Recent Patents on Engineering - Volume 18, Issue 5, 2024

Background: Lean Six Sigma is a fact-based, data-driven approach that avoids mistakes to improve quality and efficiency. Artificial intelligence (AI) is now evident in lean six sigma applications. AI waste elimination solutions can eliminate large amounts of waste that LSS could not. In lean six sigma, six sigma tackles process variance, whereas lean reduces waste to improve process quality and efficiency. Objective: To describe new pieces, trends, and the adoption and implementation of new technologies like AI by examining the current literature across multiple aspects for a more instructive and piquant viewpoint. Methods: This study is a combination of systematic and bibliometric review, where the systematic review was based on a class framework by selecting 97 articles from reputed journal databases, and the bibliometric review was conducted using a VOS viewer and web of science database for a period of 15 years (2007-2022). Results: By describing LSS's historical evolution, major concerns, prevalent research approaches, and application areas, the study helps practitioners and academics understand its present state for robust research. AI and other cutting-edge technologies help discover non-value-added operations that are difficult to recognize manually. Conclusion: This study has revealed the critical success factors for deploying LSS in numerous businesses. The motivations, barriers, and limits in the direction of the application of LSS are also discussed. The research trends in implementing modern technologies like AI showed a green wave. Future research may emphasize and dominate LSS implementation issues with modern technologies like AI.

Predictive maintenance (PdM) is a technique that keeps track of the condition and performance of equipment during normal operation to reduce the possibility of failures. Accurate anomaly detection, fault diagnosis, and fault prognosis form the basis of a PdM procedure. This paper aims to explore and discuss research addressing PdM using machine learning and complications using explainable artificial intelligence (XAI) techniques. While machine learning and artificial intelligence techniques have gained great interest in recent years, the absence of model interpretability or explainability in several machine learning models due to the black-box nature requires further research. Explainable artificial intelligence (XAI) investigates the explainability of machine learning models. This article overviews the maintenance strategies, post-hoc explanations, model-specific explanations, and model-agnostic explanations currently being used. Even though machine learningbased PdM has gained considerable attention, less emphasis has been placed on explainable artificial intelligence (XAI) approaches in predictive maintenance (PdM). Based on our findings, XAI techniques can bring new insights and opportunities for addressing critical maintenance issues, resulting in more informed decisions. The results analysis suggests a viable path for future studies.

Background: Handicraft workers usually carry out daily activities by adopting awkward postures. The most prevailing health issues among handicraft workers are musculoskeletal disorders (MSDs). Objective: The current research aims to assess the occurrence of musculoskeletal symptoms and investigate risk factors associated with musculoskeletal disorders among metal sculpture artisans. Subsequently, the study highlighted the future scope of AI applications in ergonomic evaluation. Methods: 144 male metal sculpture workers participated in the study. A modified Nordic questionnaire was adopted to determine the musculoskeletal problems among metal sculpture workers. The probable risk elements for MSD symptoms were identified by applying binary logistic regression. Results: Most workers faced discomfort in various body parts, particularly the wrist, lower back, and shoulders. The outcome of the logistic regression model revealed that job-related factors have significantly contributed to the development of MSD symptoms. Conclusion: This study concedes that awkward working postures for prolonged periods highly affect the health of metal sculpture workers, and there is a need for ergonomic intervention to minimize the risks of musculoskeletal disorders. The study also emphasizes the future scope of Artificial Intelligence (AI) applications that can be used in ergonomically assessing working postures.

Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems.

Background: Advanced technologies, including artificial intelligence (AI) and other Disruptive technology, have been directly responsible for the significant changes, renovations, and enhancements that have taken place in healthcare systems around the world. In spite of the many challenges, particularly in nations still growing their economies, the healthcare industry has a significant number of opportunities. Objective: To explore the key obstacles that were encountered by the healthcare industry both during and after the introduction of AI and other Disruptive technological practices associated with Health 4.0 in the healthcare industry to uncover how these variables influence AI and other Disruptive technology adoption in healthcare sector of India. Methods: An online survey format that included standardized questionnaire data was obtained from 83 hospitals, and a total of 434 samples have been implemented for various healthcare administrative staff members by the adoption of AI and Disruptive technology. ANOVA analysis was done to confirm the hypotheses' assumptions, then descriptive statistics were done to analyze the mean value, and also EFA and CFA analysis with SEM analysis has been done in the SPSS program for numerous validity tests (version 20). Results: This research explored 15 issues that healthcare administration staff members consider barriers and through the use of EFA, only two of the three primary obstacles"Additional workload" and "Problems with adopting technology" have a substantial impact on the rate at which AI is adopted in the healthcare industry, as seen through the eyes of the workers in that area. Conclusion: These challenges include a high need for capital, extra investments in new technologies like the internet of things (IoT), artificial intelligence (AI), business analytics, , resistance to change from both employees and employers, the need for a skilled workforce, and the upkeep of support systems. The use of blockchain technology in India's healthcare system as a secure service for administrative workers in Health 4.0 Practices could solve data security problems.

