Industrial Tasks Research Articles

Evolution of red ceramic pigments: from hazardous compounds to environmentally friendly alternatives

Synthesis of red ceramic pigments is a challenging task in the ceramic industry. Most classic reds are based on severely toxic materials including lead, arsenic, mercury, selenium, and cadmium, which are forbidden in many countries. On the other hand, the red color is super sensitive to the synthesis parameters, heat treatment conditions (atmosphere and temperature), particle size, etc. Therefore, achieving a bright true red shade and its stability at high temperatures is crucial. There has been a massive attempt to find a sustainable high-temperature resistant alternative for these hazardous compounds. Iron oxide is one of the first red pigments in history, but it cannot produce a bright red shade and its color is mostly red-brown. Ce2S3 is another red pigment with a beautiful red color. But it cannot stand the temperature above 350 °C in an oxidizing atmosphere. Doping lanthanides in the perovskites or entrapping the toxic beautiful chromophores in the core-shell structures are among the strategies to achieve safe bright red pigments. This review outlines the recent progress of hazardous classic reds to environmentally friendly ceramic red pigments. Various compounds and dopants, applied to develop sustainable reds, from simple iron-oxides to composites, solid solutions, core-shell structures, or even purified wastes have been covered in this review.

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects.

The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (1.767°, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (2.771°, 153.584 mm) to (1.034°, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication.

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine.

Identifying and diagnosing faults is a critical task in process industries to maintain effective monitoring of process and plant safety. Minimizing process downtime is critical for enhancing the quality of the product and minimizing production costs. Real-time categorization of issues across several levels is essential for the monitoring of processes. However, there are still notable obstacles, that must be addressed, such as the existence of robust correlations, the complexity of the data, and the lack of linearity. This study introduces a novel fault identification technique in batch reactor experimental trials that employs multikernel support vector machines (SVMs) to categorize internal and external issues, specifically reactor temperature, coolant temperature, and jacket temperature. The data set was obtained from empirical research. The classification has been conducted using a multikernel SVM. This article identified that the nonlinear classifier using the radial bias function results in an accuracy that is at least 22.08% superior to other methods.

A portable inflatable soft wearable robot to assist the shoulder during industrial work.

Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions using soft materials and active controls. We present the design and evaluation of a portable inflatable shoulder wearable robot for assisting industrial workers during shoulder-elevated tasks. The robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units, and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback.

Design and Manufacturing Using 3D Printing Technology of A 5-DOF Manipulator for Industrial Tasks

Robotic manipulators have become very necessary in industrial applications all over the world. In this paper, a 5-DOF robotic manipulator is designed and manufactured to simulate a real industrial task. The manipulator is intended to transfer an object with a weight of 30 grams from a known place to another known one, which is a pick and place task. Firstly, all parts of the manipulator are designed using SolidWorks software. During the design, all parts’ dimensions are considered. The end-effector of the manipulator is designed based on gear system. Secondly, 3D printing technology is used to manufacture these designed parts. The manufacturing process is very accurate and efficient. Servo motors are considered to do the motion of the manipulator, which are easily and directly connected to the control circuit. As, 5-DOF manipulator is manufactured, five servo motors are used: one motor for every joint. The motion of the motors is controlled by Arduino Uno unit which is a cheap and easy programming unit. Experiments are executed with the developed robot to show its effectiveness and success by preparing three boxes which the robot effectively transfers from one place to another. Eventually, the challenges during the design and manufacturing of this robot are mentioned in this paper.

A lightweight transformer based on feature fusion and global–local parallel stacked self-activation unit for bearing fault diagnosis

Due to the complex environment and limited hardware resources in the industrial practice diagnosis tasks, deploying deep learning-based models with large parameters is challenging. A novel lightweight bearing fault diagnosis method, global–local parallel transformer (GLP-Transformer), is proposed for balanced diagnostic performance within resource constraints. In this end-to-end framework, a multi-channel vibration feature fusion embedding block is designed, which extracts multi-position sensor signals to acquire richer original features. Furthermore, a multi-layer network structure with alternately stacked global–local parallel self-activation unit is presented for fault feature mapping processing at low costs. This unit integrates the parameter efficiency of convolutional operation and the global feature extraction expertise of transformer. Experimental verification is performed on publicly available and self-built data platforms. Compared with other methods, GLP-Transformer significantly reduces the requirements for storage and computational resources (Params: 48.28K, FLOPs: 2.74M) while possessing advanced generalization ability and robustness.

