Machine Environment Research Articles

Minimizing the earliness–tardiness for the customer order scheduling problem in a dedicated machine environment

AbstractThe customer order scheduling problem has garnered considerable attention in the recent scheduling literature. It is assumed that each of several customer orders consists of several jobs, and each customer order is completed only if each job of the order is completed. In this paper, we consider the customer order scheduling problem in a machine environment where each customer places exactly one job on each machine. The objective is to minimize the earliness–tardiness, where tardiness is defined as the time an order is finished past its due date, and earliness is the time a job is finished before its due date or the completion time of the corresponding order, whichever is later. Even though the earliness–tardiness criterion is an important objective for just-in-time production, this problem has not been studied in the context of the customer order scheduling problem. We provide a mixed-integer linear programming (MILP) formulation for this problem and derive multiple problem properties. Furthermore, we develop six different heuristics for this problem configuration. They follow the structure of the iterated greedy algorithm and additionally use a refinement function in which they differ. In a computational experiment, the algorithms were compared with each other and outperformed a solver solution of the MILP, which proves their ability to efficiently solve the problem configuration.

Effect of machining environments on the crack behavior of ZrO2-Al2O3 composite during short-pulsed laser processing

The ZrO2-Al2O3 exhibits distinct behavior compared to monolithic ceramics when exposed to stress. The compelling quality of this trait makes it well-suited for any demanding supporting application that necessitates resilience. However, under a thermal process, it might cause functional concerns such as cracking patterns, which pose a threat to the endurance of orthopedic implants. This issue has lately attracted medical scrutiny. Being a thermal process, fiber laser treatment of ZrO2-Al2O3 is more complex than monolithic ceramic because of its unique thermal characteristics and varied rates of absorption, which rely on the matrix and the reinforcement material. This research aims to scrutinize the divergent characteristics of ZrO2-Al2O3 in terms of crack behavior while treating it with the same laser fluence under auxiliary environments. It has been found that ZrO2-Al2O3 prone to form cracks when processed under high-temperature environments due to the development of stress during phase transformation because of prolonged exposure, as evidenced by the surface characterization results. Meanwhile, when it was processed at low-temperature environments like water and ice, the detrimental effect of laser fluence factor appeared to be meager by reducing the likelihood of phase transformation and crack quantity. With this, the research demonstrates a promising approach that effectively maintains the overall structural integrity of ZrO2-Al2O3 by impeding the progression of the cracks along with a smooth, flawless surface during laser processing.

Comprehensive analysis of cutting temperature, tool wear, surface integrity and tribological properties in sustainable milling of Ti6Al4V alloy: LN2, nanofluid and hybrid machining

Despite being expensive and difficult to process, the Ti6Al4V alloy is a vital component for crucial industries. To improve its machinability and accomplish sustainable production, environmentally friendly cooling and lubricating agencies are used. Studies on the machinability of the alloy are still necessary because of its unique features and significance in vital industries like aerospace, defense, and medicine. Therefore, this investigation focuses on tool wear, temperature, and surface integrity for sustainable milling Ti6Al4V under various machining environments, i.e., dry, pure-MQL, LN2, hBN, CuO-doped nanofluids, and hybrid methods. The produced nanofluids' thermophysical and rheological characteristics were examined in the study's initial phase. Because of the results from the first stage, machining performance indicators were assessed in the subsequent milling experiments. As a result, CuO-doped nanofluids gave improved results in terms of viscosity and pH. The best results obtained in the LN2 + CuO hybrid cooling lubrication environment in important machinability outcomes such as tool wear and surface integrity were attributed to the rheological properties of CuO-doped nanofluid and its harmonious cooperation with LN2-cryogenic cooling.

Tool chip contact length analysis of dry and cryogenic turning of aerospace alloy Ti-6Al-4V

Tool chip contact length is a vital machining index being representative of manufacturing efficiency. Various machining factors including tool wear and energy consumption are governed by tool chip contact length. The current work was undertaken to make a comparative analysis of dry and cryogenic turning of Ti-6Al-4V in terms of their sustainability and productivity indices. During experimentation, cutting speeds of 50 m/min and 125 m/min were selected working at feed rates ranging from 0.16 mm/rev to 0.24 mm/rev. Scanning electron microscopy was utilized to analyse the results. It was observed that machining under cryogenic conditions, in comparison with dry conditions, resulted in up to 51 % lower tool chip contact length. The results were also investigated in terms of chip formation for both machining environments. In addition, benefits of lower tool chip contact length were quantified in terms of lesser tool wear and better energy consumption. Tool chip contact length and chip formation analysis, indicative of the machining process efficiency, highlights that cryogenic condition is more sustainable and productive than dry conditions.

