Novel Technique for Enhancing the Strength of Friction Stir Spot Welds through Dynamic Welding Parameters

Presently, friction stir spot welding (FSSW) has become a common alternative for spot welding technologies. Over the years, researchers have implemented various methods for enhancing weld strength. However, the literature shows that the previously reported approaches have used static (constant) welding parameters set at the beginning of the welding stroke (i.e., the FSSW parameters were kept constant during the welding stroke). In contrast, in this study, an innovative technique is proposed for enhancing the weld strength for Al 1050 material by adjusting the FSSW process parameters during the welding stroke. Two FSSW parameters, namely, feed rate and spindle speed (dynamic parameters), are used in this study with a stepwise variation function and are changed during the welding stroke. The results of this study show that the weld tensile strength is enhanced by 12–21% when using the proposed novel dynamic welding parameter technique. This is a significant increase in the weld strength compared to when static welding parameters are employed during the welding stroke

Peri-Implant bone response around porous-surface dental implants: A preclinical meta-analysis

Introduction: This meta-analysis of relevant animal studies was conducted to assess whether the use of porous-surface implants improves osseointegration compared to the use of non-porous-surface implants. Material and methods: An electronic search of PubMed (MEDLINE) resulted in the selection of ten animal studies (out of 865 publications) for characterization and quality assessment. Risk of bias assessment indicated poor reporting for the majority of studies. The results for bone-implant contact (BIC%) and peri-implant bone formation (BF%) were extracted from the eligible studies and used for the meta-analysis. Data for porous-surface implants were compared to those for non-porous-surface implants, which were considered as the controls. Results: The random-effects meta-analysis showed that the use of porous-surface implants did not significantly increase overall BIC% (mean difference or MD: 3.63%; 95% confidence interval or 95% CI: −1.66 to 8.91; p = 0.18), whereas it significantly increased overall BF% (MD: 5.43%; CI: 2.20 to 8.67; p = 0.001), as compared to the controls. Conclusion: Porous-surface implants promote osseointegration with increase in BF%. However, their use shows no significant effect on BIC%. Further preclinical and clinical investigations are required to find conclusive evidence on the effect of porous-surface implants

Simulation-Based Optimization of a Two-Echelon Continuous Review Inventory Model with Lot Size-Dependent Lead Time

This study analyzes a stochastic continuous review inventory system (Q,r) using a simulation-based optimization model. The lead time depends on lot size, unit production time, setup time, and a shop floor factor that represents moving, waiting, and lot size inspection times. A simulation-based model is proposed for optimizing order quantity (Q) and reorder point (r) that minimize the total inventory costs (holding, backlogging, and ordering costs) in a two-echelon supply chain, which consists of two identical retailers, a distributor, and a supplier. The simulation model is created with Arena software and validated using an analytical model. The model is interfaced with the OptQuest optimization tool, which is embedded in the Arena software, to search for the least cost lot sizes and reorder points. The proposed model is designed for general demand distributions that are too complex to be solved analytically. Hence, for the first time, the present study considers the stochastic inventory continuous review policy (Q,r) in a two-echelon supply chain system with lot size-dependent lead time L(Q). An experimental study is conducted, and results are provided to assess the developed model. Results show that the optimized Q and r for different distributions of daily demand are not the same even if the associated total inventory costs are close to each other

Analyzing Critical Failures in a Production Process: Is Industrial IoT the Solution?

Machine failures cause adverse impact on operational efficiency of any manufacturing concern. Identification of such critical failures and examining their associations with other process parameters pose a challenge in a traditional manufacturing environment. This research study focuses on the analysis of critical failures and their associated interaction effects which are affecting the production activities. To improve the fault detection process more accurately and efficiently, a conceptual model towards a smart factory data analytics using cyber physical systems (CPS) and Industrial Internet of Things (IIoTs) is proposed. The research methodology is based on a fact-driven statistical approach. Unlike other published work, this study has investigated the statistical relationships among different critical failures (factors) and their associated causes (cause of failures) which occurred due to material deficiency, production organization, and planning. A real business case is presented and the results which cause significant failure are illustrated. In addition, the proposed smart factory model will enable any manufacturing concern to predict critical failures in a production process and provide a real-time process monitoring. The proposed model will enable creating an intelligent predictive failure control system which can be integrated with production devices to create an ambient intelligence environment and thus will provide a solution for a smart manufacturing process of the future

A Multi-Criteria Decision Framework Considering Different Levels of Decision-Maker Involvement to Reconfigure Manufacturing Systems

Reconfigurable Manufacturing Systems (RMSs) rely on a set of technologies to quickly adapt the manufacturing system capacity and/or functionality to meet unexpected disturbances, such as fluctuation/uncertainty of demand and/or unavailability/unreliability of resources. At the operational stage, such disturbances raise new production requirements and risks, which call upon Decision-Makers (DMs) to analyze the opportunity to move from a running configuration to another more competitive one. Such a decision is generally based on an evaluation of a multitude of criteria, and several multi-criteria decision-making (MCDM) approaches have been suggested to help DMs with the reconfiguration process. Most existing MCDM approaches require some assignment of weights to the criteria, which is not a trivial task. Unfortunately, existing studies on MCDM for an RMS have not provided guidelines to weigh the evaluation criteria. This article fills in this gap by offering a framework to set up such weights. We provide a comprehensive set of quantitative indicators to evaluate the reconfiguration decisions during the operation of the RMS. We suggest three weighting methods that are convenient to different levels of DM expertise and desired degree of involvement in the reconfiguration process. These weighting methods are based on (1) intuitive weighting, (2) revised Simos procedural weighting combined with the Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS), and (3) DM independent weighting using ELECTRE IV. The implementation of the suggested framework and a comparison of the suggested methods carried out on an industrial case study are described herein

