Computer Numerical Control Machine Research Articles

A Comparison between Powder Bed Fusion of Polyamide 12 and Aluminum Computer Numeric Control Machining: A Carbon Footprint and Energy Assessment

This study presents a comparative life cycle assessment (LCA) of two manufacturing scenarios for a camera housing: Powder Bed Fusion (PBF) using polyamide 12 (PA-12) and Computer Numeric Control (CNC) machining using aluminum, for a cradle-to-gate boundary. The selected impact categories were cumulative energy demand (CED) and global warming potential (GWP). The key findings indicate that the PA-12 PBF part outperformed the aluminum CNC machining one in terms of environmental and energy performance, showing a significant reduction of approximately 90% in equivalent carbon emissions and 84% in cumulative energy demand. Sensitivity analyses revealed that the PBF process was highly sensitive to changes in the proportion of virgin/recycled material for the printing process, variations in the life cycle inventory (LCI) data sources for PA-12 powder production, and changes in the transportation system for imported materials, as is the case for the main input in the process (PA-12 powder). Sensitivity analysis also showed less impact for the PBF camera housing even considering a lifespan of one-fifth that of the aluminum for the impact categories considered. However, it should be noted that this analysis did not include considerations for the usage and end-of-life phases, which may have significant contributions to the overall environmental impact.

CNC machine gearbox fault detection with convolutional neural network

This study focuses on the development of a deep learning-based approach of gearbox monitoring and fault detection. The project aims to create a solution for early detection of defects in dynamic equipment based on data from vibration sensor by building a binary classifier with convolutional neural network implemented. The gearboxes condition of which is being assessed is stored in three similar computer numerically controlled (CNC) milling machines. Data is collected during 15 milling operations of different duration and with different tool’s speed and feed. Vibration is measured by an accelerometer stored on the body of each gearbox. Convolutional neural network takes vibration spectra as inputs and whether fault is detected makes a prediction of a gearbox condition. To make the whole solution autonomous and be able to embed it into manufacture the project is integrated into a server with an edge-to-cloud architecture. As an end product deep learning fault classifier stored on a server is to detect possible gearbox faults, draw conclusions on condition of dynamic equipment and automate the process of fault detection.

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

Computer Numeric Control (CNC) five-axis milling plays a significant role in the machining of precision molds and dies, aerospace parts, consumer electronics, etc. This research aims to explore the potential of the machine learning (ML) technique in improving energy efficiency during the CNC five-axis milling process for sustainable manufacturing. The experiments with various machining parameters, forms of toolpath planning, and dry cutting conditions were carried out, and the data regarding energy consumption were collected simultaneously. The relationship between machine parameters and energy consumption was analyzed and built. Subsequently, a machine learning algorithm was developed to classify test methods and identify energy-efficient machining strategies. The developed algorithm was implemented and assessed using different classification methods based on the ML concept to effectively reduce energy consumption. The results show that the Decision Tree and Random Forest algorithms produced lower Root Mean Square Error (RMSE) values of 4.24 and 4.28, respectively, compared to Linear, Lasso, and Ridge Regression algorithms. Verification experiments were conducted to ascertain the real-world applicability and performance of the ML-based energy efficiency approach in an operational CNC five-axis milling machine. The findings not only underscore the potential of ML techniques in optimizing energy efficiency but also offer a compelling pathway towards enhanced sustainability in CNC machining operations. The developed algorithm was implemented within a simulation framework and the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of the machining results and integrating both conventional and advanced regression algorithms into CNC machining processes. Manufacturers stand to realize substantial energy savings and bolster sustainability initiatives, thus exemplifying the transformative power of ML-driven optimization strategies.

