Immediate Loading Using the Digitalized Customized Restoration of Single-tooth Implants Placed in Fresh Extraction Sockets in the Aesthetic Anterior Maxilla: A 10-Year Prospective Study of Marginal Bone Level.

AIM: The objective of this study was to assess marginal bone level around single implants inserted in fresh extraction sockets in the anterior maxillary region and instantly restored with computer-aided design/computer-aided manufacturing customized temporary crowns cemented on the final abutment. MATERIALS AND METHODS: A total of 20 patients (15 females and 5 males, with a mean age of 30 years), where 20 were placed in fresh extraction sockets. After raising a full-thickness flap, atraumatic extraction was performed the implant site was prepared and fixtures were stabilized on the palatal bone wall. The implant location was immediately transmitted to the prepared master model using the pick-up impression coping seated in the surgical guide template. Prefabricated abutments were used as the final abutment on the master model, scanned and the crown was planned using computer-aided manufacturing customized software. Later on 8th weeks, abutments were torqued as per the manufacturer's recommendation, and the final crowns were cemented. Using personalized intraoral radiographs marginal bone level was evaluated mesially and distally to the implant shoulder as a reference at implant placement, 8 weeks, 1, 3, 5, and 10 years after loading. RESULTS: Wholly implants were osteo-integrated positively after 10 years of practical loading, but only 18 were available for clinical and radiological follow-up, and 2 patients with two implants were excluded from the study due to relocation abroad without any implant failure. The average marginal bone loss (MBL) in the current report was 0.16 ± 0.167 mm at crown cementation, 0.275 ± 0.171 mm after 1 year, 0.265 ± 0.171 mm after 3 years, 0.213 ± 0.185 mm after 5 years, and 0.217 ± 0.194 mm at 10 years. CONCLUSION: The strategy of inserting and not removing the final abutment at the time of implant placement facilitates the establishment of adequate attachment of both soft and hard tissues to the abutment surface, ensuring uninterrupted organization of tissue architecture and offers advantages in helping maintain soft tissue maturation and preventing marginal bone level. CLINICAL SIGNIFICANCE: Immediately loaded implants in freshly extracted sockets lead to a significant reduction in marginal ridge resorption. The use of a temporary crown on a prefabricated abutment, exclusive of successive abutment manipulation, proved effective in preserving the primarily founding blood clot and served as a prototype for shaping the soft tissue around the previously wounded gum. How to cite this article: Berberi A, El Zoghbi A, Aad G, et al. Immediate Loading Using the Digitalized Customized Restoration of Single-tooth Implants Placed in Fresh Extraction Sockets in the Aesthetic Anterior Maxilla: A 10-Year Prospective Study of Marginal Bone Level. J Contemp Dent Pract 2024;25(3):213-220.

Performance Optimization for a Class of Petri Nets.

Petri nets (PNs) are widely used to model flexible manufacturing systems (FMSs). This paper deals with the performance optimization of FMSs modeled by Petri nets that aim to maximize the system's performance under a given budget by optimizing both quantities and types of resources, such as sensors and devices. Such an optimization problem is challenging since it is nonlinear; hence, a globally optimal solution is hard to achieve. Here, we developed a genetic algorithm combined with mixed-integer linear programming (MILP) to solve the problem. In this approach, a set of candidate resource allocation strategies, i.e., the choices of the number of resources, are first generated by using MILP. Then, the choices of the type and the cycle time of the resources are evaluated by MILP; the promising ones are used to spawn the next generation of candidate strategies. The effectiveness and efficiency of the developed methodology are illustrated by simulation studies.

Industry 5 and the Human in Human-Centric Manufacturing.

Industry 4 (I4) was a revolutionary new stage for technological progress in manufacturing which promised a new level of interconnectedness between a diverse range of technologies. Sensors, as a point technology, play an important role in these developments, facilitating human-machine interaction and enabling data collection for system-level technologies. Concerns for human labour working in I4 environments (e.g., health and safety, data generation and extraction) are acknowledged by Industry 5 (I5), an update of I4 which promises greater attention to human-machine relations through a values-driven approach to collaboration and co-design. This article explores how engineering experts integrate values promoted by policy-makers into both their thinking about the human in their work and in their writing. This paper demonstrates a novel interdisciplinary approach in which an awareness of different disciplinary epistemic values associated with humans and work guides a systematic literature review and interpretive coding of practice-focussed engineering papers. Findings demonstrate evidence of an I5 human-centric approach: a high value for employees as "end-users" of innovative systems in manufacturing; and an increase in output addressing human activity in modelling and the technologies available to address this concern. However, epistemic publishing practices show that efforts to increase the effectiveness of manufacturing systems often neglect worker voice.