Effect of Ambient Oxygen Content, Safety Shoe Type, and Lifting Frequency on Subject’s MAWL and Physiological Responses

Objective: The purpose of this study was to evaluate the lifting capabilities of individuals in hypoxia when they wear different types of safety shoes and to investigate the behavior of the physiological responses induced by the lifting process associated with those variables. Methods: An experimental design was used, based on two sessions. The first was training and acclimatization session, then an experimental lifting phase. A total of ten male students of King Saud University were recruited in the study. A four-way repeated measures design, with four independent variables and six dependent variables, was used in this research. The independent variables that were studied in the experimental lifting phase were: ambient oxygen content (15%, 18%, and 21%), safety shoes type (light-duty, medium-duty, and heavy-duty), lifting frequency (1 and 4 lifts/min), and replication (first and second trials). The dependent variables were also: maximum acceptable weights lifting using the psychophysical technique, heart rate (HR), electromyography (EMG) of (biceps brachii, trapezius, anterior deltoid, and erector spinae), safety shoes discomfort rating, rating of perceived exertion, and ambient oxygen discomfort rating. Results: The maximum acceptable weights lifting that were selected by participants at lower levels of the independent variables (ambient oxygen content 21%, lifting frequency 1 lift/min, and first replication) were significantly higher than at high levels of the independent variables (ambient oxygen content 15%, lifting frequency 4 lift/min, and second replication). Several interaction effects were also significant. Conclusions: It provides evidence that the ambient oxygen content increases the intensity of workload in lifting tasks. It showed that oxygen content affects the psychophysical selection of maximum acceptable weights lifting and the physiological responses represented in muscular activities and heart rate. It suggests that ambient oxygen content must be considered along with the type of safety shoes worn when the lifting task at altitudes occurs

Modified Harmony Search Algorithm for Resource-Constrained Parallel Machine Scheduling Problem with Release Dates and Sequence-Dependent Setup Times

This research focuses on the problem of scheduling a set of jobs on unrelated parallel machines subject to release dates, sequence-dependent setup times, and additional renewable resource constraints. The objective is to minimize the maximum completion time (makespan). To optimize the problem, a modified harmony search (MHS) algorithm was proposed. The parameters of MHS are regulated using full factorial analysis. The MHS algorithm is examined, evaluated, and compared to the best methods known in the literature. Four algorithms were represented from similar works in the literature. A benchmark instance has been established to test the sensitivity and behavior of the problem parameters of the different algorithms. The computational results of the MHS algorithm were compared with those of other metaheuristics. The competitive performance of the developed algorithm is verified, and it was shown to provide a 42% better solution than the others

Effect of Cycling on a Stationary Bike While Performing Assembly Tasks on Human Physiology and Performance Parameters

Objective: This study evaluated participants’ ability to assemble a computer keyboard while at a cycling workstation. Depending on task completion time, error percentage, and workload based on subjective workload ratings, subjective body discomfort, electroencephalography (EEG) and electrocardiographic (ECG) signals, human performances were compared at four different cycling conditions: no cycling, low level cycling (15 km/h), preferred level cycling, and high level cycling (25 km/h). Method: The experiment consisted of 16 participants. Each participant performed the test four times (each cycling condition) on different days. Results: The repeated measure test showed that the alpha and beta EEG signals were high during session times (post) when compared with session times (pre). Moreover, the mean interbeat (R-R) interval decreased after the participants performed the assembly while pedaling, possibly due to the physical effort of cycling. Conclusions: Pedaling had no significant effect on body discomfort ratings, task errors, or completion time

Erratum: Analyzing critical failures in a production process: Is industrial IoT the solution? (Wireless Communications and Mobile Computing (2018) 2018 (6951318) DOI: 10.1155/2018/6951318)

In the article titled "Analyzing Critical Failures in a Production Process: Is Industrial IoT the Solution?" [1], Dr. Moath Alatefi and Dr. Ammar Moohialdin were missing from the authors' list. Dr. AmmarMoohialdin conceived the idea, provided the data, and revised and commented on an earlier draft of the article. In addition, Dr. Moath Alatefi worked as a statistical consultant during the preparation and the revision of the article. The corrected authors' list is shown above. The acknowledgments section to be as: "The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. (RG- 1438-089)".</p

Analyzing critical failures in a production process: Is industrial IoT the solution?

Machine failures cause adverse impact on operational efficiency of any manufacturing concern. Identification of such critical failures and examining their associations with other process parameters pose a challenge in a traditional manufacturing environment. This research study focuses on the analysis of critical failures and their associated interaction effects which are affecting the production activities. To improve the fault detection process more accurately and efficiently, a conceptual model towards a smart factory data analytics using cyber physical systems (CPS) and Industrial Internet of Things (IIoTs) is proposed. The research methodology is based on a fact-driven statistical approach. Unlike other published work, this study has investigated the statistical relationships among different critical failures (factors) and their associated causes (cause of failures) which occurred due to material deficiency, production organization, and planning. A real business case is presented and the results which cause significant failure are illustrated. In addition, the proposed smart factory model will enable any manufacturing concern to predict critical failures in a production process and provide a real-time process monitoring. The proposed model will enable creating an intelligent predictive failure control system which can be integrated with production devices to create an ambient intelligence environment and thus will provide a solution for a smart manufacturing process of the future