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This chapter explores the adaptation and evolution of stand-alone CSR reporting in two different political economies and late-capitalist countries: Brazil and South Korea. Instead of selecting between new institutionalism and the varieties of capitalism (VOC) approach, this study attempts to explore how the interaction between converging and diverging pressures appears in the adaptation and evolution of stand-alone CSR reporting (i.e., cross-fertilization process) in two countries.

Using qualitative content analysis this study focuses on the frameworks of CSR reports and the way CSR issues are described within the stand-alone CSR reports of four telecommunication companies in Brazil and South Korea.

Even though CSR reports in both countries have become similar due to the convergence of frameworks of CSR reporting, the key themes and the representation on each theme are still embedded within each form of market economy: a hierarchical market economy (HME) in Brazil and a network market economy (NME) in South Korea. From a cross-fertilization perspective, this chapter shows that the adaptation and evolution of CSR reporting occurs at two different levels of CSR reporting.

This study has three major values. First, it explains the two different levels of the adaptation and evolution process of CSR reporting by bringing a dynamic cross-fertilization view. Second, it provides a qualitative study that focuses on the content of CSR disclosures instead of the quantity of those disclosures. Lastly, it contributes to the academic and practical research on CSR in late-capitalist countries and in two under-researched types of political economies.

Part quality inspection is playing a critical role in the metal additive manufacturing (AM) industry. It produces a part quality analysis report which can be adopted to further improve the overall part quality. However, the part quality inspection process puts heavy reliance on the engineer’s background and experience. This manual process suffers from both low efficiency and potential errors and, therefore, cannot meet the requirement of real-time detection. The purpose of this paper is to look into a deep neural network, Convolutional Neural Network (CNN), towards a robust method for online monitoring of AM parts.

The proposed online monitoring method relies on a deep CNN that takes a real metal AM part’s images as inputs and the part quality categories as network outputs. The authors validate the efficacy of the proposed methodology by recognizing the “beautiful-weld” category from material CoCrMo top surface images. The images of “beautiful-weld” parts that show even hatch lines and appropriate overlaps indicate a good quality of an AM part.

The classification accuracy of the developed method using limited information of a small local block of an image is 82 per cent. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

A real-world data set of high resolution images of ASTM F75 I CoCrMo-based three-dimensional printed parts (Top surface images with magnification 63×) annotated with categories labels. Development of a CNN-based classification model for the supervised learning task of recognizing a “beautiful-weld” AM parts. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

The purpose of this paper is to outline some key aspects such as material systems used, phenomenological and statistical process modeling, techniques applied to monitor the process and optimization approaches reported. All these need to be taken into account for the ongoing development of the SLM technique, particularly in health care applications. The outcomes from this review allow not only to summarize the main features of the process but also to collect a considerable amount of investigation effort so far achieved by the researcher community.

This paper reviews four significant areas of the selective laser melting (SLM) process of metallic systems within the scope of medical devices as follows: established and novel materials used, process modeling, process tracking and quality evaluation, and finally, the attempts for optimizing some process features such as surface roughness, porosity and mechanical properties. All the consulted literature has been highly detailed and discussed to understand the current and existing research gaps.

With this review, there is a prevailing need for further investigation on copper alloys, particularly when conformal cooling, antibacterial and antiviral properties are sought after. Moreover, artificial intelligence techniques for modeling and optimizing the SLM process parameters are still at a poor application level in this field. Furthermore, plenty of research work needs to be done to improve the existent online monitoring techniques.

This review is limited only to the materials, models, monitoring methods, and optimization approaches reported on the SLM process for metallic systems, particularly those found in the health care arena.

SLM is a widely used metal additive manufacturing process due to the possibility of elaborating complex and customized tridimensional parts or components. It is corroborated that SLM produces minimal amounts of waste and enables optimal designs that allow considerable environmental advantages and promotes sustainability.

The key perspectives about the applications of novel materials in the field of medicine are proposed.

The investigations about SLM contain an increasing amount of knowledge, motivated by the growing interest of the scientific community in this relatively young manufacturing process. This study can be seen as a compilation of relevant researches and findings in the field of the metal printing process.

The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process.

In this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples.

The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error.

This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts.

The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation.

This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

This paper aims to study multiobjective genetic algorithm ability in determining the process parameter and postprocess condition that leads to maximum relative density (RD) and minimum surface roughness (Ra) simultaneously in the case of a Ti6Al4V sample process by laser beam powder bed fusion.

