A machine-vision inspection system for conveying attitudes of columnar objects in packing processes

This paper is a new study on developing a machine vision system for inspecting the conveying attitudes of columnar objects. The presented system consists of image pre-processing, feature extraction, and attitude diagnosis. First of all, in order to segment the objects from the background (namely image pre-processing), an improved maximum between-class variance method is proposed for searching a histogram peak and calculating a threshold value based on the statistics and probability, to solve the problems caused by the non-uniform brightness in a realistic conveyor belt. Then, an open morphological operation is used to eliminate the noise from the binary images produced in the pre-processing step. In the second step (feature extraction), the features of columnar objects are determined by four methods, edge line detecting method, intercepting method, rectangle locating method and feature statistic method. Finally, the diagnosis for the conveying attitudes of columnar objects is based on a hybrid classifier using random forests, and a fuzzy logic. The proposed system is applied to a realistic process for packing industrial explosives. The results of experiments show that the proposed system allows efficient and accurate 100% inspection for the conveying attitude, which ensures the high speed and steady operation of a packing line.     

Impact of pulling direction and magnitude of force exertion on the activation of shoulder muscles

Shoulder musculoskeletal disorders (MSD) are frequently associated with the work activities that demand forceful arm exertions in pushing and pulling directions. Considering the ability of shoulder joint to exert forces in nearly any direction, our understanding of the shoulder muscles activation as affected by pushing and pulling exertions is limited. In this study the activation of seven shoulder muscles were studied for 10 male participants during pulling exertions performed in five directions (pull right, pull left, pull back, pull down and pull up) using three force levels (22.24 N, 33.36 N and 44.48 N). Exertions performed in pulling right and pulling up directions produced higher activation and received higher perceived exertion ratings than the exertions performed in the other directions. Rotator cuff and middle deltoid muscles activation were consistently higher during pulling up and pulling right exertions compared to the other muscles. A high correlation was found between the activation of rotator cuff and deltoid muscles and the perceived exertion ratings. The rotator cuff and middle deltoid muscles activation observed during the pulling up and pulling right exertions can be explained by the concavity compression mechanism which stabilizes the glenohumeral joint of shoulder.Relevance to industryThe muscle activation data expressed in terms of Maximum Voluntary Contraction (MVC) and perceived exertion ratings are widely used by the ergonomic practitioners to design and/or evaluate workplace exertions. This study provides such data for several shoulder muscles during pulling exertions performed under different conditions.     

A note on the Chen correlation of saturated flow boiling heat transfer

The Chen (Ind. Eng. Chem. Process Des. Dev., 1966, 5(3): 322–329) correlation of saturated flow boiling heat transfer is one of the most influential flow boiling heat transfer correlations. It adopted the additive concept and incorporated the Reynolds number factor F and the suppression factor S. Chen presented F and S as the best-fit curves without any parametric equation. However, the parametric equations of F and S were widely used in citing the Chen work, among which some were not accurate, and some had typos in the original sources and then used by others. The objective of this paper is to point out the incorrect and inaccurate equations in the literature. For this purpose, the existing expressions of F and S in the available literature are presented and compared with the Chen best-fit curves. The work provides a reference for the correct use of the Chen correlation.     

Fatigue of 2024-T351 aluminium alloy at different load ratios up to 1010 cycles

Fatigue properties of 2024-T351 aluminium alloy are investigated in the high cycle fatigue (HCF) and very high cycle fatigue (VHCF) regime. Endurance tests are performed with ultrasonic equipment at 20 kHz cycling frequency at load ratios of R = −1, R = 0.1 and R = 0.5 up to 10¹⁰ cycles. Additional servo-hydraulic tests between 8 and 10 Hz at R = 0.1 show no frequency influence on fatigue lifetimes. Linear lines in double logarithmic S–N plots are used to approximate data. Slope exponents of approximation lines increase with increasing numbers of cycles for all load ratios. Failures above 5 × 10⁹ cycles (R = −1 and R = 0.1) or 10¹⁰ cycles (R = 0.5) occur, and no fatigue limit is found. Fatigue cracks leading to failures above 10⁹ cycles are initiated at the surface or slightly below at broken constituent particles or at agglomerations of fractured particles, which are probably Al₇Cu₂(Fe, Mn). Specimens stressed with more than 10¹⁰ cycles at R = −1 without failure show several cracks starting at constituent particles. Maximum crack lengths are 30 μm, which is considerably below grain size.     

