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Cellulose fibers are a major industrial input, but due to their irregular shape and anisotropic material response, accurate material characterization is difficult. Single fiber tensile testing is the most popular way to estimate the material properties of individual fibers. However, such tests can only be performed along the axis of the fiber and are associated with problems of enforcing restraints. Alternative indirect approaches, such as micro-mechanical modeling, can help but yield results that are not fully decoupled from the model assumptions. Here, we compare these methods with nanoindentation as a method to extract elastic material constants of the individual fibers. We show that both the longitudinal and the transverse elastic modulus can be determined, additionally enabling the measurement of fiber properties in-situ inside a sheet of paper such that the entire industrial process history is captured. The obtained longitudinal modulus is comparable to traditional methods for larger indents but with a strongly increased scatter as the size of the indentation is decreased further. [ABSTRACT FROM AUTHOR]

Droplet impact and penetration into the paper-like medium are essential physical phenomena in variety of natural and industrial processes. The lattice Boltzmann model coupled with random-walk-based stochastic scheme is presented to calculate the interactions between inertial dominated droplet and fibrous medium. Results show that the droplet spreading regime is independent of surface wettability, volume fraction, and Ohnesorge number at very early impact stage. For fibrous medium with volume fraction ~ 50% and wettability 85°, three stages of penetration process are observed. Whereas for low-volume-fraction fibrous medium, the phases of droplet spreading and penetration are coupled which results in a small spreading width for hydrophilic wettability. It is found that the spaces of spreading width and penetration rate are divided by the curve of O h = 0.038 and R e = 144 . The effects of Ohnesorge and Reynolds numbers largely influence the behaviors of droplet spreading and penetration process. The in-depth insight of inkjet droplet impact onto the paper-like fibrous medium is beneficial for improving printed products in Paper Electronics. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

The kraft manufacturing process is the main pulping process in the paper industry. The kraft chemical recovery process is an efficient technology that enables the recycling of the pulping chemicals and the generation of electrical power. However, this process presents substantial issues related to energy consumption and environmental emissions. One of the main fundamental elements of the kraft process is the lime kiln. Lime kiln gas consumption, SO2, and NOx air emissions are key factors from the energy saving point of view (i.e., energy efficiency) and environmental pollution in this industrial process, respectively. Knowledge of the process variables involved in a lime kiln and how these are related to gas consumption and air emissions is essential to predict the kiln's behavior and minimize its environmental effects. The aim of this research study is to build a regression model for each one of the three prime variables (gas consumption, SO2, and NOx emissions) of a lime kiln employed in the paper manufacturing process using the multivariate adaptive regression splines (MARS) method in combination with the artificial bee colony (ABC) technique. These two statistical learning techniques were combined, thereby obtaining an easy-to-interpret mathematical model with a high goodness-of-fit. A coefficient of determination greater than 0.9 is obtained for all the modeled variables. Moreover, the particular contribution or importance of the input variables in each model is also calculated. The results thus obtained are a useful instrument to gain a better understanding of the dynamics of the lime kiln and the involvement of the process variables in gas consumption and gas emissions. [ABSTRACT FROM AUTHOR]

Different manufacturers do not produce the same quality of children's Ca–Fe–Zn oral liquid due to different production materials and processes. To improve the phenomenon of counterfeit and imitation oral liquid on the market and effectively monitor its quality, laser-induced breakdown spectroscopy (LIBS) fingerprinting with sample preparation methods can provide a tool for real-time and rapid detection of oral liquids. The sample preparation methods include filter paper adsorption (FPA), filter paper adsorption with elemental Cu (FPA with Cu), adding dropwise to glass slides (ADS), adding dropwise to glass slides with elemental Cu (ADS with Cu), and gel preparation (GP). This work collected LIBS spectrum of oral liquids from eight manufacturers. The model for eXtreme Gradient Boosting (XGBoost) was constructed for classifying oral liquids based on five sample preparation methods. The accuracy was 91.25, 94.17, 55.42, 91.25, and 91.29%, respectively. The results show that the FPA method is more straightforward, efficient, and less affected by the specificity of the color of the sample. Both ADS and GP are susceptible to the color characteristics of the sample and are not well suited to the direct detection of transparent liquids. This work demonstrated that oral liquids could be discriminated by analyzing LIBS spectrum combined with the XGBoost model. Additionally, sample preparation, like the simple FPA method, can improve the accuracy of LIBS classification. [ABSTRACT FROM AUTHOR]

The global trend in manufacturing has shifted from a manufacturing-centric process toward a user-centric process. This has resulted in a shorter lifespan and a high product replacement rate of any consumer product. Germany has introduced the concept of Industry Revolution 4.0 (IR 4.0) to convert manufacturing processes and mechanisms into cyber-physical systems (CPS). Digital factory, being the first step into CPS and IR4.0, is being targeted as the most important evolution of the manufacturing industry. This paper defines digital factories and their differences between other similar domains such as smart factories, CPS, and virtual factories. The requirements and goals of a digital factory are explained in detail to facilitate future digital factory tool developments. Furthermore, the current challenges faced in the implementation of the digital factory are proposed to be approached by adapting an interoperable virtual reality technology. This paper emphasizes the usage of virtual reality (VR) in simulating a digital factory that aids in the decision-making and efficient operation of a manufacturing facility. Furthermore, recommendations gathered from previous studies for developing VR-based digital factory tools are also explained in detail in this paper. [ABSTRACT FROM AUTHOR]

