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The purpose of this paper is to analyse and optimise the machinability behaviour of Carbon Fibre Reinforced Polymer (CFRP) composites with multiple performance characteristics using the Taguchi method with fuzzy logic.

A multi-response performance index (MRPI) was used for optimisation. The machining parameters, viz., tool geometry (helix angle of the endmill cutter), spindle speed, feed rate and depth of cut, were optimised with consideration of multiple performance characteristics, viz., machining force and material removal rate.

The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

The machinability behaviour of CFRP composites during milling of CFRP composites using Taguchi method with fuzzy logic has not been previously analysed.

Because many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.

The methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.

From the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.

Machining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.

This paper describes the suitability of MQL for different machining operations using M-NF and H-NF.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/

Near-net-shaped processes of jet engine blade have better performance in both reducing the material waste during production and improving work reliability in service, while the geometric features of blade, both sculptured surface and thin-walled shape, make the precise machining of blade challenging and difficult owing to its dynamics behaviors under complex clamping and machining loads. This paper aims to present a fundamental approach on modeling and performance analysis of the blade–fixture system.

A computerized framework on the complex blade–fixture dynamic behavior has been developed. Theoretical mechanic analysis on blade fixturing and machining is proposed with an especial emphasis on the boundary conditions of the blade–fixture system. Then the finite element analysis (FEA) method is used to simulate the variation trend of preloads, stiffness and blade distortion. The strong influence of parameters of workpiece–fixture configuration on blade distortion and machining error is investigated.

With a case of real jet engine blade machining, the experimental results and theoretical predictions suggest good agreement on their variation tendency. The loaded pressure of clamps has a critical influence on the total stiff performance of the blade–fixture system, and the profile error of the blade contributes much to the inconsistency in geometric dimension and surface integrity of blades’ machining. In the end, the results also validate the effectiveness of this methodology to predict and improve the performance of the blade–fixture configuration design.

The adaptive machining of near-net-shaped jet engine blade is a new high-performance manufacturing technology in aerospace production. This study provides a fundamental methodology for the performance analysis of blade-fixture system, to clear the variation law of blade distortion during preloading and machining, which will contribute to minimize the machining error and improve productivity.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

Glass fibre reinforced plastics (GFRP) contain two phases of materials with drastically distinguished mechanical and thermal properties, which brings in complicated interactions between the matrix and the reinforcement during machining. Surface quality and dimensional precision will greatly affect parts during their useful life especially in cases where the components will be in contact with other elements or materials during their useful life. The purpose of this paper is to discuss the application of the Taguchi method with fuzzy logic to optimise the machining parameters for machining of GFRP composites with multiple characteristics.

The machining tests were performed on a CNC milling machine using solid carbide (K10) End mill cutting tool with three different helix angles. Experiments were planned using Taguchi’s orthogonal array with the cutting conditions prefixed.

The machining parameters, namely, helix angle of the end mill cutter, spindle speed, feed rate, depth of cut, and work piece fibre orientation (specially applied to the GFRP composites) were optimised with considerations of multiple response characteristics, including machining force, material removal rate, and delamination. The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

Multi-response optimisation of machinability behaviour of GFRP composites using fuzzy logic has not been attempted previously.

The performance of flexible manufacturing systems, (FMS) has been one of the major topics that has attracted the attention of production and operations management (POM) researchers for the last two decades. In this paper we study the relationship between the degree of machine flexibility and the level of system performance. A machine loading and routing model is developed and used to investigate the effect of changing machine flexibility on the performance measures of an FMS. The measures used in this paper include makespan, routing flexibility, capacity flexibility, and inventory effects. The results indicate that changes in the machine flexibility affect some measures more than others. The results also show that, for a decrease of 24 per cent in the degree of machine flexibility, makespan increases by 17 per cent, routing flexibility decreases by as much as 13 per cent, and capacity flexibility decreases by as much as 38 per cent, and the inventory decreases by as much as 26 per cent. Furthermore, as the workload on the FMS increases, the differences in the performance between two FMSs (with different degrees of machine flexibility) increase.

