are many different types of popular AM methods. Unfortunately, these AM methods are susceptible to different kinds of surface defects in the product. As a result, pre- and postprocessing efforts and control of various AM process parameters are needed to improve the surface quality and reduce surface roughness.
Findings
In this paper, the various surface quality improvement methods are categorized based on the type of materials, working principles of AM and types of finishing processes. They have been divided into chemical, thermal, mechanical and hybrid-based categories.
Research limitations/implications
The review has evaluated the possibility of various surface finishing methods for enhancing the surface quality of AM parts. It has also discussed the research perspective of these methods for surface finishing of AM parts at micro- to nanolevel surface roughness and better dimensional accuracy.
Originality/value
This paper represents a comprehensive review of surface quality improvement methods for both metals and polymer-based AM parts.
Graphical abstract of surface quality improvement methods
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Yuting Lv, Xing Ouyang, Yaojie Liu, Ying Tian, Rui Wang and Guijiang Wei
This paper aims to investigate the differences in hot corrosion behavior of the GTD222 superalloy and TiC/GTD222 composite in a mixed salt of 75% Na2SO4 and 25% K2SO4 at 900°C.
Abstract
Purpose
This paper aims to investigate the differences in hot corrosion behavior of the GTD222 superalloy and TiC/GTD222 composite in a mixed salt of 75% Na2SO4 and 25% K2SO4 at 900°C.
Design/methodology/approach
The GTD222 superalloy and TiC/GTD222 nickel-based composite were prepared using selective laser melting (SLM). Subsequently, the hot corrosion behavior of the two alloys was systematically investigated in a salt mixture consisting of 75% Na2SO4 and 25% K2SO4 (Wt.%) at 900°C.
Findings
The TiC/GTD222 composite exhibited better hot corrosion resistance compared to the GTD222 superalloy. First, the addition of alloying elements led to the formation of a protective oxide film on the TiC/GTD222 composites 20 h before hot corrosion. Second, TiC/GTD222 composite corrosion surface has a higher Ti content, after 100 h of hot corrosion, the composite corrosion surface Ti content of 10.8% is more than two times the GTD222 alloy 4% Ti. The Ti and Cr oxides are tightly bonded, effectively resisting the erosion of corrosive elements.
Originality/value
The hot corrosion behavior of GTD222 superalloy and TiC/GTD222 composites prepared by SLM in a mixed salt of 75% Na2SO4 and 25% K2SO4 was studied for the first time. This study provides insights into the design of high-temperature alloys resistant to hot corrosion.
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Muhammad Arif Mahmood, Marwan Khraisheh, Andrei C. Popescu and Frank Liou
This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing…
Abstract
Purpose
This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy.
Design/methodology/approach
Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects.
Findings
The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB.
Originality/value
The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.
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Hayri Sezer, Joseph Tang, AMM Nazmul Ahsan and Sudhir Kaul
The purpose of this study is to develop a novel comprehensive three-dimensional computational model to predict the transient thermal behavior and residual stresses resulting from…
Abstract
Purpose
The purpose of this study is to develop a novel comprehensive three-dimensional computational model to predict the transient thermal behavior and residual stresses resulting from the layer-by-layer deposition in the direct metal laser sintering process.
Design/methodology/approach
In the proposed model, time integration is performed with an implicit scheme. The equations for heat transfer are discretized by a finite volume method with thermophysical properties of the metal powder and an updated convection coefficient at each time step. The model includes convective and radiative boundary conditions for the exposed surfaces of the part and constant temperatures for the bottom surface on the build plate. The laser source is modeled as a moving radiative heat flux along the scanning pattern, while the thermal gradients are used to calculate directional and von Mises residual thermal stresses by using a quasi-steady state assumption.
Findings
In this study, four different scanning patterns are analyzed, and the transient temperature and residual thermal stress fields are evaluated from these patterns. It is found that the highest stresses occur where the laser last leaves off on its scanning pattern for each layer.
Originality/value
The proposed model is designed to capture the layer-by-layer deposition for a three-dimensional geometry while considering the effect of the instantaneous melting of the powder, melt pool, dynamic calculation of thermophysical properties, ease of parametrization of various process parameters and the vectorization of the code for computational efficiency. This versatile model can be used for process parameter optimization of other laser powder bed fusion additive manufacturing techniques. Furthermore, the proposed approach can be used for analyzing different scanning patterns.
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Asad Waqar Malik, Muhammad Arif Mahmood and Frank Liou
The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of…
Abstract
Purpose
The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.
Design/methodology/approach
This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.
Findings
This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.
Originality/value
Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.
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Fabio Francisco da Silva, Lukas Daniel Filser, Fernando Juliani and Otávio José de Oliveira
Lean Six Sigma (LSS) is a continuous improvement methodology used to increase the efficiency and effectiveness of processes. Although there are several articles published, only…
Abstract
Purpose
Lean Six Sigma (LSS) is a continuous improvement methodology used to increase the efficiency and effectiveness of processes. Although there are several articles published, only two have analyzed the literature from a bibliometrics perspective. The purpose of this paper is to analyze the LSS literature by bibliometrics, identifying its state of the art, scientific gaps and research trends.