Towards a Safe Human-Robot Collaboration Using Information on Human Worker Activity.

Most industrial workplaces involving robots and other apparatus operate behind the fences to remove defects, hazards, or casualties. Recent advancements in machine learning can enable robots to co-operate with human co-workers while retaining safety, flexibility, and robustness. This article focuses on the computation model, which provides a collaborative environment through intuitive and adaptive human-robot interaction (HRI). In essence, one layer of the model can be expressed as a set of useful information utilized by an intelligent agent. Within this construction, a vision-sensing modality can be broken down into multiple layers. The authors propose a human-skeleton-based trainable model for the recognition of spatiotemporal human worker activity using LSTM networks, which can achieve a training accuracy of 91.365%, based on the InHARD dataset. Together with the training results, results related to aspects of the simulation environment and future improvements of the system are discussed. By combining human worker upper body positions with actions, the perceptual potential of the system is increased, and human-robot collaboration becomes context-aware. Based on the acquired information, the intelligent agent gains the ability to adapt its behavior according to its dynamic and stochastic surroundings.

An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis.

This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time Fault Detection and Diagnosis (RT-FDD). The approach's particular characteristics are: it uses only data that are commonly available in industrial automation systems; it automates all ML processes without human intervention; a non-ML expert can deploy it; and it considers the behavior of cyclic sequential machines, combining discrete timed events and continuous variables as features. The capacity for fault detection is analyzed in two case studies, using data from a 3D machine simulation system with faulty and non-faulty conditions. The enhancement of the RT-FDD performance when the proposed approach is applied is proved with the Feature Importance, Confusion Matrix, and F1 Score analysis, reaching mean values of 85% and 100% in each case study. Finally, considering that faults are rare events, the sensitivity of the models to the number of faulty samples is analyzed.

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0.

Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects' flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time.

Formal Verification of Control Modules in Cyber-Physical Systems.

The paper proposes a novel formal verification method for a state-based control module of a cyber-physical system. The initial specification in the form of user-friendly UML state machine diagrams is written as an abstract rule-based logical model. The logical model is then used both for formal verification using the model checking technique and for prototype implementation in FPGA devices. The model is automatically transformed into a verifiable model in nuXmv format and into synthesizable code in VHDL language, which ensures that the resulting models are consistent with each other. It also allows the early detection of any errors related to the specification. A case study of a manufacturing automation system is presented to illustrate the approach.

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the "HOSHIN KANRI TREE" (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain's activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.

Anomaly Detection in Discrete Manufacturing Systems by Pattern Relation Table Approaches.

Anomaly detection for discrete manufacturing systems is important in intelligent manufacturing. In this paper, we address the problem of anomaly detection for the discrete manufacturing systems with complicated processes, including parallel processes, loop processes, and/or parallel with nested loop sub-processes. Such systems can generate a series of discrete event data during normal operations. Existing methods that deal with the discrete sequence data may not be efficient for the discrete manufacturing systems or methods that are dealing with manufacturing systems only focus on some specific systems. In this paper, we take the middle way and seek to propose an efficient algorithm by applying only the system structure information. Motivated by the system structure information that the loop processes may result in repeated events, we propose two algorithms-centralized pattern relation table algorithm and parallel pattern relation table algorithm-to build one or multiple relation tables between loop pattern elements and individual events. The effectiveness of the proposed algorithms is tested by two artificial data sets that are generated by Timed Petri Nets. The experimental results show that the proposed algorithms can achieve higher AUC and F1-score, even with smaller sized data set compared to the other algorithms and that the parallel algorithm achieves the highest performance with the smallest data set.

Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation.

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners' brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

An Approach for Managing Manufacturing Assets through Radio Frequency Energy Harvesting.

The manufacturing industry requests novel solutions that will permit enterprises to stay competitive in the market. This leads to decisions being made based on different technologies that are focused on real-time accurate measurement and monitoring of manufacturing assets. In the context of traceability, radio frequency identification (RFID) tags have been traditionally used for tracking, monitoring, and collecting data of various manufacturing resources operating along the value chain. RFID tags and microelectromechanical systems (MEMS) sensors enable the monitoring of manufacturing assets by providing real-time data. Such devices are usually powered by batteries that need regular maintenance, which in turn leads to delays that affect the overall manufacturing process time. This article presents a low-cost approach to detect and measure radio frequency (RF) signals in assembly lines for optimizing the manufacturing operations in the manufacturing industry. Through the detection and measurement of RF signals, the RF energy can be harvested at certain locations on the assembly line. Then, the harvested energy can be supplied to the MEMS sensors, minimizing the regular maintenance for checking and replacing batteries. This leads to an increase in the operational efficiency and an overall reduction in operational and maintenance costs.

