Machining Strategies Research Articles

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

Strategy Analysis for Implementing Rice Transplanter Planting Machine Technology in Rice Farming Using the Interpretive Structural Modeling (ISM) Method in South Sulawesi

Indonesia has several rice producing centers, one of which is Sidrap Regency which is located in South Sulawesi with the support of a rice transplanter technology system. This research aims to analyze the factors inhibiting the implementation of rice transplanter rice planting machine innovation and strategies for implementing the use of this planting machine. This research uses descriptive analysis by creating frequency tables and percentages from primary data results obtained through interviews with respondents. Likert scale to measure farmers' responses to the dissemination of the Rice Transplanter rice planting machine innovation in Sidrap Regency and the ISM method was used to develop a strategy for implementing the dissemination of agricultural innovation to support agricultural development in Sidrap Regency. The research results show that the factors inhibiting the implementation of Rice Transplanter machine innovation are farmers' lack of skills in using rice transplanter, limited rice transplanter, facilities in farmer groups, lack of rice transplanter service providers and lack of farmer staff, motivation to use rice planting tools. The strategy for implementing rice transplanter innovation is through training in implementing rice transplanter innovation, increasing farmers' interest in using rice transplanters, providing rice transplanter facilities and strengthening institutions.

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.

Analysis of the machining process of short carbon fiber-reinforced polyamide additive manufactured parts

In recent years, additive manufacturing technologies have revolutionized the production of parts, particularly in the aeronautical sector. This new manufacturing paradigm has created significant challenges and opportunities for researchers in materials and manufacturing processes. One important aspect is the development of optimal strategies for finishing-oriented machining of parts produced through additive manufacturing.This article focuses on the analysis of reinforced polyamide materials and the integration of large-scale additive manufacturing using Big Area Additive Manufacturing (BAAM) technology, along with robotic systems for subtractive machining. The aim is to explore the potential of integrating additive and subtractive processes to produce high-quality, large-scale components.The study examines the production and subsequent machining of reinforced polyamide parts using BAAM technology, showcasing the advantages and promising results observed. By combining additive manufacturing with subtractive machining, this research contributes to the ongoing advancements in the field of manufacturing, particularly in relation to reinforced polyamide materials. The findings presented in this article shed light on the potential of integrating additive and subtractive processes in the manufacturing industry, paving the way for more efficient and high-quality production methods.

Superfinishing with Abrasive Films Featuring Discontinuous Surfaces.

This study introduces innovative designs for abrasive tools aimed at enhancing surface finishing processes. Prototypes consisting of non-continuous abrasive films with discontinuous surface carriers and abrasive layers were developed to improve the efficiency and effectiveness of the smoothing process. Four distinct abrasive films with varying nominal grain sizes were fabricated to explore the versatility and efficacy of the prototypes. The results indicate that the incorporation of carrier irregularities significantly influences surface finishing processes, leading to improvements in material removal efficiency and surface quality. Longitudinal discontinuities facilitate faster removal of irregularities from workpiece materials, reducing the risk of deep scratches on surfaces. Additionally, this study highlights the importance of tool motion patterns in optimizing material removal processes and ensuring surface quality. The integration of carrier irregularities with additional oscillatory tool motion shows promise for further improving surface quality. These findings advance our understanding of abrasive machining processes and provide valuable insights for optimizing abrasive tool designs and machining strategies for enhanced surface finishing.