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

In current five-axis computer numerical control (CNC) machining, the use of minute linear path segments as an approximation for the ideal cutter contacting (CC) point trajectory is still prevalent. However, introducing rotation axes leads to a deviation of the actual CC point trajectory from the ideal, resulting in nonlinear errors. An integrated method is proposed in this paper for compensating and correcting both the contour error, associated with the approximation of the part surface by the ideal CC point trajectory and the nonlinear error of the CC point trajectory based on the information in the CC point data. By analyzing the spatial relationship between the tool posture and the CC point path during the five-axis linear interpolation process, two adjacent machining tool positions containing CC point data information are selected as the starting and ending points of the five-axis linear interpolation machining. The ideal tool center point and the actual CC point are calculated during the interpolation process, as well as the distance and the unit vector in the perpendicular direction between the actual CC point and the ideal CC point trajectory segment. In the comprehensive error compensation and correction phase, the obtained unit vectors are used as direction vectors for error compensation, and the tool center point during interpolation is first compensated and corrected. This ensures the actual CC point and the contour curve are on the same plane. The compensation direction for contour error is calculated using the start/end tool axis vectors and the ideal CC point trajectory vectors. The size of the contour error approximating the contour curve is calculated through the chord error. A second compensation and correction are applied to the tool center point for interpolation, ultimately achieving comprehensive compensation and correction of nonlinear errors. The data calculations were conducted in the MATLAB environment using actual machining data. After compensation and correction, the contour error was reduced by 76%, the nonlinear error of the CC point trajectory decreased to below 0.88 μm, and the comprehensive nonlinear error of the CC point trajectory was reduced from 19 to 1.5 μm, a reduction of 93%. This demonstrates significant practical value in enhancing the accuracy of five-axis CNC machining. Through actual machining verification, after using the method described in this paper, the average surface roughness decreased from 1.133 to 0.220 μm, and the maximum surface roughness decreased from 6.667 to 1.240 μm. This significantly demonstrates that the compensation and correction method proposed in this paper can significantly improve the surface quality of machined parts.

A low‐profile circularly polarized cavity‐based waveguide aperture antenna

AbstractThis article proposes a new design of a circularly polarized (CP) aperture antenna based on a dual‐mode rectangular waveguide cavity‐resonator. A capacitive iris is employed to attain a strong coupling between the cavity and the input port, resulting in a larger fractional bandwidth (FBW) than a conventional aperture antenna. Another capacitive iris is embedded at the corner of the radiating aperture and extended into the cavity to maintain equal amplitude and a phase difference of 90° between the radiated electric (E) fields of the TE101 and TE110 modes, and thus yielding CP radiation. The proposed antenna has been fabricated using a computer numerical control machine. The antenna has an FBW of 18% at center frequency (f0) f0 = 10 GHz when the reflection coefficient (S11) S11 = −10 dB. The 3‐dB axial ratio bandwidth is 7%. A gain of 8.0 dBic is found at 10.06 GHz. The low profile and competitive performance of the antenna make it a good candidate in radar applications.

Chairside digital design and manufacturing method for children's band and loop space maintainers.

This study proposes a chairside digital design and manufacturing method for band and loop space maintainers and preliminarily validates its clinical feasibility. Clinical cases of 10 children requiring space maintenance caused by premature loss of primary teeth were collected. Intraoral scan data of the affected children were also collected to establish digital models of the missing teeth. Using a pediatric band and loop space maintainer design software developed by our research team, a rapid personalized design of band and loop structures was achieved, and a digital model of an integrated band and loop space maintainer was ultimately generated. A chairside space maintainer was manufactured through metal computer numerical control machining for the experimental group, whereas metal 3D printing in the dental laboratory was used for the control group. A model fitting assessment was conducted for the space maintainers of both groups, and senior pediatric dental experts were invited to evaluate the clinical feasibility of the space maintainers with regard to fit and stability using the visual analogue scale scoring system. Statistical analysis was also performed. The time spent in designing and manufacturing the 10 space maintainers of the experimental group was all less than 1 h. Statistical analysis of expert ratings showed that the experimental group outperformed the control group with regard to fit and stability. Both types of space maintainers met clinical requirements. The chairside digital design and manufacturing method for pediatric band and loop space maintainers proposed in this study can achieve same-day fitting of space maintainers at the first appointment, demonstrating good clinical feasibility and significant potential for clinical application.