The clogging phenomenon finds extensive application in both industrial processes and daily life events. While this broad spectrum of application motivated extensive research to identify the general factors underlying the clogging mechanism, it results in a fragmented and system-specific understanding of the entire clogging process. Therefore, it is essential to establish a holistic understanding of all contributing factors of clogging based on the microscopic physical mechanisms. In this paper, we experimentally investigate clogging of granular materials in a two-dimensional hopper flow and present a self-consistent physical mechanism of clogging based on precursory chain structures. These chain structures follow a specific modified restricted random walk, and clogging occurs when they are mechanically stable enough to withstand the flow fluctuations. We introduce a single-particle model that can explain the arch-forming probability. Our results provide insight into the microscopic mechanism behind clogging and a broader understanding of the dynamics of dense granular flow. The study of the general factors underlying clogging are fragmented and application-driven due to its broad spectrum of applications. The authors propose a holistic understanding of the clogging process by investigating the clogging of a granular hopper flow using high-speed imaging, finding a signature formation of precursory chain structures. [ABSTRACT FROM AUTHOR]

Earlier access to patients' biomarker status could transform disease management. However, gold-standard techniques such as enzyme-linked immunosorbent assays (ELISAs) are typically not deployed at the point-of-care due to their cumbersome instrumentation and complexity. Electrochemical immunosensors can be disruptive in this sector with their small size and lower cost but, without further modifications, the performance of these sensors in complex media (e.g., blood) has been limited. This paper presents a low-cost fluidic accessory fabricated using widely accessible materials and processes for boosting sensor sensitivity through confinement of the detection media next to the electrode surface. Liquid confinement first highlighted a spontaneous reaction between the pseudoreference electrode and ELISA detection substrate 3,3',5,5'-tetramethylbenzidine (TMB) that decreases the amount of oxTMB available for detection. Different strategies are investigated to limit this and maximize reliability. Next, flow cell integration during the signal amplification step of sensor preparation was shown to substantially enhance the detection of cytokine interleukin-6 (IL-6) with the best sensitivity boost recorded for fresh human plasma (x7 increase compared to x5.8 in purified serum and x5.5 in PBS). The flow cell requires no specialized equipment and can be seamlessly integrated with commercial sensors, making an ideal companion for electrochemical signal enhancement. [ABSTRACT FROM AUTHOR]

The growing expansion of the manufacturing sector, particularly in Mexico, has revealed a spectrum of nearshoring opportunities yet is paralleled by a discernible void in educational tools for various stakeholders, such as engineers, students, and decision-makers. This paper introduces a state-of-the-art framework, incorporating virtual reality (VR) and artificial intelligence (AI) to metamorphose the pedagogy of advanced manufacturing systems. Through a case study focused on the design, production, and evaluation of a robotic platform, the framework endeavors to offer an exhaustive educational experience via an interactive VR environment, encapsulating (1) Robotic platform system design and modeling, enabling users to immerse themselves in the design and simulation of robotic platforms under varied conditions; (2) Virtual manufacturing company, presenting a detailed virtual manufacturing setup to enhance users' comprehension of manufacturing processes and systems, and problem-solving in realistic settings; and (3) Product evaluation, wherein users employ VR to meticulously assess the robotic platform, ensuring optimal functionality and customer satisfaction. This innovative framework melds theoretical acumen with practical application in advanced manufacturing, preparing entities to navigate Mexico's manufacturing sector's vibrant and competitive nearshoring landscape. It creates an immersive environment for understanding modern manufacturing challenges, fostering Mexico's manufacturing sector growth, and maximizing nearshoring opportunities for stakeholders. [ABSTRACT FROM AUTHOR]

Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration. [ABSTRACT FROM AUTHOR]

The B. John Davies Prize for the best paper published in IJAMT 2020 has been awarded to Yu YANG, Shimin MAO, Bo BAI, and Yuhua KUANG. It is applied directly to the free-form machine tool, eliminating the need for the equivalent conversion of machine settings from the cradle-type machine tool to the free-form machine tool, and taking advantage of the flexibility and freedom offered by the free-form machine tool. [Extracted from the article]

The simultaneous monitoring of both the process mean and dispersion has gained considerable attention in statistical process control, especially when the process follows the normal distribution. This paper introduces a novel Bayesian adaptive maximum exponentially weighted moving average (Max-EWMA) control chart, designed to jointly monitor the mean and dispersion of a non-normal process. This is achieved through the utilization of the inverse response function, particularly suitable for processes conforming to a Weibull distribution. To assess the effectiveness of the proposed control chart, we employed the average run length (ARL) and the standard deviation of run length (SDRL). Subsequently, we compared the performance of our proposed control chart with that of an existing Max-EWMA control chart. Our findings suggest that the proposed control chart demonstrates a higher level of sensitivity in detecting out-of-control signals. Finally, to illustrate the effectiveness of our Bayesian Max-EWMA control chart under various Loss Functions (LFs) for a Weibull process, we present a practical case study focusing on the hard-bake process in the semiconductor manufacturing industry. This case study highlights the adaptability of the chart to different scenarios. Our results provide compelling evidence of the exceptional performance of the suggested control chart in rapidly detecting out-of-control signals during the hard-bake process, thereby significantly contributing to the improvement of process monitoring and quality control. [ABSTRACT FROM AUTHOR]