Ammonia decomposition kinetics over LiOH-promoted, α-Al2O3-supported Ru catalyst

A LiOH-promoted Ru-based catalyst was recently reported to have a high TOF of 17.7 s⁻¹ at 623 K, compared to 2.7 s⁻¹ for an un-promoted Ru-based catalyst, and has been reproduced for this study to develop further understanding of the catalyst activity under a range of conditions. The kinetic values were calculated using a Temkin-Pyzhev-like power law rate expression model. Reaction orders, pre-exponential factors (A) and activation energies (E) were calculated for two temperature ranges, 623–748 K, and 748–873 K. The TOF of this catalyst at 623 K is not similar to that previously reported, being only 1.6 s⁻¹ in this study. A follow-up CFD analysis supports the fact that the kinetic model effectively describes performance of the catalyst at a range of temperatures and pressures, and can be used in the future on similar catalysts. H₂ partial pressure has an inhibitory effect on the rate of decomposition of NH₃ at all temperatures, not just near or below 673 K as previously proposed in the literature, however equilibrium decomposition is still possible with sufficient catalyst loading.     

Understanding mass and charge transports to create anion-ionomer-free high-performance alkaline direct formate fuel cells

The disadvantage of anion ionomer that possesses low hydroxide conductivity, and thermal and chemical instability hinders the development of the high-performance anion-exchange membrane direct liquid fuel cells. Instead of adding additional base and synthesizing high-conductivity ionomer material, by gaining insight into species transports, herein, we propose an anion-ionomer-free anion-exchange membrane direct formate fuel cell (AEM DFFC). Experimental result reveals that this conceptual anion-ionomer-free AEM DFFC can operate stably within a 6-h constant-current discharge at 10 mA cm⁻², mainly because formate hydrolysis renders a high OH⁻ conductivity. It was also found that the anion-ionomer-free AEM DFFC yields a peak power density as high as 41 mW cm⁻² at 40 °C, 40% higher than that of the conventional quaternary ammonia polysulfone anion-ionomer AEM DFFC. This can be attributed to the fact that the OH⁻-containing formate solution facilitates the mass and charge transports, thereby enlarging the triple-phase boundary for both anodic formate oxidation reaction and cathodic oxygen reduction reaction.     

Reacting flow coupling with thermal impacts in a single solid oxide fuel cell

Thermal impacts are the major concern for the designs of electrolyte of Solid Oxide fuel cells (SOFCs) due to the high temperature operating conditions. In this study, the coupling dynamics of electrochemical reacting flows with heat transfer and generations of thermal strains and stresses (thermal impact) of solid electrolyte and porous electrodes are investigated in a single SOFC by numerical simulations. Modeling results from a test case show that the coupling is necessary as the electrochemical and thermal properties of the cell strongly depends on temperature, meanwhile, the thermal strains and stresses on temperature gradients. The differences in current density and thermal strain gradients predicted by coupling and decoupling simulations are as larger as 20% because of the strong dependents of ionic conductivity of the electrolyte material on temperature, the maximum thermal strain, thermal stresses, and temperature are all about 5%. It is identified that the high operation voltage benefits to the thermal strain, which decreases 20% when the cell operating from 0.5 V–0.7 V.     

Machine learning technology in biodiesel research: A review

Biodiesel has the potential to significantly contribute to making transportation fuels more sustainable. Due to the complexity and nonlinearity of processes for biodiesel production and use, fast and accurate modeling tools are required for their design, optimization, monitoring, and control. Data-driven machine learning (ML) techniques have demonstrated superior predictive capability compared to conventional methods for modeling such highly complex processes. Among the available ML techniques, the artificial neural network (ANN) technology is the most widely used approach in biodiesel research. The ANN approach is a computational learning method that mimics the human brain's neurological processing ability to map input-output relationships of ill-defined systems. Given its high generalization capacity, ANN has gained popularity in dealing with complex nonlinear real-world engineering and scientific problems. This paper is devoted to thoroughly reviewing and critically discussing various ML technology applications, with a particular focus on ANN, to solve function approximation, optimization, monitoring, and control problems in biodiesel research. Moreover, the advantages and disadvantages of using ML technology in biodiesel research are highlighted to direct future R&D efforts in this domain. ML technology has generally been used in biodiesel research for modeling (trans)esterification processes, physico-chemical characteristics of biodiesel, and biodiesel-fueled internal combustion engines. The primary purpose of introducing ML technology to the biodiesel industry has been to monitor and control biodiesel systems in real-time; however, these issues have rarely been explored in the literature. Therefore, future studies appear to be directed towards the use of ML techniques for real-time process monitoring and control of biodiesel systems to enhance production efficiency, economic viability, and environmental sustainability.