Reliability Analysis about A/C-Axis of Woodworking Five-Axis Milling Head

The writers of current standards and specifications for load and resistance factor design (LRFD) envisioned that those documents would be used by designers in a manner similar to that used in prior (allowable stress design) formats. They presumed that designs would be accomplished by satisfying a series of checking equations in which the structural resistance exceeds the demand on the structure. While these early developers used reliability analysis tools to derive the load and resistance factors, they did not envision that reliability analysis would evolve into a tool to be directly used by designers. In spite of those early presumptions, the evolving reality is that reliability analysis procedures are now available to an increasing number of individual designers. Sooner or later, these designers will attempt to use reliability analysis to assist them in their project-specific or, more likely, product-specific designs. This paper discusses limitations and potential pitfalls of reliability analysis when used in routine structural design. Specific examples focus on wood design. The conclusion reiterates the premise that LRFD, not theoretical reliability analysis, is the proper tool for use in daily design.

Production machine maintenance is an activity to maintain the machine's reliability to operate as planned. Apart from reliability, evaluating the effectiveness of the machine is also equally important. Therefore, this research has two main objectives: determining the system's criticality and increasing the effectiveness value of using forklifts using reliability analysis and machine's effectiveness (ME). The research began with collecting operation and maintenance data to calculate the initial conditions of machine reliability and effectiveness. The initial reliability calculation results were 36.79%. Availability is 98.89%, performance is 93.53%, and ME value is 92.49%. The performance value is still below world-class standards, so it needs to be improved. Next, rank the problem using the Pareto diagram for determining system criticality. Efforts to increase performance values to reach world-class standards are carried out by analyzing maintainability and implementing a policy that maintainability values are at least 50% for forklifts and their critical systems. As a result, the availability value increased to 99.74%, the performance value increased to 95.57%, and the ME value increased to 95.32%. So availability, performance, and ME meet the requirements because they exceed world-class standards. Therefore, companies can implement a forklift maintenance policy using maintainability of at least 50% reliability to increase the value of availability, performance, and ME. Keywords: Forklift, Reliability Analysis, Machine’s Effectiveness, Maintainability

As one of the two main drive equipment of high end CNC machines, the main axis driving system decides mostly the performance. The direct-driving Rotary Motor and its drive's performance and reliability also influence directly the performance of the whole CNC machines. This thesis mainly introduces the application of the DDR motor into the YK7432B CNC GEAR SHAVING CUTTER GRINDING MACHINE, as well as the optimized design for the DDR motors. The DDR motor could make continuous torque of 630Nm, and it's also optimized from the slot pole cooperation, the structure and manufacturing process, which has been successfully applied to the YK7432B system.

Based on the function structure tree ,the spot failure data of CNC grinding machine were analyzed and found out that of the all failure positions in the subsystem of the CNC machine was High-frequency Motor Built-in Spindle an high failure frequent and main criticality failure position . By further in detail analysis found out that the main failure cause of the CNC grinding machine was burning out of components. It was defined the main direction to improve the reliability of this CNC grinding machine and put forward the corresponding improvement measures. It can offer a theory basis for the reliability design and analysis of CNC machine tool .

Estudio de validación de un instrumento de evaluación postural (SAM, spinal analysis machine)

Vehicle movement may critically influence ride comfort, so reliable analysis and simulation systems are needed in the design of a vehicle-occupant system. In order to predict the performance of a vehicle-occupant system, it is necessary to consider the vehicle and the occupant as a dynamically coupled oscillation. This paper is concerned with theoretical analysis and verification of the simulation system of vehicle-occupant dynamic interaction. The mathematical model has twelve degrees of freedom : a vehicle model with four masses is six degrees of freedom, and an occupant model with four masses, the hinged beam model, six degrees of freedom. The random roughness of an actual highway surface has been taken into account in the simulation system. Simulated results almost agreed with experimental values. This simulation system can be used as a tool to investigate the effects of physical variables that influence automobile seat ride comfort. The effects of seat characteristics, stable posture of seat system for passengers, and engine shake on human dynamic behavior can be investigated.