Comprehensive Method for Estimating the Time and Expenditures Required for Mine Liquidation Processes of Business Processes

Abstract The European Green Deal (EU Green Deal) has set the direction for the EU’s energy transition towards climate neutrality by 2050. In Poland, this means moving away from the extraction and use of coal. The Social Contract for the Mining Industry signed in 2021 states the necessity of last mine closure by 2049. Mine closure is a complex, lengthy and costly process. A complex scientific solution may concern the use of rational operations and minimization of mine closure costs. This article presents a system for the elementary assessment of the potential time and cost of coal mine liquidation. Estimating coal mine closure costs in the early design phase is an key aspect of supporting the company dealing with mine closure. The aim of the research was to improve the tool for assessing mine liquidation price. The extended assessment solution proposed in the article is formed on base of statistics of past mining institution liquidation processes. This method can, with minor modifications, be used for each restructuring and revitalizing task for mining industries in the process of liquidation. At the core of the developed method is a preliminary data analysis, which should be confirmed by a complex and multi-criteria estimation of the costs of the planned mine liquidation.

Estimation of SARA composition of crudes purely from density and viscosity using machine learning based models

Accurate characterization of crude oils by determining the composition of saturates, aromatics, resins and asphaltenes (SARA) has always been a challenging task in the petroleum industry. However, conventional experimental methods for determination of SARA composition are labour intensive, time-consuming and expensive. In the present study, artificial neural network (ANN) models were developed to predict the SARA composition from easily measurable parameters like density and viscosity. A dataset of 216 crude oil samples covering wide range of geographical locations was compiled from various literature sources. The ANN models with one hidden layer and six neurons are trained, tested and validated using MATLAB neural network toolbox. Results obtained on analysis revealed reasonably good accuracy of prediction of SARA components except for aromatics. The performance of developed ANN models was compared with various correlations reported in literature and found to be better in terms of mean squared error and coefficient of determination. The developed models hence provide a cost-effective and time-efficient alternative to the conventional SARA characterization techniques.

Selective conversion of ethylene to propylene over rhenium-containing heterogeneous catalysts

The development of new technologies for effective propylene production is a highly important scientific and industrial task nowadays. In this study, the conversion of ethylene to propylene (ETP reaction) was explored over rhenium-containing catalysts supported on a ZrO2 carrier at temperatures 100°C–200°C and atmospheric pressure using a flow catalytic reactor with a fixed catalyst bed without the use of any other hydrocarbons or dilutants. The effect of rhenium presence in the catalysts on the activity and propylene selectivity was examined.

Multipurpose Autonomous Navigation Robot (M.A.N.R)

Abstract: The "Multipurpose Navigation Robot" project proposes the development of a highly versatile and adaptive robotic system capable of seamlessly navigating through diverse real-world environments such as restaurants, hospitals, and warehouses. The project centers around a modular design, featuring a flexible base chassis responsible for efficient and obstacle-free movement across various terrains and settings. The core innovation lies in the incorporation of easily attachable and detachable modules, enabling the robot to cater to specific tasks in different industries.

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

Predicting New Graduate Nurses’ Retention during Transition Using Decision Tree Methods: A Longitudinal Study

Background. Although retaining new nurses is imperative for the future of the nursing profession, it remains a challenging task in the healthcare industry. Understanding the career journey of new graduates as they transition from students to nurses is vital. However, longitudinal studies investigating the factors influencing retention during this period are lacking. Aim. The aim of this study is to identify the influencing factors and develop a longitudinal prediction model for new graduate nurse retention. Methods. A secondary data analysis was conducted using the New Nurse e-Cohort Study dataset from two survey periods, November–December 2020 and February–March 2022. The participants were categorized into either retention or turnover groups based on their turnover experiences. A decision tree based on classification and regression tree (CART) analysis was utilized. Results. Of the total 586 participants, 463 (79%) were in the retention group. The CART model highlighted that new nurses’ retention was significantly associated with younger age, higher readiness for practice (clinical problem-solving) during the nursing program, lower transition shock (such as confusion in professional values, loss of social support, and conflicts between theory and practice), and a higher person-environment fit (person-job fit). The predictive accuracy of the CART model was 79.7%. Conclusion. To retain new nurses, nursing educators and hospital managers should collaborate to prepare nursing students for actual practice, offer support during organizational socialization, and foster healthy professional values for competence in the workplace. Implications for Nursing Management. Transforming the educational strategies of nursing programs and hospital management policies is imperative to ultimately enhance the retention of new graduate nurses.

