Machining Operations Research Articles

Engineering workshop education and supervision: AR for machine operation guidance and safety instructions

This research explores the implementation of Augmented Reality (AR) in the mechanical workshop of an engineering education program. The objective is to provide engineering students with an immersive and interactive learning experience, improving their understanding of machine operation while emphasizing safety protocols. This research introduces an AR approach designed to overcome challenges when comprehending the intricate workings of machines and adhering to safety guidelines. By overlaying digital information onto the physical workspace, AR assists students in visualizing machine components, operational procedures, and safety measures, promoting a more effective learning process. In the AR experience, we incorporate sensor data from the workshop environment. This integration enables real-time monitoring of machine performance, environmental conditions, and student activities. Furthermore, the study investigates the potential of AR for workshop supervision. Workshop operators can monitor students’ activities in real-time, ensuring compliance with safety protocols and providing personalised assistance when needed.

Designing a Resilient Building Maintenance Program: Integrating Preventive and Predictive Maintenance at the U.S. Navy

ABSTRACT Building equipment failure can have drastic effects on a company's operations and budget. This paper presents two types of maintenance approaches that if done effectively, can prevent or significantly reduce the failure of building equipment assets. The first is traditional time-based preventive maintenance (PM), which conducts prefailure inspections and tasks in a cyclic time-based approach. The second, is predictive maintenance (PdM), which conducts maintenance functions based on the condition of the equipment found through continuous or cyclic measurements and analysis during machine operation. The purpose of investigating these maintenance approaches is to determine whether we can improve on the U.S. Navy's (Navy) existing facility maintenance program, helping to reduce overall costs while improving sustainability, equipment resiliency, and efficiency. By presenting each maintenance program and leveraging today's technologies we show that these advancements in technology can directly improve the Navy's operational mission and warfighter readiness. Research was conducted through the following methods: interviews, books, third party reports, journal articles, industry websites and articles that focus on equipment maintenance. The Navy, the National Aeronautics and Space Administration (NASA), and the University of Washington provided case studies, existing facility/utility maintenance data, and budget information used in this research. The results show that PdM approaches using advanced analytics are more effective in diagnosing equipment, prescribing equipment problems, and predicting equipment failure. It will also show that when a PdM model is used, building tenants have less operational impacts as equipment operates longer with less downtime between maintenance events. By changing to a PdM program, facility managers and owners can improve asset efficiency and resilience, directly improving environmental sustainability and lowering overall longterm costs. It highlights the significant capital costs of a fully online PdM program and the benefits of using a hybrid model of PM and PdM. This research concludes with an overview of how building maintenance is currently being conducted on Navy bases by Naval Facilities Engineering Systems Command (NAVFAC), and how they are transitioning to a more sustainable maintenance program leveraging existing advanced metering infrastructure (AMI), building control systems (BCS), and utility control systems (UCS) with Smart Grid (SG), OSI Pi, and advanced analytics. In addition, major gaps in this transition are identified, and solutions are proposed to optimize these building system investments.

Automatic work-element detection: the missing piece in developing intelligent coaching systems for cut-to-length logging machinery

The productivity of cut-to-length machine operators exhibits a significant disparity, with the most productive individuals demonstrating twice the efficiency of their less productive counterparts. This discrepancy is largely attributed to variations in work methods. While supervised training has proven effective in streamlining work methods and enhancing productivity, the availability of forest-machine instructors for supervision is limited. Intelligent coaching systems (ICS) are periodically proposed to address this constraint. ICS are computer-based aids that offer machine operators real-time feedback on their work and guide them on how to rationalize their work. The successful implementation of ICS initially requires the development of systems for automatic work-element detection (AWED). Therefore, this article explores the history, current status, and technological possibilities of AWED. Additionally, key features of ICS are briefly reviewed. Lastly, a broader, interdisciplinary discussion is initiated on how to strategically allocate limited research resources. Questions surrounding the feasible ambition level for ICS and AWED are raised, prompting considerations for the next steps in research and development.