Loading and hauling machines are of the most important operational units in open pit mines, which their failure can lead to reduction in production and profitability of mines. Therefore, it is very important to do a reliability and maintainability analysis of mining machines in order to make a better decision with respect to operation and maintenance of machines as well as production control in the mine. Awareness of transport units operational conditions and precise control over them are the key elements for preventing unwanted stops and improving the performance of machines. The aim of this study is to evaluate the reliability and maintainability of loading and hauling equipment in Golegohar Mine No. 1 in Iran, which produce 12 million tons of iron ore annually. For this purpose, the failure data of transport units including dump trucks, mining shovels and backhoe excavators were collected over a period of one year. Then the data were analyzed based on the statistical method to produce the reliability and maintainability parameters of each mining equipment. The time corresponding to reliability and maintainability levels of 90 and 50 percent are also calculated and presented.

PurposeThe purpose of this paper is to provide results for a complete reliability, availability, and maintainability (RAM) analysis utilizing data sets from a production system in a wine packaging line. Through the illustrated case study, the author demonstrates how RAM analysis is very useful for deciding maintenance intervals, and for planning and organizing the adequate maintenance strategy.Design/methodology/approachRAM analysis has been done for each machine by using failures data. The parameters of some common probability distributions, such as Weibull, exponential, lognormal, and normal distributions, have been estimated by using the Minitab software package. An investigation to determine which of these distributions provide the best fit for characterizing the failure pattern at machine and line level has been made. Reliability and maintainability of both wine packaging and its machines has been estimated at different mission times with their best fit distribution. High maintainability issues and potential factors with their potential failure modes were presented, through failure mode and effect analysis process.FindingsAnalysis of the total downtime, breakdown frequency, reliability, and maintainability characteristics of different machines shows that: first, the availability for the wine packaging line was 91.80 percent, and for the remaining 8.2 percent the line is under repair. Second, about two failures per shift are displayed on the line, whereas for the mean time-to-repair (TTR) a failure is 24 minutes. Third, there is no correlation between the time-between-failures and the TTRs for the wine packaging line. Fourth, the main three factors affecting the maintainability process in the production line are: resources availability, manpower management, and maintenance planning procedures.Originality/valueThis study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis in the much advertised field of wine packaging production line which on the other hand so little has been published on operational availability and equipment effectiveness. It can also be useful to serve as a valid data source for winery product manufacturers, who wish to improve the design and operation of their production lines.

As commonly supporting parts of the spindle and the feed systems on the high-end grade CNC machine tool, the dynamic characteristics of angular contact ball bearings had been a research hot spot in the current. Aiming at solving the problem that obtained dynamic stiffness and damping of angular contact ball bearings, an approach to investigate dynamic parameters of bearings joint surfaces was proposed based on the single degree of freedom (SDOF) system, and several groups of modal testing were carried out. The dynamic characteristics of bearings joint surfaces were viewed as several viscoelastic units of spring and damper. Then, models of bearings dynamic parameters in the axial and radial directions were established, and several groups of modal simulation were carried out. The comparison of experiment and simulation results showed that the proposed approach could accurately identify dynamic stiffness and damping of bearings under different load conditions, and the errors were controlled within 7.6%.

Bayesian approaches have been demonstrated as effective methods for reliability analysis of complex systems with small-amount data, which integrate prior information and sample data using Bayes’ theorem. However, there is an assumption that precise prior probability distributions are available for unknown parameters, yet these prior distributions are sometimes unavailable in practical engineering. A possible way to avoiding this assumption is to generalize Bayesian reliability analysis approach by using imprecise probability theory. In this paper, we adopt a set of imprecise Dirichlet distributions as priors to quantify uncertainty of unknown parameters and extend traditional Bayesian reliability analysis approach by introducing an imprecise Dirichlet model (IDM). When the prior information is rare, the result of imprecise Bayesian analysis method is too rough to support engineering decision-making, so we proposed an optimization model to reduce the imprecision of the new method. Spindles are crucial for machine tools and reliability data related to spindles of new-developed machine tools are often rare. We can then use the imprecise Bayesian reliability analysis method to assess its reliability. In this paper, we mainly investigate the reliability assessment of a motorized spindle to illustrate the effectiveness of the proposed method.