The influence of the machining strategy on milling of polyetheretherketone (PEEK)

The influence of the machining strategy on milling of polyetheretherketone (PEEK)

Investigating micro-cutting mechanism of glow discharge polymer based on material properties and removal behaviors analysis

Glow discharge polymer (GDP) is the unique artificial material for the target balls in Inertial Confinement Fusion tests, while its practical micromachining such as the fabrication of microstructure on material surfaces keeps challenging due to its unclear micro-cutting mechanism. Hence, this paper seeks to investigate the micro-cutting mechanism of GDP from the perspective of the material flow and cutting energy. To achieve it, the energy conservation equation of three cutting modes triggered by different ratios of uncut chip thickness to the tool cutting edge radius (RTS) was established based on cutting deformation behaviors. Meanwhile, the diamond cutting tests and the FEM simulation at different RTS were developed. The experimental observation of cutting forces and specific cutting forces verified the evolution of three cutting modes, including shearing, shearing-ploughing and ploughing in the micro-cutting of GDP with the decrease of RTS. Next, from the change of the node displacement vector observed from the simulated results, it can be seen that the real-time material flow behavior during micro-cutting of GDP varies obviously with the evolution of cutting modes. Besides, the fracture toughness Gc, and the energy dissipation of different cutting modes were analyzed. The proportion of the energy spent on material fracture (Gc=9.95 N/mm) is the largest one in shearing and shearing-ploughing modes, while in ploughing mode, the material plastic deformation consumed the most energy. The above results reveal the specific material properties and removal behaviors of GDP and contribute to optimizing the machining strategies for the practical micromachining of microstructures on material surfaces.

Exploring the constraints on artificial general intelligence: A game-theoretic model of human vs machine interaction

The potential emergence of artificial general intelligence (AGI) systems has sparked intense debate among researchers, policymakers, and the public due to their potential to surpass human intelligence in all domains. This note argues that for an AI to be considered “general”, it should achieve superhuman performance not only in zero-sum games but also in general-sum games, where winning or losing is not clearly defined. In this note, I propose a game-theoretic framework that captures the strategic interactions between a representative human agent and a potential superhuman machine agent. Four assumptions underpin this framework: Superhuman Machine, Machine Strategy, Rationality, and Strategic Unpredictability. The main result is an impossibility theorem, establishing that these assumptions are inconsistent when taken together, but relaxing any one of them results in a consistent set of assumptions. This note contributes to a better understanding of the theoretical context that can shape the development of superhuman AI.

Development of a machining strategy to manufacture SiSiC nuts by EDM

Today, the high-precision manufacturing of small cavities in difficult-to-machine materials is still a challenge, even more so if they need to be threaded. The machining time, the wear suffered by the electrodes and the surface finish are determining factors in the efficiency of the threading process. However, there is scant literature on this subject so there is a need to study the process and the parameters involved. Thus, this study presents a novel machining strategy for the manufacture of nuts using die-sinking electrical discharge machining (EDM). Moreover, the novelty of this strategy is that it is carried out in a single stage and with a conventional EDM generator. To do so, a design of experiments (DOE) methodology has been followed. First, the optimal machining conditions are determined by studying the influence of EDM parameters on operation variables and mathematical models are developed using multiple linear regression. These models allow the behavior of the response variables under study to be predicted. Finally, this machining strategy developed from the previous experimental results is validated in the manufacturing process of a final part, specifically a square nut. It can be concluded that the mathematical model is good enough to predict the experimental results. Thus, the new method presented and described in this present study allowed a nut to be obtained with a real arithmetic mean deviation of the roughness profile (Ra) value of 1.27 μm whereas the predicted value from the model was 1.28 μm. To do so, the machining conditions selected were: 4 A (current intensity), 5 µs (pulse time) and 0.4 (duty cycle), which also gave a material removal rate (MRR) value of 0.5370 mm3/min. The machining strategy proposed here may be used for future research works related to the manufacturing of mechanical joints made of conductive ceramic materials.