The development of environmentally friendly catalysts for organic transformations is of great importance in the field of green chemistry. Aldehyde oxidation reactions play a crucial role in various industrial processes, including the synthesis of pharmaceuticals, agrochemicals, and fine chemicals. This paper presents the synthesis and evaluation of a new metallosalen carbon nitride catalyst named Co(salen)@g-C3N4. The catalyst was prepared by doping salicylaldehyde onto carbon nitride, and subsequently, incorporating cobalt through Schiff base chemistry. The Co(salen)@g-C3N4 catalyst was characterized using various spectroscopic techniques including Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Infrared Spectroscopy (IR), and Thermogravimetric Analysis (TGA). Furthermore, after modification with salicylaldehyde, the carbon nitride component of the catalyst exhibited remarkable yields (74–98%) in oxidizing various aldehyde derivatives (20 examples) to benzoic acid. This oxidation reaction was carried out under mild conditions and resulted in short reaction times (120–300 min). Importantly, the catalyst demonstrated recyclability, as it could be reused for five consecutive runs without any loss of activity. The reusable nature of the catalyst, coupled with its excellent yields in oxidation reactions, makes it a promising and sustainable option for future applications. [ABSTRACT FROM AUTHOR]

In embedded systems, the execution semantics of the real-time operating system (RTOS), which is responsible for scheduling and timely execution of concurrent processes, is crucial for the correctness of the overall system. However, existing approaches for the formal verification of embedded systems typically abstract from the RTOS completely, or provide a detailed and synthesizable formal model of the RTOS. While the former may lead to unsafe systems, the latter is not compatible with industrial design processes. In this paper, we present an approach for reusable abstract formal models that can be configured for custom RTOS. Our key idea is to formally capture common execution mechanisms of RTOS like preemptive scheduling, event synchronization, and communication abstractly in configurable timed automata models. These abstract formal models can be configured for a concrete custom RTOS, and they can be combined into a formal system model together with a concrete application. Our reusable models significantly reduce the manual effort of defining a formal model that captures concurrency and real-time behavior, together with the functionality of an application. The resulting formal model enables analysis, verification, and graphical simulation. We validate our approach by formalizing and analyzing a rescue robot application running the custom open source RTOS EV3RT. [ABSTRACT FROM AUTHOR]

Memristive devices have become promising candidates to complement the CMOS technology, due to their CMOS manufacturing process compatibility, zero standby power consumption, high scalability, as well as their capability to implement high-density memories and new computing paradigms. Despite these advantages, memristive devices are susceptible to manufacturing defects that may cause faulty behaviors not observed in CMOS technology, significantly increasing the challenge of testing these novel devices after manufacturing. This work proposes an optimized Design-for-Testability (DfT) strategy based on the introduction of a DfT circuitry that measures the current consumption of Resistive Random Access Memory (ReRAM) cells to detect not only traditional but also unique faults. The new DfT circuitry was validated using a case study composed of a 3x3 word-based ReRAM with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library. The obtained results demonstrate the fault detection capability of the proposed strategy with respect to traditional and unique faults. In addition, this paper evaluates the impact related to the DfT circuitry's introduced overheads as well as the impact of process variation on the resolution of the proposed DfT circuitry. [ABSTRACT FROM AUTHOR]

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies' technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making—including aspects of salience, credibility, and legitimacy—within each national context. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology is highly regarded due to its advantages, such as high precision and the ability to address complex geometric challenges. However, the development of additive manufacturing process is constrained by issues like unclear fundamental principles, complex experimental cycles, and high costs. Machine learning, as a novel artificial intelligence technology, has the potential to deeply engage in the development of additive manufacturing process, assisting engineers in learning and developing new techniques. This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing, particularly in model design and process development. Firstly, it introduces the background and significance of machine learning-assisted design in additive manufacturing process. It then further delves into the application of machine learning in additive manufacturing, focusing on model design and process guidance. Finally, it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing. [ABSTRACT FROM AUTHOR]

This paper considers the use of loan loss reserves (LLRs) in the banking production process and treats it as one variable with a dual role. We establish a three-stage network Data Envelopment Analysis model to address this issue. Using a sample of 43 Chinese commercial banks over the period 2011–2019, the results show that the banks with the ratio between LLRs and total loans less than 1% have higher level of efficiency compared to the ones holding the ratio greater than 1%. The results show that when excluding LLRs in the production process, the efficiency scores are significantly inflated. We find that small and medium sized banks are more efficient than their big counterparts, however, the results show that big banks hold more than enough amounts of LLRs than the one required by the regulatory authority. When LLRs are excluded from the production process, it shows that big banks perform better than small and medium sized banks. Our findings show that less liquid banks perform better than the ones with higher levels of liquidity no matter in which way LLRs are treated. Finally, we find that lower capitalized banks, compared to the ones with high levels of capitalization, are less efficient. however, it shows that higher capitalized banks consistently keep more than 1% LLRs out of total loans. [ABSTRACT FROM AUTHOR]