Integrating Cloud Computing, Bayesian Optimization, and Neural-Additive Modeling for Enhanced CAM Systems in 5-Axis Milling

This publication introduces the development and application of an advanced CAD/CAM/CAE system that leverages the computational capabilities of an edge-cloud infrastructure. The developed system consists of containerized technology models for various complex process planning simulation tasks in 5-axis-milling, such as toolpath calculation, cutter-workpiece-engagement, uncut chip geometry or cutting force simulations. Moreover, a Bayesian optimization (BO) algorithm is coupled with the system enabling multi-objective optimizations of the considered machining operations by varying predefined CAM-parameters. The result of the optimization consists of a set of Pareto-efficient solutions. Each solution realizes a different tradeoff between the technological objectives of the process planner. Since mastering the complexity of the design space is a major challenge in today’s CAM programming workflow, a Neural-Additive Model (NAM) is coupled with the system improving guided search through the configuration/result space. This reduces the convergence time to an optimal CAM parameter set. The coupling of the cloud computing, multi-objective optimization and artificial intelligence method with a CAM-kernel is demonstrated based on the process design of 5-axis milling operations for a blade-integrated disk (blisk).

Resource-efficient Edge AI solution for predictive maintenance

Data-driven predictive maintenance (PM) is an approach that leverages advanced analytics, artificial intelligence (AI), and sensor data to predict when equipment failure might occur, and to perform maintenance just in time to prevent it, reducing downtime and maintenance costs. In manufacturing, one of the biggest potential applications for intelligent PM systems is tool condition monitoring (TCM). TCM aims to monitor tool wear in real-time, ensuring the quality of the manufactured products and the safety of the surrounding people and equipment. In recent decades, many studies have been carried out on tool condition monitoring for different machining operations such as milling, drilling or turning, and have demonstrated the effectiveness of various AI algorithms. Recent advances in hardware have made it possible for edge devices to run complex AI algorithms locally. This technology is called Edge AI. The Edge AI approach has several key benefits such as reduced latency, scalability, data privacy and security that can accelerate the integration of the PM solution for TCM at the production level. This paper presents the design of an Edge AI system for tool condition monitoring, consisting of state-of-the-art, low-cost components and using open-source software. Based on the proposed design, a prototype was built and tested during milling process. Four machine learning (ML) models and one deep learning (DL) model were run on a low performance edge device. Their predictions were validated. Challenges faced in the implementation are concluded along with directions and suggestions for future research.

Health Status of Cotton-ginning Workers in Central India: A Cross-sectional Study

Abstract Introduction: Cotton ginning is an important industry in Central India, employing a significant number of workers. The cotton-ginning industry is associated with various occupational health hazards, including exposure to cotton dust, which can lead to respiratory problems such as byssinosis. In addition, cotton-ginning workers are also exposed to workplace stress, which can have negative impacts on their physical and mental health. Aims: The aim of this cross-sectional study is to assess the health status of cotton-ginning workers in Central India. Methodology: A total of 285 cotton-ginning workers from various factories in Central India were recruited for this study. Data on sociodemographic characteristics, occupational characteristics, presence of byssinosis, and workplace stress were collected through a structured questionnaire. The Roach and Schilling criteria for byssinosis were used to assess the presence of this byssinosis. Workplace stress was assessed using the Workplace Stress Scale, which is a validated tool for assessing stress in occupational settings. Data were analyzed using R software 4.2.2. Results: The majority of cotton-ginning workers were female (71.2%) and had a primary level of education (51.3%). Most workers (77.54%) had been employed in the industry for 1–5 years, and the majority of workers (55.8%) worked as helpers. Only 4.21% of workers reported having byssinosis. The majority of workers (63.51%) reported lower levels of workplace stress, while a small proportion (3.16%) reported severe levels of workplace stress. Conclusion: The results of this study suggest that the health status of cotton-ginning workers in Central India is relatively good. However, there is a need to implement appropriate occupational health and safety measures which are inclusive of all workers, not just machine operators.

Ingineering and technological problems of improving the environmental safety of crop production

The article deals with engineering and technological problems of improving the environmental safety of crop production as a complex biotechnical system, for the environment, consumers of crop production and machine operators who ensure production in the field. It is noted that the impact of agriculture on the environment and humans is not only promoting global warming, participation in environmental pollution by pesticides and undigested remnants of mineral and organic fertilizers, the pollution of the production of field crops with residual amounts of pesticides and food is not absorbed nitrogen, toxic threat environment in the area of the machine, but unsustainable use of non-renewable resources, the availability of which depend on agricultural technologies. The main directions of minimizing environmental pollution and reducing dependence on non-renewable resources as a result of agricultural activities are shown.