Classification of CNC Vibration Speeds by Heralick Features

In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co-occurrence matrices. The culmination of this methodological framework is the identification of discernible co-occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10-fold cross-validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000  24000  26000  28000  and 30000 RPM. 

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

Movement Strategy Influences on the Characteristics of Low-Carbon Steel Generated by the Lamination Object Manufacturing Method

This paper investigates the effects of heating movement techniques on the properties of low-carbon steel samples that are 3D printed using S20C lamination object manufacturing (LOM). A Tungsten iner gas (TIG) machine and a computer numerical control (CNC) machine were used together to join the steel sheet. The LOM samples were created with a straight-profile, short-profile, cross-profile, and curved-profile. The results indicate that the majority of the samples had a grain size number of 7–9. The samples exhibited an isotropy grain shape. The LOM samples exhibited dimples, which suggests ductility fractures. Pore flaws showed up in the microstructure of the cross-profile and short-profile samples during the LOM process. The samples with curved- and straight-profiles had a better microstructure. In comparison to samples with a short profile and a cross-profile, the samples with a straight-profile and a curved-profile had a superior combination of ultimate tensile strengths (UTSs) and elongation value. The straight- and curved-profiles’ greater elongation and tensile strength can be attributed to their improved microstructure and finer grain size. A straight-profile sample with an elongation value of 25.6% and a UTS value of 430 MPa was the ideal LOM sample. Conversely, the weakest sample was the LOM sample with a cross-profile, which had an elongation value of 10.8% and a UTS value of 332.5 MPa. This research could provide further information about the LOM method and the best straight-profile movement strategy. A suitable TIG gun movement strategy could produce a good LOM sample with a good microstructure, tensile strength, and ductility. Further research should incorporate more movement strategies and techniques that completely prevent the formation of pore defects.

Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning

The quality of multiple-machining-feature parts in batch computer numerical control machining systems is affected by complex spatial-temporal relationships, making accurate prediction difficult. Traditional quality prediction methods are difficult to cope with the challenges posed by the time impact effects generated by batch production sequences and the spatial impact effects generated by the adjacency of machining features of thin-walled parts. Hence, the time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning is proposed to analyze the spatial-temporal impact relationship of machining quality and achieve accurate quality prediction. Specifically, a graph learning-based framework to capture the coupled spatial-temporal effects is designed to represent the spatial topological and temporal relationship of the machining features of batch parts. A novel global-local multi-task spatial-temporal graph learning framework is constructed to simultaneously predict the time series quality of each machining feature at different spatial positions. The global multi-task model aims to extract spatial-temporal coupling data features (i.e., input parameters for machining features at different positions of batch parts) for machining feature clusters based on gated networks and spatial-temporal graph convolution with multi-head attention. Then, the local multi-task model for each feature cluster (round holes, rectangular grooves, etc.) performs individual time series quality prediction regarding multiple quality indicators. Experimental results on a production dataset of a computer numerical control machining system for thin-walled parts show that the proposed method is superior to the conventional models such as a simple spatial-temporal graph convolution method (3.0%, 18.3% and 11.0% improvement in R2, MAE and RMSE) or the model without graph approach (6.5%, 29.8% and 19.8% improvement in R2, MAE and RMSE).

Evaluating Technicians' Workload and Performance in Diagnosis for Corrective Maintenance.

The advancement in digital technology is transforming the world. It enables smart product-service systems that improve productivity by changing tasks, processes, and the ways we work. There are great opportunities in maintenance because many tasks require physical and cognitive work, but are still carried out manually. However, the interaction between a human and a smart system is inevitable, since not all tasks in maintenance can be fully automated. Therefore, we conducted a controlled laboratory experiment to investigate the impact on technicians' workload and performance due to the introduction of smart technology. Especially, we focused on the effects of different diagnosis support systems on technicians during maintenance activity. We experimented with a model that replicates the key components of a computer numerical control (CNC) machine with a proximity sensor, a component that requires frequent maintenance. Forty-five participants were evenly assigned to three groups: a group that used a Fault-Tree diagnosis support system (FTd-system), a group that used an artificial intelligence diagnosis support system (AId-system), and a group that used neither of the diagnosis support systems. The results show that the group that used the FTd-system completed the task 15% faster than the group that used the AId-system. There was no significant difference in the workload between groups. Further analysis using the NGOMSL model implied that the difference in time to complete was probably due to the difference in system interfaces. In summary, the experimental results and further analysis imply that adopting the new diagnosis support system may improve maintenance productivity by reducing the number of diagnosis attempts without burdening technicians with new workloads. Estimates indicate that the maintenance time and the cognitive load can be reduced by 8.4 s and 15% if only two options are shown in the user interface.

