Reliability of the microelectronic interconnection materials for electronic packages has a significant impact on the fatigue properties of the electronic assemblies. This is due to the correlation between solder joints reliability and the most frequent failure modes seen in electronic devices. Due to their superior mechanical and fatigue properties, SAC alloys have supplanted Pb-solder alloys as one of the most commonly used solder materials used as interconnection joints on electronic packages. The main aim of this study is to develop a prediction model of the fatigue life of the solder joints as a function of the experimental conditions. Using a customized experimental setup, an accelerated fatigue shear test is applied to examine the fatigue life of the individual SAC305 solder joints at actual setting conditions. OSP surface finish and solder mask defined are used in the studied test vehicle. The fatigue test includes three levels of stress amplitude and four levels of testing temperature. A two-parameter Weibull distribution is used for the reliability analysis for the fatigue life of the solder joints. A stress–strain curve is plotted for each cycle to construct the hysteresis loop at each cyclic load and testing temperature. The acquired hysteresis loop is used to estimate the inelastic work per cycle and plastic strain. The Morrow energy and Coffin Manson models are employed to describe the effects of the fatigue properties on the fatigue life of the solder joints. The Arrhenius model is implemented to illustrate the evolutions in the stress life, Morrow, and Coffin Manson equations at various testing temperatures. The fatigue life of SAC305 solder joints is then predicted using a general reliability model as a function of the stress amplitude and testing temperature.

Frequent failures in interconnected materials in microelectronic packages play a vital role in determining the reliability of electronic devices. Differences in the thermal expansion coefficients of the solder joint, printed circuit board (PCB), and electronic package are a major source of applied stress on solder joints. Due to health concerns and governmental regulations associated with using leaded solder alloys, SAC solder alloys are commonly used as an alternative to Pb-alloys because of their outstanding solderability and reliability. The shear fatigue creep accelerated test at different load levels (16 MPa, 20 MPa, and 24 MPa) was implemented in this study by using a universal micro-testing machine to investigate solder joint reliability. The creep effects were demonstrated by employing 60 seconds of dwell time for each alternating stress value and comparing its impact on solder joint reliability with the fatigue test results. The accelerated test was applied to SAC305 (96.5% Sn, 3% Ag, 0.5% Cu) solder joints with OSP surface finish at different testing temperatures (25°C, 60°C, and 100°C). Seven replicates were considered as a sample size for the reliability analysis. A two-parameter Weibull distribution was employed as the dominant distribution to describe the fatigue behavior at each condition. The characteristic life and shape parameters at each condition were extracted from the obtained Weibull models. The stress–life equation was used to estimate the reliability performance of the solder joints against fluctuations in stress amplitude at differing testing temperatures. The inelastic work per cycle and plastic strain were calculated from the acquired hysteresis loop under each condition. Coffin–Manson and Morrow energy models illustrate the relationships between fatigue life versus plastic strain and inelastic work, respectively. The effects of the oscillated testing temperature on the stress–life model, Morrow energy model, and Coffin–Manson model were elaborated using the Arrhenius equation. The results indicated a severe degradation of fatigue life with increased testing temperature. Raising the stress amplitude had a less significant impact on fatigue life reduction than increasing the testing temperature. The reliability prediction models, as a function of the fatigue properties and testing temperature, were derived from the Arrhenius, Coffin–Manson, and Morrow energy models. The acquired prediction equations produced acceptable goodness-of-fit values. Finally, a general reliability model with a 99% adequacy value (R ² ) was constructed as a function of testing temperature and stress amplitude.

Aluminum alloys use is so profound in many applications such as transportation, construction, energy, defense applications, automotive, aerospace, to name a few. Therefore, investigating the mechanical and electrical properties of the different types for aluminum alloys is vital. The Al–Mg alloy is widely used in the automotive industry because of its optimal properties, for example, corrosion resistance, weldability, and strength-to-weight ratio. This study aims to investigate and model the effect of changing the magnesium percentage content on the hardness and electrical conductivity of Al–Mg alloy with a detailed statistical analysis to validate the results. The microstructure at each Mg percentile content is demonstrated using a scanning electron microscope (SEM) and an optical microscope for validating the Mg content after solidification and tracking the grain size evolutions at different Mg percentiles. Vickers hardness testing is applied for hardness evaluation at each experimental condition. The electrical conductivity is tested using a PCE 20COM electric conductive non-destructive test (NDT). Prediction models are constructed to estimate the hardness and electrical conductivity as a function of Mg percentile by using a nonlinear optimizer. The results indicated that the hardness is significantly increased with increasing the Mg content by three times when comparing 0 Mg wt.% and 5 mg wt.%. In contrast, a 48% reduction in the electrical conductivity is found when Mg wt.% is increased to 5 and a notable decrease in the grain size is observed when the Mg content is increased.

This paper investigates the views of healthcare researchers and professionals on the implementation of the Quality Management System (QMS) approach using a 5-point Likert scale survey. Researchers and healthcare professionals who observed or participated in QMS implementation were surveyed. Multiple channels, including occupational societies, social networking, i.e., LinkedIn, hospital’s directories, award recipients, academic researchers, and professional connections, made it possible to reach this particular sample. Participants were surveyed using a series of questions with a total of 56 questions. The survey was administrated through the web portal of Qualtrics and then analyzed both on Qualtrics and SPSS software packages. Descriptive Statistics, Exploratory Factor Analysis (EFA), and Linear Regression were employed to draw conclusions. The final sample group consisted of 71 participants representing a range of healthcare settings. EFA was conducted, producing a model of 10 emergent factors and an outcome for total improvement. Regression modeling revealed the Critical Success Factors (CSFs) and the interaction between emergent factors. The results indicated that QMS Implementation Culture, Structure, and Managerial Training are critical to the QMS implementation success. This research helps quality professionals enhance their ability to prioritize elements affecting the successful implementation of the QMS.

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.