Multifunctional Sensing Platform Based on Single Antenna for Noncontact Human–Machine Interaction and Environment Sensing

Multifunctional Sensing Platform Based on Single Antenna for Noncontact Human–Machine Interaction and Environment Sensing

The capability of Acoustic Emission features to monitor diamond-coated burr grinding wear and effectiveness

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs) for grinding high strength ceramics or glass, in which the random nature of the tool, inconsistent manufacturing methods and high wear rates create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool's life being underutilised due to premature replacement. Workpiece surface damage, increased grinding forces and large-scale diamond grain pullout could all be the result of high levels of runout and in-circularity common within electroplated DCBs. As such it is important to not only monitor the overall tool wear but also tool condition. Acoustic Emission (AE) presents as an indirect on-machine sensing method highly suited to grinding applications. The high frequency range of AE, >20 kHz, prevents machine noise from dominating the acquired signals, isolating the micro-scale machining processes within noises machine environments. AE resulting from the grinding process has the potential to monitor not only tool wear but also runout and circularity. A series of DCB wear tests have been conducted, each consisting of the continuous acquisition of AE during grinding and frequent tool surface measurements. Preliminary results demonstrate AE kurtosis can be seen as an indicator of each tool’s runout, representing the fraction of time the tool and workpiece are in contact during a revolution. As a result, an indirect monitoring system capable of monitoring wear and tool state with AE could be utilised within the manufacturing sector.

Integrated optimization of production and maintenance scheduling with third-party worker resource constraints in distributed parallel machines environment

Outsourcing machine maintenance to third parties has been a trend due to the increasing complex of machines and the benefits of maintenance outsourcing. However, this new phenomenon is ignored in previous studies pertaining to the integrated optimization of production and maintenance scheduling (IOPMS) problems, resulting the lack of theoretical guidance for production managers to formulate optimal scheduling schemes under this new trend. In this study, we investigate an IOPMS problem considering maintenance outsourcing in a distributed parallel machine environment, referred as IOPMSTW, which requires the use of third-party worker resources to perform preventive maintenance. Makespan and total cost are two optimization objectives. We first formulate the problem by developing a mixed integer linear programming. Then a memetic algorithm incorporating iterated greedy method (IG) is proposed to solve the IOPMSTW, in which an improved decoding method and a problem-dependent local search operator based on IG are designed to respectively ensure the feasibility of new generated individuals and improve the searching efficiency. The validity of proposed mathematical model is verified by CPLEX based on eight small instances. Based on 240 constructed instances, a set of comprehensive experiments are conducted. The results demonstrate that the local search operator improved the searching performance of the proposed algorithm by 100%. Comparison results with other well-known algorithms show that the proposed algorithm achieved the best results on more than 85% of the tested instances.

PERFORMANCE ANALYSIS OF AISI 4340 HARDENED STEEL IN DUAL JET MQL TURNING SYSTEM: A COMPARISON STUDY BETWEEN CVD AND PVD-COATED CARBIDE TOOLS