The purpose of this paper is to introduce a new design optimization technique for a surface mounted permanent magnet (SMPM) machine to increase sensorless performance at high loadings by compromising with torque capability.

An SMPM parametric machine model was created and analysed by finite element analysis (FEA) software by means of the Matlab environment. Eight geometric parameters of the machine were optimized using genetic algorithms (GAs). The outer volume of the machine, namely copper loss per volume, was kept constant. In order to prevent sensorless performance loss at high loading, an optimization process was realized using two loading stages: maximum torque with minimum ripple at nominal load and maximum self-sensing capability at twice load. In order to show the effectiveness of the proposed technique, the obtained results were compared with the classical one-stage optimization realized for each loading condition separately.

With the proposed technique, fairly good performance results of the optimization were obtained when compared with the one-stage optimizations. Using the proposed technique, sensorless performance of the motor was highly increased by compromising torque capability for high loading. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.

In related literature, design optimization studies for the sensorless capability of SMPM motor are very few. By increasing optimization performance, new proposed technique provides to achieve good result at high load for sensorless performance compromising torque capability.

The purpose of this paper is to optimise the electro‐discharge machining (EDM) parameters and investigate feasibility of using direct metal laser sintering (DMLS) parts as EDM electrodes.

In this paper the effects of discharge current, pulse‐on‐time, flushing pressure are optimized for minimum tool wear rate (TWR), maximum metal removal rate (MRR) and minimum surface roughness (Ra). Taguchi‐based L₉ orthogonal array has been used for performing experiments on EDM machining of EN 24 steel using DMLS electrodes. The grey relational analysis combined with ANOVA techniques have been employed to determine the optimal level as well as their significance.

Experimental results have shown that the performance characteristics of the EDM process (TWR, MRR and surface roughness) using DMLS electrode can be quantified and controlled effectively by grey relational approach presented in the study. Current is found to be the most affective parameter in EDM machining using DMLS electrode. Excessive DMLS tool (electrode) wear was also reported, which limits the use of DMLS tool for EDM machining and it has been found out that porosity (which was about 20 per cent) was one of the primary cause.

This paper was focused on understanding the effects of important EDM parameters on three performance characteristics (TWR, MRR and surface roughness). While this study identifies that DMLS electrode wear rate is high and porosity could be one of the main cause, presently it does not cover the investigations on reducing the porosity level and its implications.

The DMLS material had shown huge potential to be used as EDM electrode. The current investigation established a structured experimental approach to understand the effects of EDM parameters on multi response characteristics. The results derived from this study helps to focus future research on two aspects including enriching the copper content and reducing the porosity level, thereby the benefits of lead time reduction in EDM electrode making could be realized.

The previous research attempts were not focussed on optimising the EDM machining process using rapid tooling electrodes. With the best of author's knowledge none of the researchers have reported these aspects especially for DMLS electrodes. Application of grey relational analysis for performance evaluation of rapid tooling‐based EDM electrodes (DMLS electrodes) appear to be completely new.

The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies.

The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review.

The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized.

Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries.

This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia.

This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.

The purpose of this paper is to study the performance and efficiency of two different permanent magnet (PM) machine rotor configurations under magnetic core saturation conditions.

Since the accuracy of conventional analytical methods is limited under saturation conditions, a finite element model of the machine is built; which is used to predict the various losses over its operating range such as eddy current, hysteresis, copper and magnet losses. Using this model, the efficiency map of the machine is derived which is used to investigate its efficiency corresponding to a heavy vehicle drive cycle. The performance of two different rotor designs are studied and the efficiency of each design is compared under the considered drive cycle.

It has also been proved that the performance advantage due to reluctance torque in the v-shaped interior PM (IPM) machine is offset by its core steel saturation at higher current/torque levels. The magnitude of iron losses in the IPM is higher than that in the surface PM (SPM) machine, however, the magnet loss in the SPM is higher than in the IPM.

An investigation of the performance of the IPM design in comparison with the SPM∼design under magnetic saturation conditions is not known to the authors. Hence, in this paper, it will be determined if the assumed performance advantage of the IPM over the SPM still holds true under these conditions.