Modeling surface quality, cost and energy consumption during milling of alloy 2017A: a comparative study integrating GA-ANN and RSM models

ABSTRACT The use of simple machining features to determine cutting parameters and the machining process is limited, as parts may contain complex features interacting with each other. This study, therefore, focuses on pocket/groove features and proposes an approach integrating hybrid GA-ANN and RSM algorithms to predict surface quality, cost, and energy consumption (QCE). A parametric study was carried out, taking into account the swarm population size (pop) and the number of neurons (n) in the hidden layer, to find the best prediction using the hybrid GA-ANN algorithm. The results showed the highest correlation values (R2) for all output variables (above 0.97%). The study also revealed that the allocation of machining strategies and sequences can have a significant impact on energy consumption, with a 99.25% variation between minimum and maximum values. Mean square error (MSE) data confirmed the effectiveness of the GA-ANN model. Compared with RSM model predictions, energy consumption (Etot), cost (Ctot), and surface quality (Ra) values all showed statistically significant increases of 90.9%, 96.55%, and 40.18%, respectively. This study highlights the potential of the GA-ANN hybrid approach for multi-criteria prediction (quality, cost, and energy: QCE) in comparison with the RSM method, offering potential improvements for machining 2017A alloy.

INCONEL® Alloy Machining and Tool Wear Finite Element Analysis Assessment: An Extended Review

Machining INCONEL® presents significant challenges in predicting its behaviour, and a comprehensive experimental assessment of its machinability is costly and unsustainable. Design of Experiments (DOE) can be conducted non-destructively through Finite Element Analysis (FEA). However, it is crucial to ascertain whether numerical and constitutive models can accurately predict INCONEL® machining. Therefore, a comprehensive review of FEA machining strategies is presented to systematically summarise and analyse the advancements in INCONEL® milling, turning, and drilling simulations through FEA from 2013 to 2023. Additionally, non-conventional manufacturing simulations are addressed. This review highlights the most recent modelling digital solutions, prospects, and limitations that researchers have proposed when tackling INCONEL® FEA machining. The genesis of this paper is owed to articles and books from diverse sources. Conducting simulations of INCONEL® machining through FEA can significantly enhance experimental analyses with the proper choice of damage and failure criteria. This approach not only enables a more precise calibration of parameters but also improves temperature (T) prediction during the machining process, accurate Tool Wear (TW) quantity and typology forecasts, and accurate surface quality assessment by evaluating Surface Roughness (SR) and the surface stress state. Additionally, it aids in making informed choices regarding the potential use of tool coatings.

Research on Energy Management Strategy of Fuel Cell Tractor Hybrid Power System

In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system.

Machining characteristics in ultrasonic vibration-assisted powder-mixed electrical discharge machining of TiN ceramics

This study aims to investigate the application and machining characteristics of brittle conductive ceramics using ultrasonic vibration-assisted powder-mixed electrical discharge machining (UV-PMEDM) technology. Titanium nitride (TiN) ceramics are widely used in aerospace, biomedicine, and electronics due to their excellent properties. However, the conventional mechanical machining methods of these materials result in severe tool wear, high costs, and low efficiency. Although electrical discharge machining (EDM) can overcome some limitations, it typically exhibits a low material removal rate and poor surface quality. This study utilizes the UV-PMEDM technology to process TiN ceramics. Single-factor experiments are conducted to analyze the effects of processing parameters, including ultrasonic amplitude, powder concentration, pulse-on time, and pulse-off time, on the material removal rate, surface roughness, and relative electrode wear rate, which indicate the EDM processing performance. With the assistance of ultrasonic vibration and mixed powders, the material removal rate of TiN ceramics using UV-PMEDM reaches 0.50 mm3/min, while maintaining a relative electrode wear rate below 1 % and surface roughness lower than 4.2 μm. The synergistic combination of ultrasonic vibration and mixed powders significantly enhances the processing efficiency and reduces surface roughness of TiN ceramics. These findings contribute to a better understanding of machining strategies for brittle conductive ceramics.