The hard part turning process used to finish the cylindrical surfaces has some critical machining responses like tool wear with the lower quality of the machined surfaces. Minimum quantity lubrication (MQL)-based machining environment is currently considered an advanced cooling and lubrication technique. Cutting speed, feed rate, and cutting depth are considered input parameters, and the TaguchiL27 orthogonal array (OA) has been adopted as the design of the experiment. In this context, average surface roughness ([Formula: see text]), tool flank wear (VBc), cutting temperature ([Formula: see text]C), and chip morphology have been selected as measured outputs. To improve the machining performance of hardened steel, a sustainable MQL ([Formula: see text] directions) cooling system has been employed at the cutting zone. Iron–aluminum LRT 30 was considered as the base fluid for the hard turning of AISI 4340 steel by PVD (AlTiN) and CVD (TiN/TiCN/Al2O3) coating cutting inserts. The primary effects of process parameters and their influences on the measured outputs have been discussed. The dual jet MQL-based hard turning was performed at three levels of cutting speeds (80, 170, and 260[Formula: see text]m/min), feed rate (0.5, 0.1, and 0.15[Formula: see text]mm/rev), and cutting depth (0.2, 0.3, and 0.4[Formula: see text]mm) with an air pressure of 5[Formula: see text]bar and mist flow rate 50[Formula: see text]ml/h. The findings showed that PVD-coated carbide tools outperformed CVD-coated tools. The model of the measured outputs of both tools has been developed using second-order regression analysis. Utilizing ANOVA analysis, all the predictive models were observed to be significant and acceptable with larger than 90% of [Formula: see text] value.

Estimation of surface quality for turning operations using machine learning approach

The present article examines the effects of machining parameters on machined surfaces to determine optimum turning parameters for AISI-316 under dry machining environment. L27-OA with different levels of Cutting Speed (CS), Feed Rate (FR) and Depth-of-Cut (DOC) is used for experimentation. Surface Roughness (Ra) and Material Removal Rate (MRR) are considered as the response parameters. Among three Machine Learning (ML) models viz. Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Gradient Boosting Regression (GBR), GBR yielded the best results, with significantly higher R2 scores and lower RMSE values. An integration of GBR-PSO algorithms is used to determine 50 sets of, Pareto solutions and desirability analysis rendered the most suitable input parameter values as 111.66 m/min for CS, 0.15 mm/rev for FR and 1.23 mm DOC. At validation stage, Ra and MRR predicted by ML have a nominal difference of 15.19% and 0.115% compared to measured values.

Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.

Ball-end tool wear monitoring and multi-step forecasting with multi-modal information under variable cutting conditions

Tool condition recognition is considered an indispensable solution with significant advantages in improving production cost and quality in intelligent manufacturing. However, the emergence of complex problems such as variable cutting conditions and feature engineering further causes the technology to have a low generalization performance, which severely limits its application in engineering practice. To overcome the above problems as much as possible, a technological framework for monitoring and multi-step forecasting of ball-end tool wear based on multi-modal information under different cutting conditions is proposed. Firstly, a two-stage hybrid deep feature extraction method is proposed by monitoring the cutting vibration and power signals of the spindle. Secondly, a tool wear monitoring model based on SBiLSTM_Multihead Self-attention is proposed to adapt to different cutting conditions. On this basis, a multi-step forecasting model with CNN_SBiLSTM_Multihead Self-attention is proposed to realize the future forecasting of tool wear trend. Finally, the generalization performance of the proposed methods is investigated based on three-axis and five-axis milling experiments. The results show that the correlation coefficient of the enhanced features can reach a maximum value of 87 %. The average accuracy of the proposed monitoring model is improved by an average of 23.84 % over the conventional method. In particular, the multi-step forecasting method is more suitable for long-term forecasting under different cutting conditions. Its average accuracy reaches an average of about 0.013 in the 24-step forecasting. Therefore, the study can provide theoretical references for the application of tool condition recognition in complex machining environments in engineering practice to some extent.

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

Energy-efficient scheduling in an identical parallel machine environment with peak power consumption and deadline constraints

Energy-efficient scheduling is an essential means of achieving sustainability in manufacturing systems. This paper defines and addresses the problem of scheduling n jobs on m identical parallel machines in which peak power consumption and deadline constraints exist simultaneously. The objective is to maximize the total value of the selected jobs. We show that this problem is equivalent to a special case of the rectangular knapsack problem, based on which four properties are observed. To solve the problem, an effective mixed integer linear programming model is proposed based on the properties, and it is much more efficient compared to the performance of modeling methods inspired by other works. Furthermore, three effective decoding methods are proposed and embedded into genetic algorithms (GAs). Comparisons with the exact algorithm (i.e., the proposed model) show that our GAs can lead to good-quality solutions within one second for small instances. Meanwhile, experimental results for large instances indicate that the proposed GAs can obtain near-optimal or satisfactory solutions. Finally, results also show that the proposed GA-DD significantly outperforms the existing matheuristic algorithm.