This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination (R2=99.3%) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (βT=3.5).

With the theories of reliability mathematics, reliability system engineering and machine design, the paper presents the reliability analysis on the electrode plates driving system of the water resistance test system of high speed diesel locomotive. Stress - strength interference theory and item stress analysis are applied to its mechanical and electrical parts to derive reliability calculation formulae. The probability models of system life are established and the reliability prediction is worked out. The life curve is arrived to indicate the relations between the reliability and the working hours of the system. The results show that the system is of high reliability, which declines with the increasing of working hours, and is mainly influenced by reliability of electric parts, with an enough life in mechanical parts.

CNC machine tools are essential in determining the quality of machined components and improving the manufacturing process. This article discusses geometric errors, structural rigidity, thermal effects, motion accuracy, 2D and 3D trajectories and motion precision. Modern solutions such as automatic spindle calibration, error compensation through advanced algorithms, adaptive control and the use of intelligent sensors are presented in this article. The need for a general approach, vibration tuning and dynamic error compensation is emphasised in the paper. The results show that artificial intelligence and advanced control technologies can significantly improve the accuracy and reliability of CNC machines, enabling the production of high quality parts. This is due to the stringent requirements of the industry. In addition, the analysis highlights that optimisation of positioning systems and real-time monitoring can significantly reduce machining inaccuracies. These solutions can improve the effectiveness of CNC machines, resulting in reduced production costs and increased process efficiency.

Predictive maintenance (PdM) in CNC machine tools has gained significant attention due to its potential to reduce downtime and increase operational efficiency. This paper explores automated diagnostic techniques used in predictive maintenance for CNC machines, focusing on fault detection, prognosis, and decision-making processes. With the advent of Industry 4.0, intelligent sensors and data analytics have revolutionized machine monitoring, enabling the prediction of failures before they occur. The integration of real-time monitoring systems, vibration analysis, acoustic emission monitoring, and machine learning algorithms has proven effective in diagnosing potential failures. This paper provides an in-depth review of various approaches to automated diagnostics, highlighting the advancements in machine learning models, artificial intelligence (AI), and the Internet of Things (IoT) technologies in enhancing PdM. The research also examines the challenges associated with implementing PdM systems, such as data acquisition complexities, sensor calibration, and computational costs. Through case studies and application examples, the paper discusses how these diagnostic systems can optimize maintenance schedules, minimize machine downtime, and improve overall productivity. The objective of this research is to provide a comprehensive understanding of automated diagnostic methods and their role in predictive maintenance systems for CNC machine tools. The hypothesis posits that the application of advanced diagnostic techniques using AI and IoT will lead to a significant reduction in unplanned downtime and maintenance costs. The findings suggest that the continued development of predictive maintenance systems can lead to improved reliability and lifespan of CNC machines, benefiting industries where precision and continuous operation are critical.

The lattice Boltzmann method (LBM) has been formulated as a powerful numerical tool to simulate incompressible fluid flows. However, it is still a critical issue for the LBM to overcome the discrete effects on boundary conditions successfully for curved no-slip walls. In this paper, we focus on the discrete effects of curved boundary conditions within the framework of the multiple-relaxation-time (MRT) model. We analyze in detail a single-node curved boundary condition [Zhao etal., Multiscale Model. Simul. 17, 854 (2019)10.1137/18M1201986] for predicting the Poiseuille flow and derive the numerical slip at the boundary dependent on a free parameter as well as the distance ratio and the relaxation times. An approach by virtue of the free parameter is then proposed to eliminate the slip velocity while with uniform relaxation parameters. The theoretical analysis also indicates that for previous curved boundary schemes only with the distance ratio and the halfway bounce-back (HBB) boundary scheme, the numerical slip cannot be removed with uniform relaxation times virtually. We further carried out some simulations to validate our theoretical derivations, and the numerical results for the case of straight and curved boundaries confirm our theoretical analysis. Finally, for fluid flows with curved boundary geometries, resorting to more degrees of freedom from the boundary scheme may have more potential to eliminate the discrete effect at the boundary with uniform relaxation times.