Адаптация системы ЧПУ станка к условиям комбинированной обработки

Introduction. Increasing the efficiency of processing technologies for products made from modern high-strength, difficult-to-process materials with increased physical, mechanical and operational properties consists not only in improving the technology itself, the tools for its implementation, but also in modernizing technological equipment taking into account new achievements in the field of mechanical engineering. Modern computer numerical control (CNC) equipment is now quite advanced in terms of controlling basic cutting movements. Adaptive monitoring and control systems, as a rule, additionally installed on processing equipment, make it possible to further improve the quality of processing parameters. With the development of new hybrid and combined technologies that combine several types of influence on the product being processed, the issue of synchronizing the automatic control of the movements of parts of technological equipment with the control and management of accompanying processes of combined technologies has become acute. One example of such technologies is electrochemical diamond grinding with periodic dressing of the working surface of a diamond wheel using reverse polarity current. The polarity of the current and the duration of its pulses are controlled by special programmable devices. Current switching units are connected to it. It serves to supply alternating currents of direct and reverse polarity to the electrical circuit and is made on the basis of key elements. Installing such programmable devices on CNC machines leads to its’ equipping with an additional autonomous automatic control system. At the same time, it is difficult to coordinate the operation of the machine’s CNC system, which controls the movements of its working parts, and the programmable device used to control the polarity and duration of current pulses during combined processing. The purpose of the work is to synchronize the CNC system of the machine with the control system for the process of periodically changing the polarity of the current. The study was carried out on an experimental stand. Methods. The research methodology involved conducting an experiment consisting of synchronizing the operation of the machine’s CNC system with the operation of the control system for the process of periodically changing the polarity of the current. To evaluate the results, the time of movement of the diamond wheel as a result of the working stroke was compared with the duration of current pulses of different polarities specified in the control program of the developed software. Results and discussions. As a result of the research, it is established that the developed software and hardware complex makes it possible to synchronize in the CNC system of the machine tool the control of the movements of the working parts with automatic control of the periodic change of current polarity during electrochemical diamond grinding, which can significantly expand the technical capabilities of CNC machines.

Adaptation of Symbolic Discrete Control Synthesis for Energy-Efficient Multi-Pocket Milling

In engineering, cost minimization, especially in Computer Numerical Control (CNC) machining like pocket milling, is crucial. Existing tool path definition software often lacks optimization, particularly at critical starting and ending points. This study optimizes CNC machine tool paths for energy-efficient multi-pocket milling, utilizing the Symbolic Discrete Control Synthesis (SDCS) method for formal correctness. In our work, the tool path generation is formulated as a traveling salesman problem. We introduce a modeling framework to adapt SDCS to multi-pocket-milling processes, aiming to enhance precision and efficiency for potential cost savings, including energy and time, in engineering applications. This study reports experimental and comparative results, where comparative evaluations were made using metaheuristic algorithms. Our proposed approach improves CNC machining processes for multi-pocket milling. We experimentally evaluate our control algorithms and demonstrate and validate our approach through case studies.