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) processes are known as revolutionary manufacturing processes that fabricate a part using a 3D model layer upon layer. These techniques gained more attention from various industries due to their advantages like low waste material. Also, these processes can produce any part with high degrees of complexity in a short period of time. The Fused Deposition Modeling (FDM) process is a material extrusion-based technique which works by extruding a fine molten polymeric filament through a heated nozzle on the heated platform named printer bed. In this method, some important manufacturing parameters play a crucial role in controlling the mechanical properties and quality of the final fabricated part. However, all printed specimens through the FDM process should be tested based on the standards under some critical circumstances. Thus, in the current research paper, five and three test speeds are considered in tensile and fracture testing procedures, respectively to evaluate how these speeds can affect the mechanical and mode I fracture properties. Also, as the FDM specimens present elastic–plastic behavior, the critical value of J-integral is assumed as a fracture assessment and calculated from the finite element analysis. Among the mechanical properties, ultimate tensile strength is affected significantly by the test speed. For instance, the ultimate tensile strength of FDM specimens is 39.02, 38.58, 42.33, 48.09, and 52.11 for test speeds of 2, 4, 6, 8, and 10 mm/min, respectively. But vice-versa results are detected for the mode I fracture behavior and corresponding values of J for the FDM-PLA specimens. Finally, experimental and numerical results together with comprehensive discussions about the considered speeds and obtained results are reported. [ABSTRACT FROM AUTHOR]

This paper introduces a prediction model based on machine learning techniques for dimensional control in the manufacturing process of side flange bearing housings, according to the technical standard DIN 31693. The process is implemented in a journal-bearing manufacturing industry positioned among the three brands with the highest participation in the international market in 2023. The manufacturing process consists of rigid machining processes composed of a universal horizontal machining center and dimensional control composed of a coordinate measuring machine. After machining, the part is measured, and its dimensional report is generated. Qualified professionals use deviations obtained from this report to support the decision-making. The method used is based on the holistic monitoring of the surface geometry of the machined part. The approach used to compensate for dimensional deviations is based on monitoring and modeling the total deviation. In this context, the effects of all sources of systematic errors are compensated regardless of their origin. The heuristic is used for the steps that make up the decision-making process. The way to implement the predictive model in the production line is based on the interaction between human and machine experience. This paper proposes using the regression decision trees for defining the displacement parameters of the machining center axes from the dimensional results of housings obtained in the coordinate measuring machine. The model is validated if the mean absolute error is less than or equal to 0.003 mm. A comparison between an assembled model is performed to verify the performance between different predictive models. [ABSTRACT FROM AUTHOR]

Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration. [ABSTRACT FROM AUTHOR]

The small punch (SP) test has been widely used in applications where conventional mechanical tests cannot be performed; this paper predicts the material strength from the SP test results by empirical correlations method. The yield strength (YS) and ultimate tensile strength (UTS) models serve to associate the load–displacement (LD) curves of SP tests with the stress–strain data of uniaxial tensile tests, and then, the feasibility of these models at different temperatures is investigated. Using the Pearson's correlation coefficient (PCC) method, the ideal strength equations are accessed from past studies for the cast superalloys, including ZG15Cr2Mo1, P91, 316H, and Hastelloy X, whereas the validation results of the additively manufactured (AM) GH4169 alloy are not satisfactory. As a result, considering the unique manufacturing process of AM GH4169, this paper develops a YS model with correlation coefficients higher than 90% between the calculated and actual values and proposes a UTS formula with estimation errors less than 3.5%. Furthermore, with the purpose of superior strength prediction results, the discussion of the SP test velocity reveals that 0.50 mm min−1 had wide adaptability to various strength models and operating temperatures. [ABSTRACT FROM AUTHOR]

In this paper we propose three models for locating multiple facilities anywhere in the plane. The facilities serve demand points and require raw materials from a list of available sources. Problem characteristic originally proposed in 1909 by Weber for manufacturing systems. Weber argued that optimal locations involve minimizing total transport cost which was comprised of the costs of transporting the raw materials and the delivery cost of the final product when plant production and location costs were invariant across the plane. Both the parameters of raw material sources and demand points affect the best locations for the facilities. In this paper, a special algorithm is designed to heuristically solve these three models. The algorithm exploits the special structure of the models. Problems with up to 2000 demand points and 20 facilities were tested. The results are compared with applying available non-linear solvers in a multi-start approach. The special algorithm performed better in most instances especially for a large number of facilities and a large number of demand points. [ABSTRACT FROM AUTHOR]

The demand for micro-components continues to increase, and there is a constant trend towards miniaturization and complexity. The traditional forming process micromachining technology has disadvantages such as high cost, complex manufacturing process, and long cycle, which cannot meet the needs of the industrial development. A new micro-forming process of metal foil arrays based on submerged cavitation water jets is proposed in this paper, and the shapes of the mold grooves are selected as triangular, quadrilateral, pentagonal, and hexagonal. The micro-jet and shock wave generated by the collapse of the cavitation in the submerged cavitation jet is used as the force-loading method of the foil to complete the flexible array micro-forming of the metal foil in this process. The results show that: the flow field at different incident pressures has low pressure and low pressure action time, fully satisfying the conditions required for cavitation to occur; the flooded cavitation water jet is concentrated in the downstream collapse area as a ring-shaped area; the more the number of single hole sides of the mold, the lower the deformation resistance in each area of the T2 Cu foil, and the clearer the T2 Cu foil forming profile. The submerged cavitation water jet array micro-forming studied in this paper is a low-cost, green, high-efficiency, and highly applicable forming process, which is a beneficial exploration and attempt at a new type of foil array micro-forming, which has high research value and good application prospects. [ABSTRACT FROM AUTHOR]