Correlated translational motions in pseudo-rotaxane complexes controlled by a single chemical stimulus.

Coordinated motions are essential in the operation of molecular machines. This feature can be achieved by landscaping the energy surface along the movement coordinates. Herein, we present an approach of using a single stimulus to modify the free energy curve describing the threading and shuttling of a ring along a linear molecule. This approach has been realized by locating two identical ring-binding sites near the axle termini.

Safety Risk Assessment of an Autonomous Agricultural Machine

Highlights The safety guidelines outlined in ISO 18497 are not sufficient to guarantee the safe operation of autonomous agricultural machines. Since the risk assessment techniques specified in ISO 12100:2012 require historical failure data of the machine at hand, they cannot be used to effectively mitigate the risk associated with autonomous agricultural machines where such data are not readily available. Analysis from the perspective of ergonomics can potentially increase the safety of autonomous agricultural machines. Abstract. The goal of this study was to analyze the safety implications of an autonomous agricultural machine (TerraPreta) using the standards ISO 18497 (ISO, 2018) and ISO 12100:2012 (ISO, 2012), as well as to investigate the ergonomics associated with the use of the autonomous agricultural machine. First, three engineers involved in the robot's manufacturing process were asked to evaluate the robot's functionalities compliance with the applicable safety standards and protective measures outlined in standard ISO 18497 (ISO, 2018). Second, while the robot was planting cover crop seeds, an attempt was made to identify and evaluate every risk connected to the robot using the risk assessment techniques outlined in ISO 12100:2012 (ISO, 2012). (1) Half (50%) of the functionalities of the autonomous agricultural machine complied with the safety requirements and protective measures described within the standard ISO 18497 (ISO, 2018). (2) The heavy reliance on past incident data of the risk assessment procedure described within the standard ISO 12100:2012 (ISO, 2012) makes it ineffective for new and revolutionary technologies such as autonomous agricultural machines where such data are not available. (3) Lifting a bag to fill the robot hopper with seeds was found to be a moderately hazardous activity associated with human-robot interaction. Multiple tentative solutions were provided to avoid this moderately hazardous activity. Keywords: Autonomous agricultural machine, Ergonomics, Machine safety, NIOSH lifting equation, Risk assessment, Standards.

Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)

Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.

ФОРМИРОВАНИЕ ПРОФЕССИОНАЛЬНЫХ НАВЫКОВ СПЕЦИАЛИСТОВ ПО ЭКСПЛУАТАЦИИ ТРАНСПОРТА В СООТВЕТСТВИИ С ТРЕБОВАНИЯМИ РЫНКА ТРУДА

The article presents the experience of automated analysis of the corpus of job advertisements relevant for graduates of the training direction 23.03.03 «Operation of transport and technological machines and complexes». As part of the study, 20516 vacancies in the automotive business and related sub-sectors were collected and analyzed. The location of vacancies, the aver-age salary are determined, the most frequent requirements for applicants are identified. The results of the analysis are correlated with the main provisions of the educational program, on the basis of which conclusions are drawn about the need for systematic consideration of employers' require-ments to improve the quality of training specialists.

Life under tension: the relevance of force on biological polymers.

Optical tweezers have elucidated numerous biological processes, particularly by enabling the precise manipulation and measurement of tension. One question concerns the biological relevance of these experiments and the generalizability of these experiments to wider biological systems. Here, we categorize the applicability of the information garnered from optical tweezers in two distinct categories: the direct relevance of tension in biological systems, and what experiments under tension can tell us about biological systems, while these systems do not reach the same tension as the experiment, still, these artificial experimental systems reveal insights into the operations of biological machines and life processes.

Controller design and performance study of DC-DC bidirectional converter for speed regulation of permanent magnet direct current machine

This paper presents the design of a controller for a bidirectional DC-DC converter for speed control of a permanent magnet DC machine (PMDC). In recent years, this electric drive system has often been used in electric vehicles or hybrid electric vehicles. In this application, the development of a controller is required to support the various functions of the machine. The design methodology of the controller should provide satisfactory dynamic performance under both motor and generator operation of the PMDC machine. In this regard, the control of the bidirectional DC/DC converter is necessary. From the perspective of speed control, the condition for absolute stability is derived from the small-signal model of the PMDC drive system. Then the Ziegler-Nichols tuning table is used to determine the gain of the proportional-integral (PI) controller in the conventional way. From further investigation, it was found that the transient response and stability margin of the speed control loop in the conventional case are