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios.

A Comprehensive Review on Low-Cost MEMS Accelerometers for Vibration Measurement: Types, Novel Designs, Performance Evaluation, and Applications

Maintenance of mechanical elements is considered to be the most crucial task in industry and affects the economics, predominantly. There are many techniques available for fault diagnosis and condition monitoring of rotatory machine elements and one of the popular tools is vibration monitoring. Analyzing vibrations would be useful in supervising equipment/machine components in an attempt to detect the significant change which is the cause of growing failures or faults. Broadly speaking, it comes under prognostics and health monitoring (PHM) and prevents unexpected failure and costly repair. Proficient predictive monitoring and on-board recognition of failures is a need of the Fourth Industrial Revolution. Several types of vibration analyzers and sensors are commercially available, however, their total budget is not affordable. This motivates reviewing the suitability of low-cost sensors for vibration monitoring. An inclusive review of a variety of applications wherein microelectromechanical system (MEMS)-based accelerometer is used is presented in this paper. This would support an open-design movement for creating open.

ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition.

Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu. This paper presents a transformer-based method called ET-Network that integrates self-attention into EfficientNet for feature extraction and a transformer for language modeling. The use of self-attention layers in EfficientNet helps to extract global and local features that capture long-range dependencies. These features proceeded into a vanilla transformer to generate text, and a prefix beam search is used for the finest outcome. NUST-UHWR, UPTI2.0, and MMU-OCR-21 are three datasets used to train and test the ET Network for a handwritten Urdu script. The ET-Network improved the character error rate by 4% and the word error rate by 1.55%, while establishing a new state-of-the-art character error rate of 5.27% and a word error rate of 19.09% for Urdu handwritten text.

Occupational tasks associated with shoulder pain and upper extremity disability: a cross-sectional study in the Johnston County Osteoarthritis Project

BackgroundShoulder pain is a leading cause of disability. Occupations requiring high upper extremity demands may put workers at greater risk of shoulder injury and resulting pain. We examined associations of occupation with shoulder pain and upper extremity disability in the Johnston County Osteoarthritis Project.MethodsWork industry and occupational tasks for the longest job held were collected from participants. At follow-up ranging from 4–10 years later, participants were asked about shoulder symptoms (pain, aching, or stiffness occurring most days of 1 month in the last year) and given a 9-item, modified Disabilities Arm Shoulder and Hand (DASH) questionnaire to categorize disability from 0–4 (none-worst). Logistic regression and cumulative logit regression models were used to estimate associations with prevalent shoulder symptoms and with worse disability category, respectively. Models were adjusted for cohort, age, sex, race, education and time to follow-up. Sex- and race-stratified associations were evaluated.ResultsAmong 1560 included participants, mean age was 62 years (standard deviation ± 9 years); 32% were men, and 31% were Black. Compared to the managerial/professional industry, higher odds of both shoulder symptoms and worse upper extremity disability were seen for most industrial groups with physically demanding jobs, particularly the service industry. Work that often or always required lifting/moving > 10 lbs. was associated with higher odds of shoulder symptoms. Work that sometimes or always required heavy work while standing was associated with higher odds of shoulder symptoms, and this association was stronger among men and White workers.ConclusionPhysically demanding occupations were associated with increased occurrence of shoulder pain and disability. Mitigating specific physical work demands may reduce shoulder-related disability.

Quicker Path planning of a collaborative dual-arm robot using Modified BP-RRT* algorithm

