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A product's design influences its manufacturing process and the associated costs. Consequently, design engineers need to review their designs from a manufacturing perspective. While ‘Design For Manufacturing’ (DFM) tools often identify the manufacturing problems associated with a design, they would be more effective if these problems could be represented to the designer in terms of a cost value. This research developed a cost estimation tool for the designer in the surface mount printed circuit board (PCB) domain by integrating computer aided design, computer aided process planning (CAPP) and cost estimation techniques using a knowledge based framework. The cost estimation can be done in two design stages. First, an initial approximation of the manufacturing cost can be obtained using information such as the component mix, type of substrate and the size of board. After the detailed design of the PCB has been developed, a more accurate PCB assembly cost can be obtained using computer aided design (CAD) data. Both cost determination strategies would require the generation of a macro‐process plan. The cost advisor considers tangible and intangible factors. This cost advisor and the DFM environment have been developed using C ++ and object oriented programming constructs under the MS Windows operating system.

Surface mount technology (SMT) is being increasingly used in printed circuit board (PCB) assembly. The reduced lead pitch of surface mount components coupled with their increased lead count and packing densities have made it imperative that automated placement methods be used. However, the SMT placement process is often a bottleneck in surface mount manufacturing. A reduction in placement time in SMT will enhance throughput and productivity. This paper describes the design and development of a prototype expert system based approach which identifies ‘near’ optimal placement sequences for surface mount PCBs in (almost) realtime. The software structure used integrates a knowledge based system with an optimisation module. PROLOG is the language used in this research. The system was rigorously validated and tested. Ideas for further research are also presented.

The objective of this research is to address issues that relate to the assembly of Sn/Ag/Cu bumped flip chips.

Flip chips bumped with Sn/Ag/Cu bumps were assembled onto different lead‐free surface finishes at lead‐free soldering temperatures. Sensitivity to fluxes, reflow profiles, pad finishes and pad designs were all investigated and the potential consequences for assembly yields were calculated numerically.

Soldering defects, such as incomplete wetting and collapse and poor self‐centring were observed in the assemblies. Defect levels were sensitive to contact pad metallurgy and flux type, but not to flux level and reflow profile within the ranges considered. Owing to a particularly robust substrate‐pad design, defects observed in this work were limited to incomplete wetting and collapse, as well as poor self‐centering.

The scope of this work is limited to the lead‐free fluxes available at the time of research. A switch to lead‐free solder alloys in flip chip assemblies raises concerns with respect to the compatibilities of materials and the quality of soldering that is achievable. While this may be less of an issue in the case of larger area array components, such as ball grid arrays and chip scale packages, it is more of a concern for applications that use flip chips due to the smaller size of the solder spheres. Assembly yields tend to become more sensitive to the reduced collapse of the joints. More work is essential to investigate the potential benefits of more active lead‐free fluxes, both no‐clean tacky and liquid fluxes, in reducing or eliminating soldering defects.

The paper offers insights into assembly issues with Sn/Ag/Cu bumped flip chips.

This paper aims to focus on integrating a lean framework in a high-mix-low-volume (HMLV) printed circuit board assembly (PCBA) environment to enhance current assembly processes and facility layouts. An HMLV PCBA environment is characterized by stochastic demands, a variety of products in terms of shapes and sizes and different sequences of assembly and test operations, in addition to long cycle times and high fall-out rates.

Preliminary analysis indicates that the push inventory control system led to the longer cycle times, such that various lean methodologies have been applied to enhance the assembly operations. In this research, Kanban sizes for different assembly lines are also estimated to integrate and implement a “pull-system” into the lean framework. In addition, material movement and facility layout have been studied to minimize work-in-process travel time. An “iterative-MAIC” approach has been applied to implement lean principles.

As a result, a lean manufacturing pilot line has been implemented to evaluate the effectiveness of the lean principles before rolling them out across the manufacturing floor. It has been shown that the cycle times of the pilot line products are decreased by 40 per cent and the number of defects decreased by 10-30 per cent, depending on different assembly processes, after the lean implementation.

There is limited literature that addresses lean transformation in an HMLV electronics manufacturing service provider handling several product types with different testing methodologies, frequent product revision changes and higher fall-out rates. Hence, in this research, lean manufacturing has been implemented in an HMLV PCBA environment, which has the challenges of varying demand with a mix of assembly and test operations for different product families.

The leaching of lead from electronic components in landfills to ground water is harmful to health and to the environment. Increasing concern over the use of lead in electronics manufacturing has led to legislation to restrict its use as a joining material. Consequently, significant recent research efforts have been geared to identification of suitable lead‐free solder pastes. Typically, lead‐free solder pastes contain a very active flux in an effort to improve wetting. These aggressive fluxes have the tendency to explode (or burst) and create flux spatter, causing many process problems with sensitive electronic components. The purpose of this paper is to propose solution procedures to minimize/eliminate these flux spatters, particularly, on gold fingers in memory modules when lead‐free solder pastes are used.