A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems.

With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented. Models as well as algorithms are carefully designed, which can trace back the lifecycle of a possibly contaminated item. A practical prototype system for supporting traceability is designed, and a real-life case study of a quality control system for bee products is presented to validate the effectiveness of the approach.

Effects of mental workload on manufacturing systems employees: A mediation causal model.

BACKGROUND: Although some research has been done in the Mexican manufacturing industry regarding mental workload, none has explored its association with physical fatigue, body weight gain, and human error simultaneously. OBJECTIVE: This research examines the association between mental workload and physical fatigue, body weight gain, and human error in employees from the Mexican manufacturing systems through a mediation analysis approach. METHODS: A survey named Mental Workload Questionnaire was developed by merging the NASA-TLX with a questionnaire containing the mental workload variables mentioned above. The Mental Workload Questionnaire was applied to 167 participants in 63 manufacturing companies. In addition, the mental workload was used as an independent variable, while physical fatigue and body weight gain were mediator variables, and human error was a dependent variable. Six hypotheses were used to measure the relationships among variables and tested using the ordinary least squares regression algorithm. RESULTS: Findings indicated that mental workload significantly correlates with physical fatigue and human error. Also, the mental workload had a significant total association with human error. The highest direct association with body weight gain was provided by physical fatigue, and body weight gain had an insignificant direct association with human error. Finally, all indirect associations were insignificant. CONCLUSION: Mental workload directly affects human error, which physical fatigue does not; however, it does affect body weight gain. Managers should reduce their employees' mental workload and physical fatigue to avoid further problems associated with their health.

A review of medical wearables: materials, power sources, sensors, and manufacturing aspects of human wearable technologies.

Wearable technology is a promising and revolutionary technology that is changing some aspects of our standard of living to a great extent, including health monitoring, sport and fitness, performance tracking, education, and entertainment. This article presents a comprehensive literature review of over 160 articles related to state-of-the-art human wearable technologies. We provide a thorough understanding of the materials, power sources, sensors, and manufacturing processes, and the relationships between these to capture opportunities for enhancement and challenges to overcome in wearables. As a result of our review, we have determined the need for the development of a comprehensive, robust manufacturing system alongside specific standards and regulations that take into account wearables' unique characteristics. Seeing the whole picture will provide a frame reference and road map for researchers and industries through the design, manufacturing, and commercialisation of effective, portable, self-powered, multi-sensing ultimate future wearable devices and create opportunities for new innovations and applications.

FASTory assembly line power consumption data.

Machine learning (ML) techniques are widely adopted in manufacturing systems for discovering valuable patterns in shopfloor data. In this regard, machine learning models learn patterns in data to optimize process parameters, forecast equipment deterioration, and plan maintenance strategies among other uses. Thus, this article presents the dataset collected from an assembly line known as the FASTory assembly line. This data contains more than 4,000 data samples of conveyor belt motor driver's power consumption. The FASTory assembly line is equipped with web-based industrial controllers and smart 3-phase energy monitoring modules as an expansion to these controllers. For data collection, an application was developed in a timely manner. The application receives a new data sample as JavaScript Object Notation (JSON) every second. Afterwards, the application extracts the energy data for the relevant phase and persists it in a MySQL database for the purpose of processing at a later stage. This data is collected for two separate cases: static case and dynamic case. In the static case, the power consumption data is collected under different loads and belt tension values. This data is used by a prognostic model (Artificial Neural Network (ANN)) to learn the conveyor belt motor driver's power consumption pattern under different belt tension values and load conditions. The data collected during the dynamic case is used to investigate how the belt tension affects the movement of the pallet between conveyor zones. The knowledge obtained from the power consumption data of the conveyor belt motor driver is used to forecast the gradual behavioural deterioration of the conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems.

Comparing integrated continuous process "LaVortex®" and conventional batch processes for the pharmaceutical manufacturing of acetaminophen oral dosage formulations: Challenges and pharmaceutical properties of the granular and tableted products.