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

An ultra-fast humidity sensor is a useful sensing node in medical monitoring and industrial processing units. In this paper, epoxy-carbon nanotubes (CNTs) nanocomposites-based humidity sensors at various CNTs concentrations were fabricated by spin coating technique. The structural and morphological properties of the prepared samples were studied by X-ray diffraction (XRD) analysis and scanning electron microscopy (SEM), respectively. Fourier transform infrared spectroscopy (FTIR) was employed to characterize the functional groups of the composites. Thin films of the synthesized nanocomposites were deposited onto the surface of interdigitated electrodes (IDEs) and humidity sensing properties were investigated at different working frequencies (0.1–1.5 kHz) in the humidity range 30–90% RH at room temperature. Additionally, response/recovery time and sensitivity of fabricated devices were also measured. The obtained experimental results revealed that CNTs concentration plays a key role in the sensing properties of the current sensors. The optimum CNTs concentration was found to be 1.0 wt % by virtue of its high sensitivity, quick capacitive response/recovery time (14 s/6 s) and resistive response/recovery time (10 s/5 s) as compared to the pristine epoxy and CNTs. [ABSTRACT FROM AUTHOR]

3D printing technology, as a flexible and efficient manufacturing method, provides new possibilities for preparing flexible tactile sensors. This paper reviews the preparation results of flexible tactile sensors based on 3D printing technology. Three types of 3D printing technology commonly used in preparing flexible tactile sensors are first described: photopolymerization, material extrusion, and electrohydrodynamic. Under the process corresponding to each type, suitable printing materials are discussed, and their applications in flexible tactile sensors preparation are explored. On this basis, the effects of these three types of processes and printing materials on the performance of flexible tactile sensors and the related application progress are analyzed. Finally, the challenges and opportunities of flexible tactile sensors based on 3D printing technology are summarized, and the future development trend is prospected. [ABSTRACT FROM AUTHOR]

Wireless technology is becoming increasingly critical in industrial environments in recent years, and the popular wireless standards are WirelessHART, ZigBee, WLAN and ISA100.11a, commonly used in closed-loop systems. However, wireless networks in closed-loop control experience packet loss or drops, system delay and data threats, leading to process instability and catastrophic system failure. To prevent such issues, it is necessary to implement dead-time compensation control. Traditional techniques like model predictive and predictive PI controllers are frequently employed. However, these methods' performance is sluggish in wireless networks, with processes having long dead times and set-point variations, potentially affecting network and process performance. Therefore, this paper proposes a fractional calculus-based predictive PI compensator for wired and wireless networks in the process control industries. The proposed technique has been simulated and evaluated on industrial process models, including pressure, flow, and temperature, where measurement and control are carried out wirelessly. The wireless network's performance has been evaluated based on packet loss, reduced throughput, and increased system latency. The proposed compensator outperformed traditional methods, demonstrating superior set-point tracking, disturbance rejection, and delay compensation characteristics in the performance evaluations of the first, second, and third-order systems. Overall, the findings indicate that the proposed compensator enhances wireless networks' performance in the process control industry and improves system stability and reliability by reducing almost half of the overshoot and settling an average of 8.3927% faster than the conventional techniques in most of the systems. [ABSTRACT FROM AUTHOR]

Statistical Process Control (SPC) plays a vital role in maintaining quality and reducing variability in manufacturing processes. Among SPC techniques, the Exponentially Weighted Moving Average (EWMA) stands out for its ability to detect small process shifts quickly, making it a valuable tool in ensuring product consistency and preventing quality issues. EWMA constructs control charts to monitor process mean shifts, tracks product/service quality by identifying variations, and monitors manufacturing process parameters for early detection of deviations and necessary adjustments. EWMA control chart has been proposed as an alternative to the Shewhart control chart. Sequential measurements are processed using the EWMA function before being placed on the control chart. One of the crucial concerns about the EWMA control chart is the asymmetry of the data around the mean. Although processing with the EWMA function reduces data skewness, the problem of asymmetric data may not be solved. The control chart is designed to leave in front of the upper control limit (UCL) α/2 of the data and behind the lower control limit (LCL) another α/2 of the data, and this does not occur in the case of symmetric data. α/2 represents the significance level for each tail in a two-tailed hypothesis test, indicating the probability of incorrectly rejecting the null hypothesis for each side of the distribution. Since many of the distributions in real life can be approximated by the Gamma distribution, the Gamma distribution was adopted in this study. The Monte Carlo simulation methodology was implemented to generate Gamma distributed data, process it with EWMA function and assess the skewness and kurtosis. The purpose of this paper is to evaluate the effect of EWMA parameters on the performance of the EWMA control chart. Moreover, it focuses on skewness and kurtosis reduction after data processing using the EWMA function. The findings help researchers and practitioners to select the best parameters. Further, the research investigates the effect of EWMA parameter on the shape of distribution. [ABSTRACT FROM AUTHOR]

