Search Results (10)

In recent years, the touch panel industry has experienced rapid growth. With technological maturation and progressive cost reduction, touch technology has been widely adopted in human–machine interfaces. Currently, touch panels are predominantly employed in smartphones and tablet devices, and the industry is increasingly pursuing thinner, lighter designs, driving the development of diverse touch technologies, including one-glass solution (OGS), on-cell, and in-cell architectures. To enhance competitive advantage within the touch panel industry, it is essential to improve production efficiency and elevate product quality; consequently, yield has become a critical metric for evaluating industrial competitiveness. This study adopts the electrical test yield of Touch-on-Lens (TOL) touch-sensing glass as the primary performance indicator. A Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework is applied to systematically address impedance-related quality defects occurring during manufacturing. First, key quality characteristics (KQCs) of the TOL touch-sensing glass process are rigorously defined. Subsequently, measurement system analysis (MSA) and process capability assessment are conducted. Next, the Taguchi method is employed to identify the most influential process factors affecting electrical test yield. Finally, response surface methodology (RSM) is utilized to determine the optimal combination of process parameter settings that maximize electrical test yield. Results from the empirical case study demonstrate that the electrical test yield improved significantly—from 90.2% to 93.6%. This outcome validates that the integrated application of the Six Sigma DMAIC methodology, combined with the Taguchi method and RSM, effectively enhances the electrical test yield of TOL sensing glass. The proposed approach offers a robust, data-driven improvement framework applicable to touch panel manufacturers seeking to optimize sensing-glass fabrication processes—thereby supporting broader industry efforts to improve product quality and reduce manufacturing costs. Full article

In industrial environments, providing intuitive spatial information via 3D maps is essential for maximizing the efficiency of teleoperation. However, existing SLAM algorithms generating 3D maps predominantly focus on improving robot localization accuracy, often neglecting the optimization of viewability required for human operators to clearly perceive object depth and structure in virtual environments. To address this, this study proposes a methodology to optimize the viewability of RTAB-Map-based 3D maps using the Taguchi method, aiming to enhance VR teleoperation efficiency and reduce cognitive workload. We identified eight key parameters that critically affect visual quality and utilized an L18 orthogonal array to derive an optimal combination that controls point cloud density and noise levels. Experimental results from a target object picking task demonstrated that the optimized 3D map reduced task completion time by approximately 9 s compared to the RGB image condition, achieving efficiency levels approaching those of the physical-world baseline. Furthermore, evaluations using NASA-TLX confirmed that intuitive visual feedback minimized situational awareness errors and substantially alleviated cognitive workload. This study suggests a new direction for constructing high-efficiency teleoperation interfaces from a Human–Robot Interaction perspective by expanding SLAM optimization criteria from geometric precision to user-centric visual quality. Full article

Additive manufacturing has promising potential for the development of 3D-printed protective structures such as stab-resistant body armor. However, no research to date has examined the impact of 3D printing parameters on the protective performance of such 3D-printed structures manufactured using fused filament fabrication technology. This study, therefore, investigates the effects of five key printing parameters: layer thickness, print speed, print temperature, infill density (Id), and layer width, on the mechanical and protective performance of 3D-printed polycarbonate (PC) armor. A Taguchi L₂₇ matrix was employed to systematically analyze these parameters, with toughness, stab penetration depth, and armor panel weight as the primary responses. ANOVA results, along with the Taguchi approach, demonstrated that Id was the most influential factor across all print parameters. This is because a higher Id led to denser structures, reduced voids and porosities, and enhanced energy absorption, significantly increasing toughness while reducing penetration depth. Morphological analysis supported the statistical findings regarding the role of Id on the performance of such structures. With optimized printing parameters, no penetration to the armor panels was recorded, outperforming the UK body armor standard of a maximum permitted knife penetration depth of 8 mm. Moreover, an artificial neural network (ANN) utilizing the 5-14-12-3 topology was created to predict the toughness, stab penetration depth, and armor panel weight of 3D-printed armors. The ANN model demonstrated better prediction performance for stab penetration depth compared to the Taguchi method, confirming the successful application of such an approach. These findings provide a critical foundation for the development of high-performance 3D-printed protective structures. Full article