ОПРЕДЕЛЕНИЕ ПОЦИЛИНДРОВОЙ МОЩНОСТИ ДВИГАТЕЛЯ ВНУТРЕННЕГО СГОРАНИЯ ПРИ РАБОТЕ В РЕЖИМЕ СВОБОДНОГО РАЗГОНА

Efficient operation of transport and technological machines for agricultural purposes is impossible without a timely assessment of the energy performance of their internal combustion engines. The torque and power of each cylinder can be estimated by the magnitude of the reactions of the engine support when operating in free acceleration mode with some of the cylinders disconnected. (Research purpose) The research purpose is improving the method of determining the cylinder-by-cylinder power of an internal combustion engine in the free acceleration mode by the magnitude of the reactions on its supports. (Materials and methods) Developed a methodology for estimating the power of each engine cylinder by the magnitude of the reactions on its supports and the current value of the crankshaft speed. Experimental studies were carried out on a diesel four-cylinder four-stroke engine D-243. During the execution of the research program, the fuel supply to one or two cylinders was gradually turned off, the engine operation mode was set to free acceleration, and forces arising in the supports were recorded using strain gauges and a set of measuring equipment. The following research methods were applied: a priori ranking, regression and computational analysis. (Results and discussion) It was found that the maximum torque is reached in the crankshaft speed range of 1298-1467 revolutions per minute and is 285 nanometers when all cylinders are running, 219 - when three cylinders are running and 148 - when two cylinders are running. The maximum value of the effective power was 57.8 kilowatts for all cylinders, 34 for three cylinders and 22 for two cylinders. It was determined when assessing engine power during the alternate shutdown of one of the cylinders that the effective engine power varies in the range of 33-33.4 kilowatts. (Conclusions) The obtained results of experimental studies can be used in the implementation of a method for operational control of the power of each cylinder of an internal combustion engine under operating conditions.

Failures and methods of ensuring the service life of friction discs of gearboxes

The resource of a complex machine is determined by the resource of its units and components. As a result of the normal operation of energyintensive mobile vehicles of the K-7 series, it was established that the service life of the hydromechanical gearbox does not exceed 3000 engine hours, and the experimental probability of its failure is more than 0.55 of all machine units. The cause of failure of the hydromechanical gearbox is failure of the clutches and drive shaft. Failures of friction clutches are associated with significant overheating of the friction discs, their wear, welding and changes in spatial geometry. In the practice of normal operation of mobile machines, the drive shaft is repaired or replaced with a new one. Failure of friction discs occurs due to changes in spatial geometry (experimental probability of failure - 0.76), wear (experimental probability of failure - 0.63), destruction (experimental probability of failure - 0.38) and welding (experimental probability of failure - 0.34) friction discs. The work examines the use of the method of restoring friction discs by flat dimensional grinding, and conducts comparative life tests of restored friction discs.

Uncorrelated Sparse Autoencoder With Long Short-Term Memory for State-of-Charge Estimations in Lithium-Ion Battery Cells

For the safe and reliable operation of battery-driven machines, accurate state-of-charge (SOC) estimations are necessary. Unfortunately, existing methods often fail to identify patterns relevant to long-term SOC estimation due to complex battery cell characteristics such as aging. In this paper, we propose the Uncorrelated Sparse Autoencoder with Long Short-Term Memory (USAL). USAL is a novel neural network that addresses the challenging task of long-term SOC estimation given a limited initial history of a cell’s charge-discharge behavior. USAL uses a multi-task learning strategy to harness the advantages of sparse autoencoders and Long Short-term Memory (LSTM) networks by enforcing correlation penalties. The USAL simultaneously learns to (i) generate a latent space of informative SOC encodings from commonly measured cell characteristics, (ii) penalize for high multicollinearity between encodings, and (iii) identify non-trivial long and short temporal correlations between the encodings using LSTM cells. USAL outperforms benchmarked models in our experiments when trained on five initial charge-discharge cycles across multiple battery cells using three publicly available accelerated aging datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes USAL, a custom-built deep neural network to address the challenging task of long-term SOC estimations in battery cells. Long-term SOC estimation involves estimating SOC for cycles near End-Of-Life (EOL) given some initial charge-discharge cycles. Three fundamental steps involved in long-term SOC estimations using USAL are (i) exploiting a multi-task learning strategy to learn efficient encodings given limited training data, (ii) penalizing these encodings for high correlations to efficiently transform measured inputs into a space of informative features, and (iii) mapping of aging-related trends to support long-term SOC estimations. USAL is designed to be a data-driven SOC estimation method that is (i) capable of alerting the user to a faulty cell when integrated into a real-life Battery Management System (BMS) and (ii) identifying the relative quality of a battery cell from only a few initial charge-discharge cycles.