Path-planning of an industrial robot is an important task to reduce the overall operation time. In industrial tasks, path planning is executed with lead-through programming, where in most cases the robot faces singulated object configurations. Cluttered environments demand path-planning algorithms, which are sensor driven, rather than pre-programmed. Path-planning algorithms, like RRT, and RRT* and their variants have inherent problems like the duration of a search and the creation of several node samples which may lead to longer path lengths. Back Propagation-Rapidly exploring Random Tree* (BP-RRT*) algorithm was a leap forward when an obstacle is enveloped with a sphere. Due to the spherical envelope of the obstacle, this method evaluates the connection between the path and obstacle in space with a spherical envelope using the triangular function and identifies the non-collision path in 3D space. This predicts the best non-collision path in the 3D workspace. The current state-of-the-art of BP-RRT* is limited to single-arm robots. A collaborative dual-arm robot faces more problems in path planning than a single-arm robot like inter-collision of manipulator arms apart from avoiding obstacles. A Modified BP-RRT* algorithm is proposed for the dual-arm collaborative robot has a pre-stage partition of grids that makes the computation faster, efficient, and collision-free compared to the traditional path planning algorithms namely RRT, RRT*, Improved RRT* and BP-RRT*. The algorithm is implemented in simulation as well as in physical implementation for ABB YuMi dual-arm collaborative robot and the typical length of the path of the proposed modified BP-RRT* method has reduced by 53.8% from the traditional RRT method, 6.95% from the RRT* method, 7.77% from improved RRT* method and 6.83% from the BP-RRT* method. Also, the average time to grasp has reduced by 17.84%, the typical duration for search has decreased by 33.45%, the number of node samples created has reduced by 14.79% from BP-RRT* algorithm.

Movement and binding control strategy based on a new type of rebar-binding robot

PurposeThe paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.Design/methodology/approachThe robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.FindingsBy moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.Originality/valueThe innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

Characterization of the spin arc welding technique for dissimilar materials Hastelloy C-2000 and C-276

Hastelloy C-2000 and C-276 are primarily employed in the petrochemical and pharmaceutical industries. Finding an effective way to combine these alloys is still a complex task in industry. The main focus of this study is to investigate the potential efficacy of the spin arc welding process on Hastelloy C-2000 and C-276. The welding process was carried out under various conditions, such as weld current, spinning speed, and spin diameter. ERNiCrMo-4, with a diameter of 1.2 mm, was employed as a filler. Optical microscopy, XRD and FESEM were used for microstructural examination. The mechanical properties of the welded samples were critically analyzed. The results of the ultimate tensile strength (UTS), hardness value (HV), and impact tests of spin arc welding were compared with those of the multipass pulsed current gas tungsten arc welding process (MPCGTAW). In spin arc welding, Hastelloy C-2000 and C-276 exhibited higher UTS, HV, and toughness compared to MPCGTAW because there was no secondary TCP phase formation in the weld centre and hot cracking in spin arc weldments. Additionally, compared to MPCGTAW, the elongation with spin arc welding was noted to be lower. Moreover, microstructural analysis revealed no evidence of grain segregation, which may have contributed to the improved UTS and HV of the materials during spin arc welding. Thus, spin arc welding proves to be superior for dissimilar Hastelloy joints of C-2000 and C-276.

Consistency evaluation of multidimensional quality attributes of Atractylodis Macrocephalae Rhizoma by fusion of multi-source information

The quality control of Chinese medicinal decoction pieces is one of the key tasks in the traditional Chinese medicine industry. In this study, multi-source information fusion was employed to fuse the data from near-infrared spectroscopy, electronic tongues, and other tests and establish an overall quality consistency evaluation method for Atractylodis Macrocephalae Rhizoma, which provided methodological support for the overall quality evaluation of Atractylodis Macrocephalae Rhizoma. The near-infrared spectroscopy information was measured in both static and dynamic states for 23 batches of Atractylodis Macrocephalae Rhizoma samples from different sources, and the electronic tongue sensory information, moisture content, and leachate content were measured. The overall quality of Atractylodis Macrocephalae Rhizoma was evaluated by multi-source information fusion. The results showed that the near-infrared spectroscopy information of 16122103, 801000509, 801000352, 701003656, HX21L01, and 160956 was different from that of other batches of Atractylodis Macrocephalae Rhizoma powder in the static state, and 701003298, 16122103, 701003656, 701003107, 801000229, and 18090404 were the different batches in the dynamic state. The moisture content showed no significant difference between batches. The leachate content in the batch 801000509 was different from that in other batches. The electronic tongue sensory information of 150721004, 151237, 160703004, HX21M01, HX21K04, HX21K01, and 601003516 was different from that of other batches. Furthermore, data layer fusion was employed to analyze the overall quality of Atractylodis Macrocephalae Rhizoma. Four batches, 150721004, HX21M01, HX21K04, and HX21K01, showed the parameters exceeding the 95% control limits and differed from the other samples in terms of the overall quality. This study integrated the information of moisture, near-infrared spectroscopy, and other sources to evaluate the quality consistency among 23 batches of Atractylodis Macrocephalae Rhizoma samples, which provides a reference for the quality consistency evaluation of Chinese medicinal decoction pieces.