Linear antenna arrays exhibit radiation patterns that are restricted to a half-space and feature axial radiation, which can be a significant drawback for applications that require omnidirectional coverage. To address this limitation, the synthesis method utilizing the Taguchi approach, originally designed for linear arrays, can be effectively extended to two-dimensional or planar antenna arrays. In the context of a linear array, the synthesis process primarily involves determining the feeding law and/or the spatial distribution of the elements along a single axis. Conversely, for a planar array, the synthesis becomes more complex, as it requires the identification of the complex weighting of the feed and/or the spatial distribution of sources across a two-dimensional plane. This adaptation to planar arrays is facilitated by substituting the direction $\theta$ with the pair of directions $(\theta ,\varphi )$, allowing for a more comprehensive coverage of the angular domain. This article focuses on exploring various configurations of planar arrays, aiming to enhance their performance. The primary objective of these configurations is often to minimize the levels of secondary lobes and/or array lobes while enabling a full sweep of the angular space. Secondary lobes can significantly impede system performance, particularly in multibeam applications, where they restrict the minimum distance for frequency channel reuse. This restriction is critical, as it affects the overall efficiency and effectiveness of communication systems that rely on precise beamforming and frequency allocation. By investigating alternative planar array designs and their synthesis methods, this research seeks to provide solutions that improve coverage, reduce interference from secondary lobes, and ultimately enhance the functionality of antennas in diverse applications, including telecommunications, radar systems, and wireless communication. Full article

The production of cement, an essential material in civil engineering, requires a substantial energy input, with a significant portion of this energy consumed during the grinding stage. This study addresses the gap in the literature concerning the collective impact of key parameters, including ball size, feed rate, and mill speed, on grinding efficiency. Nine spherical balls, ranging from 15–65 mm, were utilized in six distinct distributions, alongside varying feed rates and mill speeds. ANOVA, Taguchi, and regression analyses were employed to explore their influence on grinding efficiency and cement properties. The findings revealed that ball size variation significantly affects grinding performance, with smaller diameter balls yielding higher efficiency due to increased abrasion and fine formation. Conversely, elevating mill speed generally diminishes grinding efficiency, particularly at speeds approaching 90% of the critical speed, impacting ball shoulder and foot angles. Moreover, increasing the feed rate affects the grinding performance differently based on ball distribution, with finer distributions experiencing adverse effects. Signal-to-noise ratios facilitated determining the optimal control factor levels to minimize energy consumption. Quadratic regression models exhibited strong predictive capabilities for energy consumption in grinding. Ultimately, the optimal grinding performance was achieved with Bond-type ball distribution No. 6, considering ball size, mill speed, and feed-rate interactions, albeit with considerations regarding grinding time and energy efficiency. Full article

With the continuous growth of industrialization, the presence of heavy metals (HMs) in the environment has become a critical issue, necessitating cost-effective and efficient techniques for their removal. The present study aimed to determine the optimal preparation conditions for synthesizing pectin (PC) as a polymer sorbent, combined with Magnesium (Mg) Aluminum (Al) layered double oxides (LDOs), using a fast and facile co-precipitation method. Both the response surface method (RSM) and the Taguchi method were employed to optimize the influence of key independent variables, including the molar ratio of cations Mg:Al, the ratio of pectin to LDO, and the temperature for removing multiple elements from wastewater. The results indicated that RSM is more accurate and examines more interactions, while Taguchi reduces the number of tests and is more economical than RSM. However, both statistical methods showed good potential for predicting the adsorption capacity (Qe) of HMs. The optimal preparation conditions were identified as a molar ratio of 3:1, a ratio of pectin to LDO of 7% w/w, and a temperature of approximately 600 °C. In conclusion, the application of RSM and Taguchi approaches was found to be feasible and effective in optimizing the preparation conditions of modified LDO, which can be utilized as a potential adsorbent for removing multiple elements from wastewater. Full article