Reasonable production allocation of tight sandstone gas reservoirs is an important basis for efficient development of gas wells. Taking Block XX in Ordos Basin as an example, the modified flowing material balance equation was established considering the variation of gas viscosity and compression coefficient, the advantages and disadvantages of the method were discussed, and a reasonable production allocation process for gas wells was developed. The results show that: ① The commonly used flow material balance method ignores the change of natural gas compression coefficient, viscosity and deviation coefficient in the production process. The slope of the relationship curve between bottom hole pressure and cumulative production and the slope of the relationship curve between average formation pressure and cumulative production are not equal After considering this change. Compared with the results calculated by the material balance method, the results calculated by the flow material balance method are smaller. ② The production of 660 gas wells in the study area during stable production period is verified. Compared with the open flow method, the dynamic reserve allocation method is better, with an error of 0.06%. ③ The new method in this paper is used to allocate production for different types of gas wells. The cumulative production of different types of gas wells shows different degrees of increase. The I, II, III and IV types of gas wells increase by 32.26%, 30.29%, 23.58% and 25.07% respectively. This study provides technical support for dynamic reserve calculation and reasonable production allocation of gas wells in the study area, and has important guiding significance for the formulation of reasonable development plan and economic and efficient development of tight sandstone gas reservoirs. [ABSTRACT FROM AUTHOR]

Scholarly work points to 5S + 1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6th S in 5S + 1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing personal protective equipment (PPE). The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers' PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and verifies PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 frames per second (FPS), making it suitable for real-time detection. [ABSTRACT FROM AUTHOR]

Large-mechanical-part machining is a very important trend for modern industry to develop, and it has attracted a lot of attention from advanced industries. As an important element of the research, the manufacturing system for large parts has been widely studied. In order to get a comprehensive understanding of this kind of system, the state of the art in several aspects including the classification of the system, major challenges facing each kind of system, structure and optimizes design of the system are summarized in this paper. The manufacturing system is divided into two categories: large workshop machine tools and light and agile machine systems. The design and optimization methods for large workshop machine tool structural parts are summarized. Common techniques for error compensation are also analyzed. Development of the light and agile machine system is stated, and its further classification is carried out. Significantly, features as well as advantages and disadvantages of different systems are analyzed. Finally, this paper gives out further research on the manufacturing systems for large parts. [ABSTRACT FROM AUTHOR]

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving. [ABSTRACT FROM AUTHOR]

In order to study the milling machinability of Zr-based bulk metallic glass, a comparative experimental study on the surface roughness of the milling groove bottom surface for Zr-based bulk metallic glass was carried out in this paper. Through different processing materials, milling tools with different coatings, milling tools with different geometric parameters, and different processing conditions, a large number of comparative tests were carried out on the surface roughness of the milling groove bottom surface for Zr-based bulk metallic glass. The surface roughness values of the milling groove bottom surface are compared, studied, and analyzed from many angles (surface roughness Sa, Sq, Sz, Ssk, and Sku). The test results show that Zr-based bulk metallic glass has good milling machinability. It can obtain a low surface roughness value through the processing conditions and methods used in the test. The research content of this paper provides experimental basis for groove milling of Zr-based bulk metallic glass. At the same time, it also provides a strong test basis for the manufacture of Zr-based bulk metallic glass parts. [ABSTRACT FROM AUTHOR]

Insufficient data is always a challenge for developing an accurate machine learning or deep learning model in manufacturing processes, especially in tool wear monitoring under varied cutting conditions. This paper presents a Random Forest model for predicting tool wear under varied cutting conditions as well as studies extracted signal features. The Random Forest algorithm was chosen as the machine learning model, rather than the novel deep learning model. This was due to the feature importance investigation, which was embedded in the Random Forest algorithm, thereby making it easier to study the physical meanings of signal features. The frequency domain signals were rearranged as features related to spindle speeds and machine tool structure based on domain knowledge. This is the first paper to rearrange the frequency domain signals for observing the physical meanings of selected features. When data normalization was adopted, frequency domain signals related to spindle speeds were excluded from important features. Only spectrum energy related to structure vibration and time domain signals were important features. Data normalization enhanced the weighting of structure vibration features in a machine learning model. This study showed that feature normalization made the machine learning model more adaptable to different cutting conditions. Furthermore, prediction accuracy for cutting condition of spindle speed = 42,000 rpm and feed = 1.5 μm/rev (lowest prediction accuracy among cutting tests in this study) showed an increase from 68.0 to 84.1%. In addition, spindle speed had a more significant effect than feed on classification accuracy in tool wear monitoring based on experimental results. As a result, at least two data sets of the same spindle speed as in tool wear prediction were recommended to be used for model training. When there were at least two data sets in training data with the same spindle speed as in testing data, the study showed prediction accuracies were greater than 75% without data normalization and 81% with data normalization. [ABSTRACT FROM AUTHOR]

