Reliability assessment of cutting tool life based on surrogate approximation methods

A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Surrogate modelling for reliability assessment of cutting tools

Currently, cutting tool life for machining operations is correlated to process parameters through the widely applied Taylor functions. The latter are valuable expressions in established practice however their generalised nature does not allow accurate prediction of the tool’s service life or optimization of the manufacturing process due to effects of uncertainties in various input variables. These variables should be treated in a stochastic way in order to avoid employment of safety factors for quantification of uncertainty. This paper documents a procedure that allows derivation of analytical expressions for cutting tools performance employing advanced approximation methods and concepts of reliability analysis. Due to the complexity of manufacturing processes surrogate modelling (SM) methods are applied, starting from a few sample points obtained through lab or soft experiments and extending them to models able to predict/estimate the values of control values/indicators as a function of the key design variables, often referred to as limit states

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Method of determining the process applied for feature machining : experimental validation of a slot

In this paper, we will be evaluating the "manufacturability" levels for several machining processes of "slot" feature. Using the STEP standard, we will identify the slot feature characteristics. Then, using the ascendant generation of process method, we will define the associated milling process. The expertise is based on a methodology relative to the experience plans carried out during the formalization and systematic evaluation of the machining process associated with the feature

Design of a Solar Panel Deployment and Tracking System for Pocketqube Pico-Satellite

Modularized small satellites will have even greater potential with better energy supply. In this paper, a PocketQube solar panel deployment and tracking system will be presented. The system is designed for a 3P PocketQubes. During the designing phase, trade-off analysis is done to meet the balance of weight, dimension and efficiency. Reliability, manufacturability, and cost are also considered from the beginning, as commercial production and launch are expected. The CAD design, dynamics analysis, motion simulation, and rendering for the project are undertaken by Solidworks, whereas Abaqus CAE is utilized for the finite element analysis of the vibration test of the panels. In the gimbal subsystem, we use two micro stepper motor to drive the panels via a two-axis gearbox, enabling the panels to track the sun omnidirectionally. In the panel subsystem, two types of customized spring hinges are designed. Robust and verified parts, such as burner resistors, are chose for the control and deployment system. After the continuous optimization process throughout the design phase, by comparing different manufacturing processes technologies, materials, and design details, the full scale prototypes of the gimbal subsystem were built and tested. In the end, the most feasible solution, as well as the suggestions for the development, were put forward