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Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.

This study aims to outline the current challenges in ultrasonic additive manufacturing (AM). AM has revolutionized manufacturing and offers possible solutions when conventional techniques reach technological boundaries. Ultrasonic additive manufacturing (UAM) uses mechanical vibrations to join similar or dissimilar metals in three-dimensional assemblies. This hybrid fabrication method got attention due to minimum scrap and near-net-shape products.

This paper reviews significant UAM areas in process parameters such as pressure force, amplitude, weld speed and temperature. These process parameters used in different studies by researchers are compared and presented in tabular form. UAM process improvements and understanding of microstructures have been reported. This review paper also enlightens current challenges in the UAM process, process improvement methods such as heat treatment methods, foil-to-foil overlap and sonotrode surface roughness to increase the bond quality of welded parts.

Results showed that UAM could solve various problems and produce net shape products. It is concluded that process parameters such as pressure, weld speed, amplitude and temperature greatly influence weld quality by UAM. Post-weld heat treatment methods have been recommended to optimize the mechanical strength of ultrasonically welded joints process parameters. It has been found that the tension force is vital for the deformation of the pre-machined structures and for the elongation of the foil during UAM bonding. It is recommended to critically investigate the mechanical properties of welded parts with standard test procedures.

This study compiles relevant research and findings in UAM. The recent progress in UAM is presented in terms of material type, process parameters and process improvement, along with key findings of the particular investigation. The original contribution of this paper is to identify the research gaps in the process parameters of ultrasonic consolidation.

For metal additive manufacturing, metallic powders are usually produced by vacuum induction gas atomization (VIGA) through the breakup of liquid metal into tiny droplets by gas jets. VIGA is considered a cost-effective technique to prepare feedstock. In VIGA, the quality and the morphology of the produced particles are mainly controlled by the gas pressure used during powder production, keeping the setup configuration constant.

In VIGA process for metallic additive manufacturing feedstock preparation, the quality and morphology of the powder particles are mainly controlled by the gas pressure used during powder production.

In this study, Inconel-625 feedstock was produced using a supersonic nozzle in a close-coupled gas atomization apparatus. Powder size distribution (PSD) was studied by varying the gas pressure.

The nonmonotonic but deterministic relationships were observed between gas pressure and PSD. It was found that the maximum 15–45 µm percentage PSD, equivalent to 84%, was achieved at 29 bar Argon gas pressure, which is suitable for the LPBF process. Following on, the produced powder particles were used to print tensile test specimens via LPBF along XY- and ZX-orientations by using laser power = 475 W, laser scanning speed = 800 mm/s, powder layer thickness = 50 µm and hatch distance = 100 µm. The yield and tensile strengths were 9.45% and 13% higher than the ZX direction, while the samples printed in ZX direction resulted in 26.79% more elongation compared to XY-orientation.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

The presence of heavy metals in milk causes many acute and chronic physiological dysfunctions in human organs. The present study aims to investigate the heavy metals in cow's and buffalo's milk of two major cities, Karachi and Gujranwala, Pakistan to estimate metal intake by humans from this source.

In total, 48 milk samples from 2 cities were drawn from animals' udder to avoid contamination. Each sample was digested with nitric acid at 105 ^oC (degree Celsius) on a pre-heated electric hot plate to investigate the metals by atomic absorption spectroscopy (flame type). Air-acetylene technique analyzed chromium, cadmium and lead, and the hydride method analyzed arsenic in the milk samples.

The results revealed the highest mean lead concentration (19.65 ± 43.86 ppb) in the milk samples, followed by chromium (2.10 ± 2.33 ppb) and arsenic (0.48 ± 0.73 ppb). Cadmium was not detected in any sample, assuming cadmium's occurrence was below the detection level. The concentrations of all the metals in the samples of the two cities do not differ statistically. Lead concentrations in the buffalo's milk were higher than in cow's milk (p < 0.05). However, the concentrations of arsenic and chromium between buffalo's and cow's milk do not differ statistically. The present study reveals a lower level of metals in the milk than those conducted elsewhere. The mean concentrations of all the metals met the World Health Organization's (WHO) safety guidelines (1993).

Although cadmium causes toxicity in the human body, cadmium could not be measured because cadmium's concentration was below the detection level, which is 1 ppb.

This study will help reduce the toxic metals in our environment, and the sources of heavy metals, particularly from the industrial sector could be identified. The feed and water consumed by the milking animals could be carefully used for feeding them.

This study will help reduce the diseases and malfunction of human organs and organ systems since these heavy metals cause toxicity and carcinogenicity in humans. Arsenic and chromium cause cancer while lead causes encephalopathy (a brain disease).