Experimental investigations and sustainability analysis on the performance of vegetable oil-based MQL drilling of SS316L

ABSTRACT Recently, efforts have been made to reduce the use of conventional cutting fluids. Minimum Quantity Lubrication (MQL) has become a favourable approach to enhance machining performance and sustainability. This study investigated the impact of various cutting conditions on the drilling performance of SS316L, considering surface roughness, tool wear, and chip forms. The drilling tests are performed under dry, flood, and vegetable oil-based MQL (VMQL) conditions. The drilling under the VMQL condition resulted in a desirable surface finish, as indicated by a lower Ra value of 1.81 µm for 20 holes drilled compared to the other conditions. The results highlighted the efficacy of VMQL in reducing tool wear during drilling. Furthermore, the analysis of chip quality demonstrated that VMQL produced favourable chip forms compared to flood cooling and dry machining. The enhanced lubricating and cooling mechanism enabled the creation of desired chip forms under VMQL. In summary, the study clarified the specific benefits of different machining conditions, highlighting the crucial importance of lubrication in achieving desired surface finish, chip quality, and tool wear in various machining environments. The sustainability assessment is conducted using the Pugh matrix approach, and the superiority of VMQL over other drilling conditions is found.

Stacking Effect of Carbon/Glass Fiber During Drilling Operation of Laminated Polymer Composite

Nowadays, fiber stacks are extensively used in the aircraft and structural component manufacturing industries. It is mainly due to their excellent mechanical and physiochemical performances. The different stacking sequences of fiber materials expand the structural properties due to high-strength carbon fiber and low-cost glass fiber. The fragile and anisotropic conduct of Carbon (Cf) and Glass (Gf) laminates generates different types of complex machining issues. This article focuses on the Drilling test of Carbon/glass fiber hybrid composites using different stacking sequences. The effect of varying stacking orders is explored in this study to identify a feasible composite. The control of varying constraints, namely, spindle speed (N), feed rate (f ), and stacking sequence (SS) of carbon (Cf) and glass fiber (Gf) reinforcement, is performed to achieve the optimal parametric condition. The finding reveals that sample A (C4G4) stacking sequence provides an acceptable value for thrust force 59.05 N and delamination 1.0001 for high drilling efficiency. The stacking technique of carbon/glass layers can be endorsed to the manufacturing sector for cost-effective composite development and a defect-free machining environment.

Experimental and machine learning comparison for measurement the machinability of nickel based alloy in pursuit of sustainability

Inconel 718 super alloy, which is widely used in the aerospace industry, has a high fracture resistance, and withstand to high temperatures. The alloy contains mainly Nickel, Chromium and Molybdenum elements in its chemical composition put it among difficult to cut materials. In this context, this study aims to improve the machinability of Inconel 718 superalloy by examining the effect of dry and MQL machining environments while measuring machinability indicators during milling. Tribological aspects considered since the wear, friction and lubrication behavior have a dramatic impact on responses such as tool wear, surface integrity and chip morphology. Microstructural and graphical results were assessed in terms of varying levels of cutting parameters and lubrication conditions. Comparison analysis between MQL and dry media indicated that MQL produces better surface topography and chip morphology, longer tool life in addition to improvement on surface roughness (up to 23.7 %) and cutting temperatures (up to 27.4 %). The root mean square error (RMSE) and coefficient of determination (R2) metrics were utilized to evaluate the findings in the course of machine learning. According to the mean and 95 % confidence interval of RMSE, error rates were found to be good and R2 varied between 67 % and 98 %. Predicted results are in a good agreement with the experimental data which indicated the applicability of machine learning algorithms on sustainable methods of machining.

Design and Implementation of an Adaptive Evaluation System for Teachers’ Information-Based Teaching Ability in the Intelligent ERA