Energy-saving strategy and method of spindle deceleration during no-load operation of machine tools for energy lean management

Computer Numerical Control (CNC) machine tools undergo periods of no-load motion, such as tool feeding, tool withdrawing, and tool changing, during workpiece processing. These intervals result in energy wastage as the machine tools remain idle without performing cutting operations. To address this issue, we propose a novel energy-saving strategy and method involving spindle deceleration during the no-load operation of machine tools for energy lean management. Specifically, we conducted three tasks: (1) analyzed the energy consumption characteristics of the CNC machine tool during no-load operation; (2) established a theoretical decision-making model for the energy-saving method of spindle deceleration; (3) discussed the energy-saving critical time for energy savings and the energy-saving effect of the deceleration strategy for the machine tool spindle through three machining cases. Furthermore, we conducted a case study using the CK6153i lathe to verify the effectiveness and feasibility of the proposed method. The results demonstrated significant energy savings of 1826.65 J, 2948.85 J, and 7650.05 J, along with reductions in energy waste of 14.99 %, 20.43 %, and 53.01 %, respectively, in the three machining cases through the implementation of the spindle deceleration strategy. We believe that the outcomes of this research can better assist engineers in the energy lean management of machine tools.

Low Observable Uncrewed Aerial Vehicle Wind Tunnel Model Design, Manufacturing, and Aerodynamic Characterization

Developing wind tunnel models is time consuming, labor intensive, and expensive. Rapid prototyping for wind tunnel tests is an effective, faster, and cheaper method to obtain aerodynamic performance results while considerably reducing acquisition time and cost for the models. Generally, the rapid prototyping models suffer from insufficient stiffness or strength to withstand the loads generated during a wind tunnel test. In the present study, a rapid prototype model reinforced with metallic inserts was produced to experimentally investigate the aerodynamic characteristics of an uncrewed aerial vehicle with various wingtip deflections. The fused deposition modeling process was used to make the outer mold, whereas the metallic parts were produced using laser cutting and the computer numerical control machining process. Then, the model was evaluated both experimentally and numerically. The test campaign presented in this work was conducted in the de Havilland low-speed wind tunnel facility at the University of Glasgow. For better characterization of flow patterns dominated by leading edge vortices, numerical simulations were run using OpenFOAM 8.0 and validated with experimental data. The experimental data obtained from the hybrid rapid-prototyped model agreed well with the numerical results. This demonstrates the efficacy of hybrid rapid-prototyped models in providing reliable results for initial baseline aerodynamic database development within a short period and at a reduced cost for wind tunnel tests.

Estimation of cutting forces in CNC slot-milling of low-cost clay reinforced syntactic metal foams by artificial neural network modeling

PurposeThis study aims to elucidate the machining properties of low-cost expanded clay-reinforced syntactic foams by using different neural network models for the first time in the literature. The main goal of this endeavor is to create a casting machining-neural network modeling flow-line for real-time foam manufacturing in the industry.Design/methodology/approachSamples were manufactured via an industry-based die-casting technology. For the slot milling tests performed with different cutting speeds, depth of cut and lubrication conditions, a 3-axis computer numerical control (CNC) machine was used and the force data were collected through a digital dynamometer. These signals were used as input parameters in neural network modelings.FindingsAmong the algorithms, the scaled-conjugated-gradient (SCG) methodology was the weakest average results, whereas the Levenberg–Marquard (LM) approach was highly successful in foreseeing the cutting forces. As for the input variables, an increase in the depth of cut entailed the cutting forces, and this circumstance was more obvious at the higher cutting speeds.Research limitations/implicationsThe effect of milling parameters on the cutting forces of low-cost clay-filled metallic syntactics was examined, and the correct detection of these impacts is considerably prominent in this paper. On the other side, tool life and wear analyses can be studied in future investigations.Practical implicationsIt was indicated that the milling forces of the clay-added AA7075 syntactic foams, depending on the cutting parameters, can be anticipated through artificial neural network modeling.Social implicationsIt is hoped that analyzing the influence of the cutting parameters using neural network models on the slot milling forces of metallic syntactic foams (MSFs) will be notably useful for research and development (R&D) researchers and design engineers.Originality/valueThis work is the first investigation that focuses on the estimation of slot milling forces of the expanded clay-added AA7075 syntactic foams by using different artificial neural network modeling approaches.