A stiffness matching-based deformation errors control strategy for dual-robot collaborative machining of thin-walled parts

Thin-walled parts are widely used in aerospace industry. To reduce the deformation errors of such thin-walled parts in machining and also to achieve simultaneous machining of both sides, a novel dual-robot machining system is developed in this paper. In this system, the local rigidity of the thin-walled parts can be enhanced via mutual support between two robots on both sides of the parts, thus reducing the deformation error caused by the low rigidity of the parts effectively. Further, a new concept of dual-robot stiffness matching is introduced, and the deformation of thin-walled parts caused by stiffness mismatching between two robots is analyzed and given a close-form solution. On basis of this solution, a dual-robot posture optimization model, which can satisfy the stiffness matching constraint and improve the normal stiffness of two robots, is developed and then is solved efficiently using the proposed easy-execution sequential algorithm based on the directed node graph and Dijkstra searching, thus the deformation errors of thin-walled parts in machining can be ulteriorly controlled effectively. Finally, the computer simulation and experiments are performed and the results demonstrate that the developed dual-robot machining system and the stiffness matching-based deformation control strategy can effectively reduce the deformation of thin-walled parts in machining.

Optimization of virtual design and machining time of the mold master ceramic jewelry products with Indonesian batik motifs

Batik has been the cultural heritage of the Indonesian nation since the 17th century. On 2 October 2009, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) designated Batik as part of Indonesia’s traditional clothing as Indonesia’s cultural heritage. This research proves that Batik is an attractive and artistic ornament of ceramic jewelry products. This ornament can increase the selling value and storytelling of the product. Naruna is a local handmade porcelain ceramic factory in Indonesia with excellence in unique colored tableware and consistently trying to develop new designs in ceramic jewelry. Modern technology in computer-based virtual design and machining on dining plate tableware with the Batik Kawung motif is also applied in this paper to obtain a master design for ceramic jewelry prints. The design stage begins with selecting ornament patterns with creative methods to form a 3D CAD model of ceramic jewelry using artistic Art CAM. The optimization of computer-aided manufacturing-based to obtain machining strategy using the software. The software is Autodesk Power Mill, whose NC Code can be directly manufactured on a CNC router machine. This combination can speed up the production process time to meet the production capacity at Naruna and get detailed, accurate, precise, and consistent textures and ornaments, thereby meeting NCS consumer demand for ceramic jewelry products. The simulation results of the two master print patterns are 141 h, 56 min, and 46 s; the machining time is estimated at 153 h. This research saves about 11 h of time processing. The resulting NC Code can be appropriately processed on a CNC router machine with a Mach 3 post-processor.

Experimental investigation of tool feed rate and machining strategy on surface roughness in flat end milling of Nickel-Iron alloy Supra50

The Nickel-Iron alloy Supra50 is one of the commonly used materials for manufacturing aeronautical parts and is resistant to excessive thermal stress. Due to its low coefficient of expansion, this material can guarantee dimensional stability at high temperatures while also offering mechanical characteristics like those of stainless steel. However, several works in the literature demonstrate that the Nickel-Iron alloy Supra50 has low machinability due to low thermal conductivity and work hardening. These main qualities, with its magnetic characteristics, qualified it for the manufacture of components for solenoid valves in aircraft. Despite its performance, obtaining Supra50 parts by machining presents difficulties. Indeed, faced with tight dimensional and geometric tolerances, manufacturers must invest in means and methods that converge to an agreement between cost and quality that ensures the efficiency of the machining process. This research work aims to analyze the effect of machining strategies and the tool feed rate on surface roughness by using an experimental approach in a Fe-Ni-based alloy-Supra50 surfacing operation with a flat end-mill tool. This study addresses a gap in the literature, focusing on the unique challenges posed by Supra 50 machining. It was demonstrated that the feed rate strongly affects surface roughness. In other hand, it was demonstrated that the machining strategy sweep gives the minimum of arithmetic mean height of the surface (Sa), compared to other analyzed machining strategies, such as mono-directional sweep and spiral sweep. All experimental results were demonstrated in sequence with other literature works on Fe-Ni-based alloy machining.