In this research, the nondominated sorting genetic algorithm II is used to achieve situations that correspond to the highest RD and the lowest Ra together.

The results show that several situations cause achieving the best RD and optimum Ra. According to the Pareto frontal diagram, there are several choices in a close neighborhood, so that the best setup conditions found to be 102–105 watt for laser power followed by scanning speed of 623–630 mm/s, hatch space of 76–73 µm, scanning patter angle of 35°–45° and heat treatment temperature of 638–640°C.

Suitable selection of process parameters and postprocessing treatments lead to a significant reduction in time and cost.

The purpose of this paper is to fabricate acrylonitrile-butadiene-styrene (ABS)/high impact polystyrene (HIPS) based multi-material geometries using a low cost polymer printer. At the same time, efforts have been made to investigate the mechanical characteristics of the obtained prints and to perform the optimization using the Taguchi-Grey (TGRA) method.

Initially, the feedstock materials were in-house fabricated in the form of filament wires, workable with fused filament fabrication (FFF) technique, of 1.75 ± 0.1 mm diameter by using a single screw extruder. Multi-material structures were fabricated using variable parameters (such as: raster angles, layer height, fill density and solid layers) and the experimentation was conducted as per Taguchi L18 array. Mechanical responses obtained by performing tensile, impact and bending test were studied in response to input variables and ultimately optimized settings were obtained, for individual as well as multiple parameters). Scanning electron microscopy (SEM) analysis was performed to analyze the fractured surfaces.

The Signal/Noise (S/N) plots for the quality characteristics highlighted that selected input parameters significantly influenced the obtained values for tensile strength, impact strength and flexural strength. Micrographs of the fractured specimens showed the occurrence of brittle fracture with higher levels of perimeter, infill density and solid layers. The extent of delamination was also increased under the bending load and further increased by increasing solid layers.

The results of the study strongly advocated the utility of fabricated multi-materials structures in automotive, aerospace and other manufacturing industries.

This work represents the fabrication, testing and analysis of polymer-based multi-material structures for engineering applications.

The purpose of this study is to investigate the impact of innovation management capability on organisational performance. Based on the resource-advantage theory, this study addresses: “To what extent do intellectual and emotional assets influence marketing management capability which loads to the organisation’s performance?”

To understand the research objectives, the data was collected via 35 in-depth interviews with managers and academics from various multi-national companies and new empirical insights were offered.

This study recognised three components of intellectual and emotional assets (knowledge and competence; digital technology; and reputation) and their influences on business performance.

The focus on small- and medium-sized enterprises (SMEs) limits the generalisation of this study. To scrutinise the relations documented in this study, future research should be conducted in other country settings and different sector.

This study contributes to the sustainability literature by developing a conceptual model that explains the development and role of innovation management in a market context with its associated sustainability management outcomes. The results are of importance to both SMEs and policymakers. Clear need to investigate further how organisations can benefit from such capabilities for greater growth is identified.

In recent years, additive manufacturing (AM) technology has been acknowledged as an efficient method for producing geometrical complex objects with a wide range of applications. However, dimensional inaccuracies and presence of defects hinder the broad adaption of AM procedures. These factors arouse concerns regarding the quality of the products produced with AM and the utilization of quality control (QC) techniques constitutes a must to further support this emerging technology. This paper aims to assist researchers to obtain a clear sight of what are the trends and what has been inspected so far concerning non-destructive testing (NDT) QC methods in AM.

In this paper, a survey on research advances on non-destructive QC procedures used in AM technology has been conducted. The paper is organized as follows: Section 2 discusses the existing NDT methods applied for the examination of the feedstock material, i.e. incoming quality control (IQC). Section 3 outlines the inspection methods for in situ QC, while Section 4 presents the methods of NDT applied after the manufacturing process i.e. outgoing QC methods. In Section 5, statistical QC methods used in AM technologies are documented. Future trends and challenges are included in Section 6 and conclusions are drawn in Section 7.

The primary scope of the study is to present the available and reliable NDT methods applied in every AM technology and all stages of the process. Most of the developed techniques so far are concentrated mainly in the inspection of the manufactured part during and post the AM process, compared to prior to the procedure. Moreover, material extrusion, direct energy deposition and powder bed processes are the focal points of the research in NDT methods applied in AM.

This literature review paper is the first to collect the latest and the most compatible techniques to evaluate the quality of parts produced by the main AM processes prior, during and after the manufacturing procedure.