Four no‐clean, lead‐free Sn‐Ag‐Cu (SAC) alloy‐based solder pastes consisting of four different flux systems from three different vendors were evaluated. Two types of reflow profiles (linear and ramp‐soak‐ramp) were also evaluated. Experiments were also conducted to optimise the soak temperature and soak time to determine a broader process window for lead‐free volume production with minimal flux spatter on the contact fingers of memory modules. In order to validate our findings the recommended profile and paste was adopted in production. Additional experiments on a board with a different surface finish were also carried out to validate the recommendations.

Flux spatter can be reduced/eliminated through proper selection of flux chemistry and reflow profile optimisation. The experimental study conducted indicates there is a reduction in the occurrence of flux spatter when a ramp‐soak‐ramp profile is used with lead‐free solder pastes.

Demonstrates that flux spatter can be reduced/eliminated by carefully choosing a soak profile and appropriate flux chemistry.

Predicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance. However, there is a lack of existing social entrepreneurship models to predict social enterprises' social impacts. This paper aims to propose the social impact prediction model for social entrepreneurs using a data analytic approach.

This study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model.

The accurate result of the developed social impact prediction model is based on the list of identified social impact prediction variables that have been evaluated by social entrepreneurship experts. Based on the three algorithms' implementation of the model, the results showed that naive Bayes is the best performance classifier for social impact prediction accuracy.

Although there are three categories of social entrepreneurship impact, this research only focuses on social impact. There will be a bright future of social entrepreneurship if the research can focus on all three social entrepreneurship categories. Future research in this area could look beyond these three categories of social entrepreneurship, so the prediction of social impact will be broader. The prospective researcher also can look beyond the difference and similarities of economic, social impacts and environmental impacts and study the overall perspective on those impacts.

This paper fulfills the need for the Malaysian social entrepreneurship blueprint to design the social impact in social entrepreneurship. There are none of the prediction models that can be used in predicting social impact in Malaysia. This study also contributes to social entrepreneur researchers, as the new social impact prediction variables found can be used in predicting social impact in social entrepreneurship in the future, which may lead to the significance of the prediction performance.

The last decade witnessed the increasing use of Surface Mount Technology (SMT) in the assembly of Printed Circuit Boards (PCBs). This fact coupled with increasing component densities, decreasing component pitch, and the need for reliable PCBs has focused attention on the critical components of the manufacturing process including the adhesive used. Components used in PCBs manufactured using SMT, which are to be wave soldered, must be adhesively attached to the board so that they remain in place during the wave soldering process. Utilisation of non‐conducting adhesives for the attachment of electronic components prior to wave soldering has become common throughout the electronics manufacturing industry. The knowledge based adhesive selection adviser described in this paper assists the user (process engineer) in the selection of adhesives for wave soldering of surface mount components. In addition to guiding the user through the adhesive selection process, it provides dispenser related information. This ‘advice’ is derived from the restrictions imposed by the user (including the user's facility temperature), material property requirements, productivity measures and the adhesive dispensing method. The adhesive selection adviser helps the user understand the relationships that need to be considered during adhesive selection. It is applicable to any existing SMT line that uses adhesives within the commercial PCB manufacturing domain.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

This paper aims to study the anti-diabetes activity of the Kombucha prepared from different snake fruit cultivars.

The juices of snake fruits of Suwaru, Madura, Pondoh and Bali cultivars were fermented for 14 days. Anti-diabetes activity of the products was analyzed. Twenty-four male albino Wistar rats were used and randomly divided into six experimental groups, i.e. four groups of the diabetic rats treated with the Kombucha, plus the normal group and diabetic control group. The Kombucha were orally administered to the streptozotocin induced-diabetic rats at 5 mL/kg body weight per day during the 28-day experiment. The fasting plasma glucose (FPG), oxidative stress indices (superoxide dismutase [SOD] activity and Malondialdehyde [MDA] level) and lipid profile of the blood plasma were measured. The pancreas was used for immunohistochemical study and β-cells quantification. Data were analysed by ANOVA followed by Fisher test using Minitab version 16.0.

FPG of the diabetic rats treated with the Kombucha (110.3-189.3 mg/dL) was significantly lower (p = 0.000) than the diabetic control group (413.3 mg/dL). Those were in line with the number of pancreatic β-cells of 42.1 in diabetic rats that lower (p = 006) than those in treated the diabetic rats (61.2-73.5). The treated diabetic rats had lower oxidative stress (SOD activity: 20.9-44.6 unit/100 µL with p = 0.000; MDA level: 0.37-0.48 ng/100 µL with p = 0.000) than those in the diabetic rats (SOD activity: 18.7 unit/100µL; MDA level: 0.84 ng/100 µL). The treated diabetic rats also showed better lipid profile than those in the diabetic control rats. There were cultivar differences, and the Suwaru and Madura snake fruit Kombucha demonstrated the most potential for diabetes management.

This is the first study on in vivo anti-diabetes activity of snake fruit Kombucha prepared from different snake fruit cultivars.

At the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.

In this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.

This study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.

As per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.