With the advent of printed electronics, electrode manufacturing has made significant progress. In contrast to the traditional lift-off method, printing methods offer several advantages. They are not limited by the shape, structure, or material of the substrate, allowing for the manufacturing of electrodes using a wider range of materials. The manufacturing process can be completed at low temperatures, and the cost is reduced due to less material consumption. Printing methods can be broadly categorized into plate printing and plateless printing. Screen printing is a representative plate printing method, while inkjet printing is a typical plateless method. The two techniques are widely used in electrode manufacturing due to their convenience and the easily customized graphics. Screen printing is particularly efficient for processing electrodes in large quantities, while inkjet printing offers greater flexibility, allowing for quick adjustments to the shape of electrodes. This paper reviews recent advances in electrode manufacturing based on printing, focuses on the characteristics of screen printing and inkjet printing, and makes discussions about the main issues and challenges and the potential solutions. [ABSTRACT FROM AUTHOR]

In the course of manufacturing excellence, decision makers are consistently confronted with the task of making choices that will enhance and meet industrial plant's requirements. To this end, it is essential to maintain machines and equipment in a timely manner, which can prove to be one of the primary challenges. Predictive maintenance (PdM) strategy can enable real-time maintenance, providing numerous benefits such as reduced downtime, lower costs, and improved production quality. This article tries to demonstrate efficient physical parameters used in PdM field. The paper presents a case study operated in industrial production process to compare between the most used algorithm in predicting equipment failures. Future research can improve prediction accuracy with other artificial intelligence tools. [ABSTRACT FROM AUTHOR]

Control charts are powerful tools to observe the presentation of the manufacturing process. Mostly, when the data in industries come from the process may follow non-normal or unknown distributions. So, the distribution-free control charts are useful in practice when the possibility model of the process productivity is unknown. In such situations, the correct selection of the sampling mechanism is beneficial for process examination. This paper proposes a nonparametric exponentially weighted moving average signed-rank (EWMA-SR) and also proposed a homogeneously weighted moving average Signed-Rank (HWMA-SR) control charts for examining the small shift in process with the help of an auxiliary variable (in the form of a regression estimator) by using an efficient plan, namely, a repetitive sampling plan. The proposal's presentation is evaluated and matched with its complements for different symmetric distributions by using some famous run length properties including average run length, median run length, and standard deviation of run length. [ABSTRACT FROM AUTHOR]

Reasonable production allocation of tight sandstone gas reservoirs is an important basis for efficient development of gas wells. Taking Block XX in Ordos Basin as an example, the modified flowing material balance equation was established considering the variation of gas viscosity and compression coefficient, the advantages and disadvantages of the method were discussed, and a reasonable production allocation process for gas wells was developed. The results show that: ① The commonly used flow material balance method ignores the change of natural gas compression coefficient, viscosity and deviation coefficient in the production process. The slope of the relationship curve between bottom hole pressure and cumulative production and the slope of the relationship curve between average formation pressure and cumulative production are not equal After considering this change. Compared with the results calculated by the material balance method, the results calculated by the flow material balance method are smaller. ② The production of 660 gas wells in the study area during stable production period is verified. Compared with the open flow method, the dynamic reserve allocation method is better, with an error of 0.06%. ③ The new method in this paper is used to allocate production for different types of gas wells. The cumulative production of different types of gas wells shows different degrees of increase. The I, II, III and IV types of gas wells increase by 32.26%, 30.29%, 23.58% and 25.07% respectively. This study provides technical support for dynamic reserve calculation and reasonable production allocation of gas wells in the study area, and has important guiding significance for the formulation of reasonable development plan and economic and efficient development of tight sandstone gas reservoirs. [ABSTRACT FROM AUTHOR]

A reconfigurable manufacturing system (RMS) is one of the next generation production systems widely used to meet uncertain market demands in the context of Industry 4.0. The design of the RMS aims to achieve sufficient responsiveness so that it can be quickly adopted to the changes required for a niche market of a customized product family. Most components of the RMS are designed to be modular. Reconfigurable machines are one of the main modular components of the RMS. In the design of the RMS, the problem of machine selection is of primary interest, as the modular machines, with their respective tools and configurations, are selected to perform a given part from the part family. Due to this trilogy of machine, tool, and configuration selection, only one type of machine is considered. To remedy this shortcoming, this work introduces a new concept of modular machine configuration capability, which leads to the selection of two types of machines, namely, the single-spindle modular reconfigurable machines (SRMT) and the multi-spindle reconfigurable machines (MRMT). This paper addresses the problem of machine selection and RMS design. Firstly, a bi-objective mathematical model is developed for the generation of the process plan and the selection of reconfigurable machines. The results obtained, together with an initial layout, are then used to generate the RMS design. Secondly, a new objective function is introduced to address the problem of under-utilization of reconfigurable multi-spindle machines. A NSGA-III (non-dominating sorting genetic algorithm III)-based approach is proposed to solve the proposed models. To help the decision maker, the pseudo-weight technique is used to determine the best process plan and the best machines to include in the new designed RMS. [ABSTRACT FROM AUTHOR]