Design/methodology/approach
Articles published up to 2016 in the database Scopus were investigated to identify the most significant articles, authors, journals, institutions and countries based on citation counting as well as the most frequent keywords and subject areas on LSS. Articles published in 2014, 2015 and 2016 were analyzed to point out scientific gaps and to identify eight main research trends on LSS.
Findings
The research trends are: “LSS implementation”, “Healthcare”, “LSS tools”, “Human factors”, “Expansion of results”, “SME”, “LSS combined with other methodologies” and “Education”. The research outcomes also point out the most significant articles, authors, journals, institutions and countries in LSS literature.
Practical implications
This research contributes to develop the state of the art of LSS and helps professionals as well as researchers to identify which issues new studies should address.
Originality/value
The performance of the literature is measured based on the number of citations and not on the number of published papers, and the bibliometric analysis covers the highest number of articles so far (319 articles). Besides, the identification of the main research trends on LSS is exclusively based on the most recent studies.
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Dong‐Shang Chang and Kuo‐Lung Paul Sun
The purpose of this study is to propose a state‐of‐the‐art new approach to enhance FMEA assessment capabilities.
Abstract
Purpose
The purpose of this study is to propose a state‐of‐the‐art new approach to enhance FMEA assessment capabilities.
Design/methodology/approach
Through data envelopment analysis (DEA) technique and its extension, the proposed approach evolves the current rankings for failure modes by exclusively investigating SOD in lieu of RPN and to furnish improving scales for SOD.
Findings
Through an illustrative example the proposed approach supports the proposition that DEA can not only complement traditional FMEA for improving assessment capability but also, especially, provide corrective information regarding the failure factors – severity, occurrence and detection. Further application of DEA Stratification also reveals that this methodology is useful for managing resource allocation and risk management.
Practical implications
It is shown that the proposed approach enables manager/designers to prevent system or product failures at an early stage of design. Moreover, the approach is able to provide managerial insight of SOD more effectively than justifying the efforts on RPN alone. Projection of each SOD is determined to help managers examine the scale of efforts. Finally, the stratification analysis offers the economical allocation of failure modes with respect to the incurred costs and the efficiency.
Originality/value
The paper proposes a unique new approach, robust, structured and useful in practice, for failure analysis. The methodology, within a firmed methodology, overcomes some of the largely known shortfalls of traditional FMEA: it takes into account multiple criteria and restricted weighted; and it analyses the failure modes' ranking considering not only the direct impacts of failure indices, but also the contribution of these indices.
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Giustina Secundo, Gioconda Mele, Giuseppina Passiante and Angela Ligorio
In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of…
Abstract
Purpose
In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.
Design/methodology/approach
Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.
Findings
Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.
Research limitations/implications
The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.
Originality/value
The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.
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Shruti J. Raval, Ravi Kant and Ravi Shankar
The purpose of this paper is to examine and introduce comprehensive insights into the field of Lean Six Sigma (LSS) by reviewing the existing literature and identifying the…
Abstract
Purpose
The purpose of this paper is to examine and introduce comprehensive insights into the field of Lean Six Sigma (LSS) by reviewing the existing literature and identifying the research gap. The state of LSS research is assessed by critically examining the field, along with a number of dimensions, including time horizon, year, journal and publisher, university, country, author, geographic analysis, research design, research affairs, research methods, tools/techniques used, focus industries, major research area, benefits gained by LSS, critical success factors and barriers of LSS implementation.
Design/methodology/approach
This paper is based on a systematic literature review of 190 articles containing the word LSS in their title, which are published in a well-known database, such as Elsevier ScienceDirect, Taylor and Francis, Emerald Full Text, Springer Link, Wiley InterScience and Inderscience from January 2000 to September 2016.
Findings
This analysis reveals 15 significant dimensions to identify the state of LSS research. Authors find a noticeable rise in the attention of LSS research in the available literature. Major findings show that, the empirical research holds greater credibility. Statistics prove that the case study method scores the highest among all the research methods used in the discipline. The largest number of studies have investigated research issues related to implementation and process of LSS. The LSS uses a wide range of tools/techniques/methodologies: the choice of tools is situation-specific. Manufacturing and health-care sectors have been the focus of LSS research, but LSS has also been adopted by other types of industries. The organizations following LSS have improved bottom-line results, improved company profitability and growth and enhanced customer satisfaction. In general the research is more interpretive in nature; there is still a lack of standard in the LSS implementation framework.
Research limitations/implications
This study is limited to reviewing those articles which contain the word LSS appeared in the title.
Originality/value
This study will help understand the current state of research on LSS, various trends in the field, its applicability and future prospects of investigation in the field.