Background: Modern spinning preparatory has undergone drastic technological changes, but still, individual’s expertise-based decisions govern the complex and non-linear multivariant relationships prevailing amongst raw material (cotton) variables, machine variables, process variables (waste), and product (card sliver) quality. The scientifically precise prediction regarding the cleanliness and quality of card sliver and waste control for the given inputted cotton variables processed on the state-of-the-art machinery setup without waiting for the production and testing of card sliver is still impossible. Methods: The present work describes the use of Aritificial Neural Networks (ANN) for ruling out these limitations on scientific grounds. Previous research and patents were reviewed. A complex system targeted at ANN was developed using the "newff" function on the mill's five-year database. Single-group ANN was initially designed to determine the influence of inherent variations in raw cotton fibre properties and trash content on blow room and card performance. A multi-group approach of ANN was developed at a later stage to define the influence of complex interactions amongst various fibre properties on three main quality measures considered at blow room and carding, viz., i) influence of blow room and card on fibre length properties, ii) fibre damage at blow room or improvement at card, and iii) degree of cleanliness of the output material. Results: Reverse modelling for both groups was also successfully designed to demarcate feed cotton quality and cleanliness requirements for targeted blow room or card cleaning performance. Conclusion: A high level of positive endurance was observed for all ANNs. Multigroup networking has proven to be more precise than single group networking.

Background: Non-Newtonian gas-liquid two-phase flows are often seen in industrial processes such as petroleum, chemical, and food engineering. The efficiency of mass and heat transfer between phases is significantly impacted by bubble rise motion in liquids. Therefore, it is crucial to deeply study the hydrodynamic characteristics of a bubble rising in non-Newtonian fluids to improve the transfer efficiency between phases and to enhance the operational efficiency of bubbling equipment. Methods: To understand the rising characteristics of a bubble in non-Newtonian fluids, a single bubble rising in shear-thinning fluids was experimentally studied using a high-speed camera. The effects of xanthan gum (XG) concentration and bubble diameter on bubble shape, trajectory, and terminal velocity were investigated. Results: Bubble terminal velocity increased with an increase in the bubble diameter and a decrease in XG concentrations. The increase rate of bubble terminal velocity varied with an increase in bubble diameter for the bubbles with different diameters and XG concentrations for the solutions with varying XG concentrations. For solutions with the same XG concentration, the Galilei and Eötvös numbers for a small bubble were relatively small but relatively large for a large bubble. Thus, the rise motion of a bubble in XG solutions becomes unsteady with an increase in bubble diameter and a decrease in XG concentrations. The unsteady characteristics of bubble motion decrease with an increase in the XG concentration of solutions. Conclusion: It was found that the influence of XG concentrations on bubble motion depends on bubble diameter since the magnitude of bubble diameter has an essential effect on the shear-thinning effect of solutions. An increase in bubble terminal velocity is mainly caused by an increase in buoyancy (i.e., bubble diameter) rather than a decrease in the apparent viscosity of solutions.

In recent years, with the development of aerospace, automotive, rolling stock, shipbuilding, military weapons and other industries, the demand for bent and rolled parts is increasing, and the requirements for roll forming accuracy are growing. With the rapid development of coal power, hydropower, nuclear power, wind power, and petrochemical industries, the design and research on roll bending machine and the profile roll forming methods have come into focus. The purpose of this study is to summarize the classification and characteristics of roll bending equipment, analyze the reasons that affect the accuracy of profile bending, and propose specific solutions. This paper summarizes the research status and patents on the existing roll bending equipment, introduces its structural characteristics, differences, and molding effects, and focuses on the difficulties, optimization, and improvement methods of roll bending molding. In this paper, the mechanical structures of the existing roll forming equipment have been analyzed and compared, the effects of different roll forming equipment on the profile bending results have been determined, and the reasons affecting the bending accuracy have been pointed out. By summarizing the patents and research on profile roll forming methods and equipment, the current situation and future research prospects of roll forming equipment are discussed. The roll forming equipment is divided into a roll bending machine, a CNC roll bending machine, and a flexible roll bending machine. Each design can meet different profile processing needs. From the design point of view, the optimization under the given processing profile can achieve relatively high processing accuracy. However, the processing of different profiles will still produce geometric defects, and there is some space for improvement in the structure of roll forming equipment, the materials of processing rollers, and the NC interactive system. Thus, further research on profile roll forming is needed in the future.

