The selection of manufacturing processes for a given application is a complex problem of multicriteria decision-making although there have been several different approaches that can be utilized to select a suitable alternative. However, identifying appropriate multicriteria decision-making approach from the list of available methods for a given application is a difficult task. This work suggests a methodology to assess different selection approaches, which are the technique for order of preference by similarity to ideal solution (TOPSIS), analytic hierarchy process (AHP), and VIKOR: stepwise procedure. This valuation was done depending on the following factors: number of alternative processes and criteria, agility through the process of decision-making, computational complexity, adequacy in supporting a group decision, and addition or removal of a criterion. A case study in this study was presented to analyse the evaluation methodology. The criteria used to evaluate and identify the best manufacturing process were categorized into productivity, accuracy, complexity, flexibility, material utilization, quality, and operation cost. Five manufacturing processes were considered, including gravity die casting, investment casting, pressure die casting, sand casting, and additive manufacturing. The results showed that each approach was suitable for the problems of manufacturing process selection, in particular toward the support of group decision-making and uncertainty modelling. Manufacturing processes were ranked based on their respective weights for AHP, TOPSIS, and VIKOR, and sand casting is the best. In terms of computational complexity, the VIKOR method performed better than TOPSIS and AHP. Moreover, the VIKOR and TOPSIS methods were better convenient to the selection of manufacturing processes for agility during the process of decision-making, the number of alternative processes and criteria, adequacy in supporting a group decision, and addition or removal of a criterion.

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.

Single-point incremental forming is an innovative flexible and inexpensive technique to form sheet products when prototypes or small batches are required. The process allows complex geometries to be produced using a computer numerical control machine, eliminating the need for a special die. This study reports on the effects of four important single-point incremental forming process parameters on produced surface profile accuracies. The profile accuracy was estimated by measuring the side angle errors and surface roughness and also waviness and circularity of the product inner surface. Full factorial design of experiments was used to plan the study, and the analysis of variance was used to analyze and interpret the results. The results indicate that the tool diameter (d), step depth (s), and sheet thickness (t) have significant effects on the produced profile accuracy, while the feed rate (f) is not significant. As a general rule, thin sheets with greater tool diameters yielded the best surface quality. The results also show that controlling all surface quality features is complex because of the contradicting effects of, and interactions between, a number of the process parameters.

PurposeThis study provides a unique integrated diagnosis system to investigate the causes of low productivity, profitability, machinery health conditions and wear severity of medium-size biscuit industry assets in Taiz, Yemen.Design/methodology/approachThe evaluation is based on an integrating of the overall equipment effectiveness (OEE) and oil-based maintenance (OBM) approaches. The data are collected using the company's operational records, interviews and observations, while the used lubricating oil samples are also collected from production lines' machineries. Scanning electron microscope (SEM) is used to study the wear debris particle features and wear mechanism. Different other analysis tools such as fishbone, 5 whys and Pareto charts are also used to investigate the root causes and plausible recovery solutions of machinery failures.FindingsThis study demonstrated that a large proportion of machinery failures and production loss are of management concerns. Also, this study inferred that the analysis of wear debris is unique and informative for determining machinery wear severity and useful life. Finally, the current conditions of production lines are clarified and suggestions to use a mixed preventive/predictive maintenance management approach are also elucidated.Originality/valueThis work implemented an integrated OEE/OBM diagnostic maintenance system to investigate the root causes of low productivity and machine failures in real production lines and suggested robust decisions on the maintenance duties.

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.