Optimization of responses in single and multi-point machining processes using Dragon-fly algorithm coupled with TOPSIS and VIKOR decision-making approaches

Due to complex production practices, it is becoming crucial to provide greater thermal management and friction reduction in manufacturing processes. Lubricants play a crucial role by effectively reducing friction, minimizing wear and tear thereby extending the lifespan of the tool. It is observed that there is a shortage of studies associated with the machining of hard materials like Ti-6Al-4V. The present work has therefore focused on the machining of high-grade Titanium alloy under dry and Neem oil aerosol-mist spray wet conditions. The machining is performed on two kinds of machining processes, such as plain facing with single point tool and co-axial turn-mill facing with multi-point cutting tools on computer numerical controlled Turn-mill machine tool. Furthermore, there are very few articles reported on usage of Neem oil (AzadirachtaIndicaL.) as a lubricant. This has motivated the authors to use Neem oil in a mist condition to minimize the quantity of coolant, without compromising on the tool temperature, surface quality, and tool-wear. The responses obtained are subjected to regression analysis to evolve the fitness functions. Multi-objective optimization of these functions is than adopted using Dragon-fly algorithm (MODA) under the constraints of the machining parameters. A set of pareto optimal solutions for each of the machining cases are obtained and are further evaluated using TOPSIS and VIKOR decision-making approaches to achieve the optimal values of the cutting parameters. The optimum parameters for single point cutting under wet conditions were observed as speed (592 rpm), feed (0.058 mm rev−1) and depth of cut (1.00 mm).The corresponding values of responses were tool temperature (37.05 °C), surface roughness (0.128 μm) and tool wear (155.19 μm). The equivalent values for multi-point cutting were determined as 510 rpm, 0.086 mm rev−1 and 0.25 mm. The responses for these optimal conditions are 32.17 °C, 0.725 μm and 45.72 μm in that order.

An optimization method of acceleration and deceleration time of feed system based on load inertia

The acceleration and deceleration time is usually a constant value in the process of computer numerical control (CNC) machine tool processing, which cannot adapt to the change of external load and greatly affects processing efficiency. This paper proposes an optimization method for the acceleration and deceleration time of the feed system based on load inertia, which provides the basis for the adaptive adjustment of the acceleration and deceleration time of the feed system. Firstly, by establishing the dynamic model of the servo system, the acceleration and deceleration method is used to identify the external load inertia under different working conditions. The prediction model of the current variance based on load inertia and acceleration and deceleration time parameters is established by using the response surface method, and then the multi-objective particle swarm optimization algorithm is used to build the acceleration and deceleration time optimization model based on the load inertia. At the same time, the inertia identification part is compared with the model reference adaptive system method and the empirical formula estimation method based on current and velocity, the simulation and cutting experiment results show that the inertia identification method based on acceleration and deceleration optimizes the other method. Finally, the machining experiments are carried out on three-axis and five-axis machine tools with the same machine tool type, and by adding different counterweight blocks to change the external load. The test proves that the acceleration and deceleration adaptive adjustment strategy based on load inertia can effectively improve processing efficiency and reduce the fluctuation of the processing load.

Towards Smart Additive Manufacturing: Cost and Component Complexity

The global market size for Additive Manufacturing is predicted to be around USD 20 billion by 2025. The question arises whether conventional machining such as Computer Numerical Control (CNC) should be replaced by Additive Manufacturing (AM). The results presented in this paper are the outcome of an ongoing study. The overall objective of this study is a decision tool to decide which manufacturing route to adopt from a sustainability perspective. This paper will discuss the first phase of this study looking at the mechanical performance, cost and complexity of parts produced from AM and CNC. The results show that small parts are cheaper to fabricate by AM regardless of part complexity, whereas large, simple parts are cheaper to fabricate by CNC machining. These results might help in identifying manufacturing limitations of AM process in terms of mechanical performance and cost. These results will serve as inputs into a decision-making framework to decide on the most effective manufacturing route based on desired application such as in the spare parts in oil and gas industry.