Background: “Smart Building” constructed using “Smart Materials” for “Smart People” and to create or build “Smart Societies or Cities” is the new trend all over the world. Introduction: To implement smartness holistically, it is essential to surf through databases where innovations from all over the world are visible or can be retrieved. A "One stop solution" to locate innovations from specific domains is patent databases and hence this paper showcases the current scenario of civil constructions using AI-ML or alike technologies. Method: Relecura database is primarily used for performing analysis of all the patents related to these two collaborative domains. A special focus is given to the analysis of data from 2015-2021, to know the latest trends in technology used for constructing smart buildings. Result: Smart home, sensors, and machine learning are highly found keywords. China is the leading country for patents in the smart building domain. The use of computational models and pictorial communication can be explored to increase the patentability of the innovation. Conclusion: This patent analysis will be useful to the end readers, researchers, and students to understand the innovative concepts implemented so far and in turn understand the possible research gaps in these collaborative domains.

Objective: In order to improve the friction-increasing and wear-reducing performance of the unfolding wheel surface, the surface microstructure of the unfolding wheel used in the detection of 8 kinds of steel balls was optimized by parameter matching. Method: Firstly, based on Hertz's theory, the contact area between steel balls of different sizes and the unfolding wheel are analyzed. The wear depth model is established based on Archard adhesive wear model. Secondly, the appropriate microstructure parameters for friction and wear experiments were selected. The finite element analysis software is used to simulate the stress on the surface of the microstructure unfolding wheel and calculate the wear depth. According to the experimental results, the relationship between friction coefficient, wear depth and microstructure parameters is obtained by data fitting, and the objective function of optimization design is established. Finally, based on the genetic algorithm DNSGA-II and Python, the parameters are optimized, and the optimal solution is obtained by using the TOPSIS method. Results: The feasibility of the simulation method is verified by friction and wear experiments, and the correctness of the optimization method is verified. Some existing patents on friction and wear of microstructure surfaces are introduced, and the future development of this field is prospected. Conclusion: The research shows that the optimal parameters matching of microstructure for steel ball diameters of Ф16.6688 mm~Ф22.2250 mm: the shape is rhombus; the area of a single pit is close to the contact area, which is 0.0319 mm² ~ 0.0554 mm²; the pit depth is 145 μm~150 μm, and the surface density of microstructure is (5.4~5.6) /mm².

Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

Background: Point cloud has become one of the most important data formats for 3D presentation because of the increased availability of acquisition devices and its wide applications. Deep learning has the most powerful ability to capture features from data and has successfully solved various problems in the field of image, such as classification, segmentation, and generation. Deep learning is commonly used to process data with a structured grid, while point cloud is irregular and unstructured. The irregularity of point clouds makes it difficult to use deep learning to solve the problems represented by point clouds. Recently, numerous approaches have been proposed to process point clouds with deep learning to solve various problems. Objective: The objective of this study is to serve as a guide to new scholars in the field of deep learning on 3D surface reconstruction from point clouds as it presents the recent progress in deep learning-based surface reconstruction for point clouds. It helps scholars to grasp the current research situation better and further explore the search direction. Method: This study reviews the recent progress in deep learning-based methods used for surface reconstruction from point clouds and large-scale 3D point cloud benchmark datasets commonly used. Results: Several relevant articles on deep learning used for surface reconstruction from point clouds and some recent patents on deep learning applications are collected and reviewed in this paper. The difficulty of irregularity of point clouds can be overcome by deep learning methods, thus achieving remarkable progress in surface reconstruction. Conclusion: Deep learning for 3D surface reconstruction from point clouds is becoming a research hotspot due to its performance in terms of anti-interference and generalization. Although the advance is remarkable, there are still some challenges that need to be further studied.