Simultaneous high-speed cutting and high-feed milling: An investigation on surface integrity

In this article, the potential of high-speed machining with an inverse cutting ratio b/h < 1 (uncut width of chip b to uncut chip thickness h) is presented based on the evaluation of surface integrity. If the machining is realized with a low cutting ratio many process quantities and chip formation mechanisms are fundamentally changed. As a result, the cutting process is stabilized through reduced feed forces and improved chip removal conditions. For the investigations, face-milling trials were carried out on an aluminum alloy (EN AC-42100) under conventional and high cutting speeds with variation of the feed per tooth and the use of cemented carbide and polycrystalline diamond tools. It is shown that when both machining strategies are combined, there is no further reduction in force components or improvement in surface topography. When the feed per tooth is increased at high cutting speeds, there is a linear increase in force components but surface roughness remain almost unchanged. After an initial increase in surface residual stresses, increasing the cutting speed leads mainly to compressive surface residual stresses. Overall, the surface integrity is influenced by the choice of cutting material and the micro-geometry of the cutting edge radius.

Study of optimization of blade non-uniform allowance process and chatter stability domain

In addressing the phenomenon of chatter during the machining of thin-walled components, a machining strategy involving the implementation of a continuous non-uniform toolpath has been proposed as a remedy for chatter suppression. The methodology commences with a meticulous determination of machining allowances, accomplished by utilizing fitting functions. Subsequently, the stiffness coefficients and natural frequencies inherent to the workpiece-tool system are extracted through modal analysis. A stability lobes diagram is formulated, employing the principles of regenerative chatter analysis. This diagram encompasses both uniform toolpath scenarios and non-uniform continuous toolpath scenarios, with the latter established via the least squares method. The resultant stability lobes diagram effectively delineates the boundaries within which chatter remains stable, under varying depths of cut. The stability lobes diagrams substantiate the effectiveness of the non-uniform continuous toolpath constructed via the least squares method in significantly augmenting the inherent stiffness coefficient of the workpiece-tool system, thereby mitigating chatter. This approach serves as a valuable guide for the manufacturing of intricate curved thin-walled components.

Ultrashort pulse laser micro processing strategies for fast beam deflection with acousto-optical deflectors

Acousto-optical deflectors (AOD) enable laser pulse-synchronous beam deflection up to the MHz range, which can deviate from the usual path control and raster scan strategies. Where the small working field of the AODs is not sufficient, a combination with a galvo scanner or an axis system can be used. Laser micromachining with modern ultrashort pulse lasers with a high pulse repetition rate is increasingly faced with the challenge of applying the laser power to the workpiece without too much heat remaining locally in the surface. AOD-based beam deflection can help here by quickly distributing the laser pulses on the surface, which is equivalent to a locally reduced laser pulse repetition rate without reducing the laser’s repetition rate. We provide an overview of the technology and present examples of innovative machining strategies resulting from the use of acousto-optical laser beam deflection.

Milling or grinding for manufacturing of an Alloy 718 gas turbine component? – A comparison of surface integrity and productivity

Milling is traditionally the most used machining method when manufacturing complex gas turbine components. In particular those made from nickel-based superalloys. However, for larger free form surfaces, grinding may be an efficient alternative that could be used throughout the complete manufacturing route, from roughing to finishing. Hence, in this work the two processing methods has been compared in regard to surface integrity and productivity.Machining tests have been performed on case plates of heat-treated Alloy 718 using best practise setting for roughing and finishing with grinding and milling. The surface integrity of the work pieces was evaluated regarding surface topography, residual stresses, and deformation. This comparison showed that the main advantage with grinding is the ability to switch between roughing and finishing by just altering the depth of cut. Further, grinding offers lower surface roughness, compressive residual stresses, and significantly lower degree of deformation.From a productivity perspective, deep grinding may offer high material removal rates and ability to machine several work pieces in the same setup. However, grinding is limited to simpler free form geometries and may result in minor surface damages and abrasive surface residue. For selection of machining strategy, advantages and drawbacks shown in this work need to be considered for the application at hand in respect to productivity, surface integrity and requirements on fatigue life.