Manufacturers of nanoparticle-based products rely on detailed information about critical process parameters, such as particle size and size distributions, concentration, and material composition, which directly reflect the quality of the final product. These process parameters are often obtained using offline characterization techniques that cannot provide the temporal resolution to detect dynamic changes in particle ensembles during a production process. To overcome this deficiency, we have recently introduced Optofluidic Force Induction (of2i) for optical real-time counting with single particle sensitivity and high throughput. In this paper, we apply of2i to highly polydisperse and multi modal particle systems, where we also monitor evolutionary processes over large time scales. For oil-in-water emulsions we detect in real time the transition between high-pressure homogenization states. For silicon carbide nanoparticles, we exploit the dynamic of2i measurement capabilities to introduce a novel process feedback parameter based on the dissociation of particle agglomerates. Our results demonstrate that of2i provides a versatile workbench for process feedback in a wide range of applications. [ABSTRACT FROM AUTHOR]

Background: Murraya koenigii (L.) Spreng. has several well-established nutritional and therapeutic applications. Following our desire to investigate the global and scientific community's knowledge of medicinal plants, this study was intended to examine the evolution of knowledge related to M. Koenigii studies. The primary purpose of this paper is to clarify the status of these studies, investigate their methods, findings, and trends, and define their significance within the current research landscape. Results: To achieve these goals, bibliometric analysis was conducted, retrieving, and analyzing 934 original articles published between 1965 and 2023 based on Scopus Dataset results. Data were exported as CVS (comma-separated values) and BibTex files and analyzed using Bibliometrix and VOSviewer software. Articles from 502 sources have been identified, averaging 21.8 citations per document. The research in this plant has had exponential growth (R2 = 0.77). International co-authorship is 13.08%. India and Malaysia are the top publishing countries. Debajo, A.C. (Nigeria), Phatak,R.S. (India), and Sukari,M.A. (Malaysia) are the most productive authors. The top source is the Journal of Ethnopharmacology. "Green synthesis," "nanoparticles," "oxidative stress," "Asian citrus psyllid," "apoptosis," "antimicrobial," "anticancer," "Chromatographic profile," "bioactive compounds," and "alkaloids" are strongly related to the current trends in M. Koenigii research. Regarding the specialized topics, M. Koenigii's study concentrated on using this plant as an antioxidant agent in manufacturing and biological systems. Dynamic subjects like chromatographic profiles, essential oils, and Asian citrus psyllids were included in the motor theme. Conclusions: The current study used bibliometric techniques to evaluate research on M. Koenigii and identify trends and potential future research hot spots. [ABSTRACT FROM AUTHOR]

Friction-stir processing (FSP) is a novel technology that utilizes stirring and friction between a pin tool and processed material to change its partial or integral performance. This paper examined the FSP of AA6061-T6 sheets using a pinless tool, with the cup drawing test mold employed to assess forming properties. The research analyzed the effect of FSP parameters on sample formability. The results show that the optimal improvement rates of the cupping value and the fracture load were 68.83% and 28.25%, respectively. The sample internal structure exhibited uniform distribution, consisting of the shoulder-affected zone (SAZ), the heat-affected zone (HAZ), and the base material turn. Single-pass FSP can form an effective processing zone with a width of 16.0 mm, demonstrating outstanding softening influence. Further, the multi-output least squares support vector regression (MLS-SVR) model was used to examine FSP parameter effects on results, which had high prediction accuracy and extremely close agreement between actual and predicted values. [ABSTRACT FROM AUTHOR]

Helical flute grinding is an important process step in the manufacturing of cylindrical cemented carbide tools where the use of cooling lubricants is a defining factor determining process performance. Finding optimal parameters and cooling conditions for the efficient use of lubricant is essential in reducing energy consumption and in controlling properties of the boundary zone like residual stresses. Any mathematical model describing the interactions between grinding wheel, lubricant and workpiece during the process has to account for the complex microstructure of the wheel; however, this renders the identification of parameters like slip or heat exchange coefficients numerically prohibitively expensive. In this paper, results from grinding oil droplet experiments are compared with simulation results for the wetting behavior of grinding wheels. More specifically, finite element simulations of the thin-film equation are used to identify slip parameters for different grinding wheel specifications (grain size, bonding structure, wetting status). Our results show that both the bonding and the grain size have an influence on the wetting behavior. The slip parameters that we identified account for the fluid-microstructure interactions and will be used to effectively model those interactions in more complex 3D fluid-dynamic simulations via the Beavers-Joseph condition. [ABSTRACT FROM AUTHOR]

The development of autonomous finishing operations in manufacturing process has the potential to decrease the costs and increase the quality of the operations. In this context, robotic manipulators have been introduced in sanding and polishing applications. Inspired by the recent development in machine learning and robotics, this paper is focused on designing a system capable of estimating the surface roughness using only a force torque sensor integrated with a robotic manipulator that performs the sanding of fiberglass panels. We present an investigation into the usage of convolution neural networks on the force-torque data to produce a quantitative estimation of surface roughness. To validate the results obtained a profilometer is used to gather pre- and post-operation data. The establishment of a relationship between measured force data and post-operation surface roughness will be used to develop a prediction of the surface quality for sanding operation using robotic manipulators. This project intents to act as proof-of-concept that traditional robotic sensors, can be used beyond their original scope, minimize the complexity of robotic systems integrated into manufacturing processes. [ABSTRACT FROM AUTHOR]