The rapid development of teacher informatization has become a major highlight in current teaching. The information system under adaptive conditions has become the key to the reform of the intelligent era. The goal of this study is to design and implement an adaptive evaluation system for teachers’ information-based teaching ability, aiming at improving ability to meet the needs of modern education. This system analyzes the level of acceptance under key learning technologies. It combines elements by learning and analyzing their impact on general forms. It aims to analyze the rule algorithm dataset in a single machine environment. By analyzing multiple sets of different datasets, it calculates the number of key technologies that appear together in the same report based on the key technology word matrix. A co-occurrence matrix is constructed. The system adopts an advanced adaptive algorithm, which can conduct personalized assessments according to the actual ability and needs of teachers, so as to provide more accurate and effective feedback, help teachers understand their own advantages and disadvantages, and formulate more targeted promotion strategies. In the process of system design, the detailed requirement analysis is carried out first, and the function and performance requirements of the system are clarified. Then, we design the architecture of the system and determine the main modules and functions of the system. The function module is designed and the system is implemented. Through the practical application and effect evaluation of the system, we find that the system has high effectiveness and practicability. It can not only help teachers understand their own level of information-based teaching ability and provide personalized suggestions for improvement, but also provide decision support for education administrators and promote the development of education based on information. We expect that this system can play a greater role in the future education practice and promote the further development of education informatization.

Machine learning models and machinability analysis for comparison of various cooling and lubricating mediums during milling of Hardox 400 steel

Martensitic steels are widely used in many areas such as automotive, mining, and agriculture mostly thanks to their thermal loading ability property. On the other hand, these special steels exhibit extreme tool wear tendency and low surface quality which can be associated with abrasive resistance. This situation makes this steel hard-to-cut and requires further investigation with several approaches. Sustainable machining environments are highly effective as modern strategies to improve the machinability index. Also, machine learning models have pivotal role on decreasing the total consumption in the way of lean manufacturing. In the light of above-mentioned information, this work focuses on the machining performances and optimization of dry, flood, and MQL conditions during the milling of Hardox 400 martensitic stainless steel. A novel approach was applied with using several cutting environments and machine learning models to enhance machinability of Hardox which is an industrially important material. Results were analyzed with different machine learning models using heat map and decision trees. Seemingly, cutting fluid assistance in the milling of Hardox steel is critical where flood and MQL provided a considerable effect on the tool wear for reducing it under some level. Also, this technology was found useful in determining the best conditions of machinability in terms of surface roughness, chip morphology, energy consumption, and cutting temperatures. Machine learning models provided hopeful results in analyzing the correlations between parameters used in the model. In machine learning, the heat map being close to 1 and the MSE and MAE values being close to 0 indicated that the model was suitable. This study is expected to observe the contributions of different types of cutting environments to the machinability criteria during milling of industrially important materials.

Augmented reality to visualize a finite element analysis for assessing clamping concepts

This paper presents the development of an innovative augmented reality application for evaluating clamping concepts through visualizing the finite element analysis. The focus is on transforming the traditional simulation results into immersive, holographic displays, enabling users to experience and assess finite element analysis in three dimensions. The application development process involves data processing by MATLAB, visualization in the software Unity, and displaying holograms through Microsoft’s Hololens2. The most significant advancement introduces a new algorithm for rendering different finite elements in Unity. The application targets not only university engineering students but also vocational students with limited background in finite element analysis and machining, aiming to make the learning process more interactive and engaging. It was tested in a real machining environment, demonstrating its technical feasibility and potential in engineering education.

Grey wolf algorithm for cost optimization of cloud computing repairable system with [formula omitted]-policy, discouragement and two-level Bernoulli feedback

With the increasing reliance on cloud computing as the foundational infrastructure for technological operations, the urgency and efficacy with which failed virtual machines can be successfully repaired earns significant weight. In this study, we have presented a queueing model that analyzes the repair process of failed virtual machines in cloud computing system. This model includes various important components, such as control N-policy, server unreliability, discouragement behavior of failed virtual machines (like balking and reneging), retention of reneged machines, redundancy, and two-level Bernoulli feedback. A steady-state performance analysis has been performed. The proposed model provides practical suggestions for cloud service providers to improve the reliability and availability of their services, which insights more than theoretical frameworks. This study is unique in the field of queueing models of cloud computing machining environment, as it incorporates parallel consideration of control N policies, balking, reneging, retention, server vacation, two-level Bernoulli feedback and unreliable server behavior. A thorough examination of the behavior of key performance indicators has been conducted and presented using detailed graphical representations. In addition, a cost function is formulated for the proposed model. Further, the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) techniques are utilized to optimize the total cost function, thereby gaining valuable insights into the system’s behavior across various conditions as well as to compare the outcomes of GWO and PSO.