LaVortex® was developed as a novel free-flow continuous granulation/drying (CGD) system. In this study, we compared the advantages and disadvantages of granules prepared by continuous and batchwise manufacturing systems. Granules containing 30 % acetaminophen were manufactured under various operating conditions using CGD system, with comparison granules manufactured using conventional batch systems that involve a combination of fluid bed granulation (FG), agitation granulation (AG), continuous drying, fluid bed drying, and/or shelf drying, after which the pharmaceutical properties of each type of manufactured granule were evaluated. Cumulative particle-size distributions were determined by sieving, powder flowabilities were determined by angle of repose measurements, and scanning electron microscopy was employed to examine granule morphologies. The CGD system produced fine-to-large spherical or ellipsoidal granules that exhibited excellent powder fluidities and tabletabilities that are almost identical to those of AG granules. Moreover, the CGD granules exhibited better powder flowability than the FG granules. The addition of water promoted CGD-granule growth and improved significantly powder flowability, and did a little in tabletability. Small spherical granules with good fluidity suitable for fine-particle-coating core materials, or large granules with excellent fluidity and tabletability, were prepared by adjusting the values of the elemental parameters of the CGD process.

Knowledge, attitude, and practice of digital dentures among dentists.

The objective of this survey was to evaluate the knowledge, awareness, and practice of digital dentures among dentists. This is a cross-sectional survey conducted from January to February, 2022. Fifteen close-ended questions were framed and circulated among 150 dental practitioners and interns using an online survey form. The responses were collected and statistically analyzed. The results summarize that 95.3% were aware of digital dentures and 4.7% were not. About 60.1% do not use digital workflow, 27% do not have essential equipment, 9.5% were not confident in practicing digital dentures, and 3.4% found that it was inaccurate, showed poor retention, and a well-skilled technician was required. Most dental practitioners are aware of digital dentures. Among all practitioners with postgraduation were more aware of digital dentures than the interns and undergraduate practitioners. Most dentists do not practice digital dentures due to the high initial setup cost and maintenance. The majority of practitioners agree that digital dentures will be the ultimate tool in future dentistry.

Refined Simulation Method for Computer-Aided Process Planning Based on Digital Twin Technology.

Simulation technology is widely used in computer-aided process planning (CAPP). The part machining process is simulated in the virtual world, which can predict manufacturing errors and optimize the process plan. Simulation accuracy is the guarantee of process decision-making and optimization. This article focuses on the use of digital twin technology to build a high-fidelity process model, taking the advantage of the integration of multiple systems, in order to achieve the dynamic association of real-time manufacturing data and process models. Making use of the CAPP/MES systems, the surface inspection data of the part is fed back to the CAPP system and associated with the digital twin process model. The wavelet transform method is used to reduce the noise of the high-frequency signal of the detection data, and the signal-to-noise ratio (SNR) is calculated to verify the noise reduction effect. The surface topography, after noise reduction, was reconstructed in Matlab. On this basis, the Poisson reconstruction algorithm is used to reconstruct the high-fidelity process model for the refined simulation of the subsequent processes. Finally, by comparing the two sets of simulation experiments with the real machining results, we found that the simulation results, based on the digital twin model, are more accurate than the traditional simulation method by 58%.

Workforce and supply chain disruption as a digital and technological innovation opportunity for resilient manufacturing systems in the COVID-19 pandemic.

During the SARS-CoV-2 pandemic (also known as COVID-19), workforce downsizing needs, safety requirements, supply chain breaks and inventory shortages affected manufacturing systems' and supply chain's responsiveness and resilience. Companies wandered in a disrupted scenario because recommended actions/strategies to survive - and thrive - were not available an improvised actions to keep their operations up and running. This paper analyzes the COVID-19 impacts on the workforce and supply resilience in a holistic manner. The following research questions are discussed: (i) how can manufacturing firms cope with urgent staff deficiencies while sustaining at the same time a healthy and safe workforce in the perspective of socially sustainable and human-centric cyber-physical production systems?; (ii) is remote working (cf. smart working) applicable to shop-floor workers?; (iii) is it possible to overcome supply chain breaks without stopping production? In the first part, we propose three Industry 4.0-driven solutions that would increase the workforce resilience, namely: (i) the Plug-and-Play worker; (ii) the Remote Operator 4.0; (iii) the Predictive Health of the Operational Staff. In the second part, the concepts of (i) Digital & Unconventional Sourcing, i.e. Additive Manufacturing, and (ii) Product/Process Innovation are investigated from a novel business continuity and integration perspective. We ultimately argue that forward-looking manufacturing companies should turn a disruptive event like a pandemic in an opportunity for digital and technological innovation of the workplace inspired by the principles of harmonic digital innovation (that places the human well-being at the center). These aspects are discussed with use cases, system prototypes and results from research projects carried out by the authors and real-world examples arising lessons learned and insights useful for scientists, researchers and managers.