Integrated Genetic Algorithm with Dispatching Rules to solve the Flexible Job Shop Scheduling Problem under Multi-AMR Transportation Constraints

The Flexible Job Shop Scheduling Problem (FJSSP) is a challenging issue for industries and manufacturers. However, the transportation tasks in this problem should not be underestimated, as their impact is substantial, and they are frequently overlooked in the literature. In this paper, the FJSSP with multi-AMR (Autonomous mobile robot) transportation constraints is addressed. We developed a genetic algorithm with dispatching rules to solve the Flexible Job Shop Scheduling Problem with Multi-AMR Transportation Constraints by minimizing the total completion time. Two dispatching rules are used to assign transportation robots. The first rule is random assignment and the second rule is AMR per Job assignment. Our encoding and decoding process utilizes a three-layer sequence (machine sequence, operation sequence, and AMR sequence). Finally, we conducted comprehensive experiments on different benchmarks to assess the performance of the proposed algorithm with a variable number of AMR to transport the operations. The experimental results indicate that the proposed algorithm effectively and efficiently solve the problem.

Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review

Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its effectiveness, traditional OR, especially exact methods, often struggle with scalability, computational efficiency, and adaptability to the dynamic and uncertain environments of Industry 4.0. While machine learning (ML) advancements provide novel approaches for addressing these challenges, they also present limitations, such as the lack of guaranteeing exact solutions and the need of relevant data. Therefore, the integration of OR and ML offers a balanced solution, leveraging ML’s capability to extract patterns from large datasets and making predictive decisions and OR’s precision to enhance decision-making processes, especially in scheduling tasks withing the context of Industry 4.0. This combination not only improves solution robustness and efficiency but also mitigates individual limitations of both fields. and make predictive decisions under uncertainty complements the decision-making process of OR. This paper aims to conduct a bibliometric analysis and a brief literature review on the integration of ML and OR, focusing on their application in scheduling problems.

Effect of cryotreated post tempered electrodes on EDM performance of AISI 304

In the present investigation, a comparison study has been conducted to investigate the effect of process parameters and three different type of electrodes {untreated (UT), cryotreated single tempered (CT1) and cryotreated double tempered (CT2)} during electro discharge machining (EDM) operation. For this purpose, WC and AISI 304 stainless steel was used as electrode and work material. Peak current, pulse on time, duty cycle, gap voltage, and flushing pressure were considered as process parameters and performance characteristics were measured by means of tool wear rate (TWR), material removal rate (MRR) and surface roughness (Ra). Microstructural analysis was conducted on the EDMed surfaces machined by UT, CT1 and CT2 electrodes. It is found from the investigation that UT electrode provides higher tool wear rate (TWR), material removal rate (MRR), and surface roughness (Ra) compare to cryotreated post tempered electrodes. Better surface quality has been achieved for EDMed surface machined by CT2 electrode compare to UT and CT1 electrodes.

Power prediction and operation scheduling strategy of pumped storage power station based on machine learning

The power of a pumped storage power station depends on the water level of the upstream reservoir and the downstream demand. The change in water level is related to rainfall, evaporation, river inflow, and other factors. Therefore, it is difficult to accurately predict the generating power and its scheduling. A machine learning-based power prediction and operation scheduling strategy for pumped storage power plants is proposed. The relationship between the forgetting gate, input gate, output gate, and memory unit of the long and short-term memory network is redefined. The activation function between input data and output data is constantly updated, which is used as the power prediction function of the pumped storage power station. The power prediction model is built. The operation scheduling process of the pumped storage power station is designed. The operation scheduling of the pumped storage power station is completed by using the daily scheduling time scale. The experimental results show that the research method can accurately predict the power generation of a pumped storage power station. The power generation is more balanced, and the power scheduling effect is better under the application of the research method.