This research focuses on a comprehensive exploration of the experimental and mechanical aspects of the electrical discharge machining (EDM) process, specifically targeting the machining characteristics of AA2014/Si₃N₄/Mg/cenosphere hybrid composites. The aim is to optimize the process parameters for enhanced machining performance through a combination of testing, optimization, and modelling methodologies. The study examines the effects of key EDM variables—peak current, pulse on time, and pulse off time—on critical output responses: surface roughness (Ra), electrode wear rate (EWR), and material removal rate (MRR). Leveraging an L9 Taguchi orthogonal array experimental design, the impact of controllable factors on these responses is analysed. An integrated approach utilizing MATLAB’s logic toolbox and Mamdani’s technique is employed to model the EDM process, and a multiple-response performance index is calculated using fuzzy logic theory, enabling multiobjective optimizations. Furthermore, a mechanical behaviour evaluation of AA2014/Si₃N₄/Mg/cenosphere hybrid composites is performed through mechanical testing, with a comparison between experimental machining results and predicted values. Scanning electron microscopy (SEM) images reveal the presence of filler reinforcements within the base alloy, displaying an improved microstructure and uniform reinforcement dispersion. An X-ray diffraction (XRD) analysis confirms the major elemental constituents—aluminium, silicon, and magnesium—in the hybrid composites. A microstructural analysis of the hybrid metal matrix composites (MMCs) prepared for EDM showcases closely packed reinforcement structures, circular ash-coloured spots indicating silicon and nitrates, and a fine dispersion of cenosphere reinforcement particles. The study’s outcomes demonstrate a promising application potential for these hybrid composites in various fields. Full article

This study was designed to examine the effects of a trimethylolpropane trioleate (TMPTO)-based lubricant on thrust force and torque under the high-speed drilling of Al-6061 as an effective environmentally friendly cutting fluid. The tribological performance of three lubricant blends was evaluated based on ASTM standards. TMPTO base oil, notably enhances load-carrying capacity under extreme pressure conditions, with a seizer load of 7848 N. The best-performing oil was further optimized using a Taguchi-based design experiment to investigate the effect of different additive concentrations on thrust force and torque under actual contact conditions. Experiments were conducted using three critical machining parameters: additive concentration, spindle speed, and feed rate. The results of the ANOVA analysis reveal that spindle speed contributes most substantially (62.99%) to torque, with feed rate (23.72%) and additive concentration (7.74%) also showing significant impacts. On the other hand, thrust force is primarily influenced by feed rate (73.52%), followed by spindle speed (16.82%), and additive concentration (6.28%). Furthermore, a machine learning model was developed to predict and compare a few significant aspects of high-speed drilling machinability, including thrust force and torque. Three different error metrics were utilized in order to assess the performance of the predicted values, namely the coefficient of determination (R2), mean absolute percentage error (MAPE) and mean square error (MSE), which are all based on the coefficient of determination. Compared to other models, decision tree produces more accurate prediction values for cutting forces. The present study provides a novel approach for evaluating the most promising biodegradable lube oils and predicting cutting forces by formulating a perfect blend. Full article

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. Full article

Industries demand stringent requirements towards economical machining without hindering the surface quality while cutting high carbon high chromium (HcHcr) steel. Electrical discharge machining (EDM) of HcHcr steel aims at reducing machining cost (i.e., maximize material removal rate (MRR) and minimize tool wear rate (TWR)) with good surface quality (i.e., minimize surface roughness (SR)). A comparative study was carried out on EDM of HcHcr D2 steel (DIN EN ISO 4957) by applying Taguchi L₁₈ experimental design considering different electrode materials (copper, graphite, and brass), dielectric fluids (distilled water and kerosene), peak current, and pulse-on-time. The process performances were analyzed with respect to material removal rate, surface roughness, and tool wear rate. Pareto analysis of variance was employed to estimate the significance of the process variables and their optimal levels for achieving lower SR and TWR and higher MRR. Hybrid Taguchi-CRITIC-Utility and Taguchi-PCA-Utility methods were implemented to determine the optimal EDM parameters. Higher MRR of 0.0632 g/min and lower SR of 1.68 µm and TWR of 0.012 g/min was attained by graphite electrode in presence of distilled water as dielectric fluid compared to the brass and copper. Additionally, a metallographic analysis was carried out to study the surface integrity on the machined surfaces. Micrographic analysis of the optimal conditions showed lower surface roughness and fewer imperfections (lesser impression, waviness surface, and micro-cracks) compared to worst conditions. Full article