The study reports heavy metal concentrations in the two attributes of four independent variables of raw milk samples that were scarcely reported from Pakistan.

The purpose of this study is to investigate the post–enterprise resource planning (ERP) issues and challenges in the context of Saudi Arabia. There is a lack of research in the context of developing countries regarding post-ERP implementation issues and challenges. The high failure rate of the ERP system is a reflection of many management issues that occurred at different phases of ERP implementation. Previous research indicated that even after a successful implementation, the ERP system was unable to sustain itself in the organizations.

An exploratory case study design was adopted to proceed with this research process at two organizations in Saudi Arabia. The interviews of the top and middle management are conducted and transcribed. These case studies were further analyzed using the Creswell approach to generate several themes, and descriptions provided a deeper understanding of the post ERP implementation issues and challenges.

Research findings show that for successful ERP implementation, identified factors are top management support, integration, strategy, employee resistance, BPR, change management, vendor selection, team formation and culture. Moreover, factors for the post-ERP implementation that led to sustainability are top management support, training, system adoption, system testing, data migration, cost overrun, employee retention and post-implementation support.

This study is unique in its type to examine the issues and challenges organizations face after deploying ERP initiatives. This research's findings were useful and supportive for the senior management interested in successfully sustaining such an initiative in the organization.

The purpose of this paper is to explore the types of Shariah capabilities of Islamic banks, which provide bases for the types of Shariah value propositions, offered by the relationship managers in their front-line service experiences with the customers.

A resource-based view of Shariah capabilities and a service-dominant logic view of value propositions are adopted. Fifteen relationship managers from multiple Islamic banks in Pakistan are interviewed to find a typology of Shariah capabilities and a resultant typology of value propositions for Islamic banks.

The findings suggest that Islamic banks claim to possess five types of Shariah capabilities, namely, Shariah governance capability (SGC), Shariah compliance capability (SCC), Shariah monitoring capability (SMC), Shariah structuring (product) capability (SSC) and Shariah learning capability (SLC). These capabilities lead to four types of values propositions, namely, Shariah identity value (SIV), Riba-free value (RFV), Shariah disclosure value (SDV) and Tangibility value (TV) of the real assets in transactions.

The study has relied on the front-line experiences of relationship managers who are connected to the Islamic banks’ capabilities inside the banks and the value propositions that they offer to show relationships with customers in front-line service experiences. Other stakeholders may have different perspectives on both capabilities and value propositions.

This paper contributes to Islamic finance theory by theoretically and empirically showing two typologies for the Islamic banks' capabilities and value propositions, respectively.

Supervisor phubbing is an increasingly common behavior depicted by supervisors despite the significance of supervisor–subordinate interactions. This study explores the impact of this behavior on workplace incivility and workplace presenteeism and analyzes the mediating role of self-esteem and the moderating role of power distance.

Data were collected from employees belonging to IT sector. The research was cross-sectional in nature and the data were collected using a structured questionnaire.

The findings indicate a significant impact of supervisor phubbing on self-esteem, workplace incivility and workplace presenteeism. Additionally, results reveal the mediating role of self-esteem between supervisor phubbing and workplace presenteeism. However, the moderating effect of power distance on the relationship between supervisor phubbing and self-esteem was not supported.

The research contributes to the existing literature and theory, especially in the area of supervisor phubbing and communications management. The study suggests a need for comprehensive approach that involves both organizational policies and individual behavior change.

This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature.

To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models.

The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable.

The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows.

The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context.

This paper assesses the environment of legitimacy by determining the role of institutional quality and policy uncertainty on the performance of five major South Asian stock markets (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using 21 years data from 2000 to 2020. The focus of this study is to approach the issue of the environment of legitimacy that leads to sustained market returns.

Panel cointegration tests of Kao and Pedroni are applied, and the Dynamic Panel Vector Autoregressive (PVAR) model is used to determine the estimates.

ADF P-Values of both Kao and Pedroni tests show that the panels are cointegrated; the statistical significance of the results of the Kao and Pedroni panel cointegration test confirms cointegration among the variables. After determining the most appropriate lag, the analysis is done using PVAR. The results indicate that institutional quality, policy uncertainty, and GDP positively affect stock market return. Meanwhile, government actions and inflation negatively affect stock market returns. On the other hand, stock market return positively affects institutional quality, government action, policy uncertainty, and GDP. While stock market return negatively affects inflation.

The sample is taken only from a limited number of South Asian countries, and the period is also limited to 21 years.

Based on our research findings, we have identified several policy implications recommended to enhance and sustain the performance of stock markets.

This paper uses a unique analytical tool, which gives a better insight into the problem. The value of this work lies in its findings, which also have practical implications and theoretical significance.