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The purpose of this study is to obtain strong materials with multiwall carbon nanotubes (MWCNTs) doped and investigate laser cut of MWCNTs also find the effect of the laser cutting parameters on composite materials.

The laminated composite plates were manufactured by using a vacuum infusion process. The mechanical properties of the composite materials produced were determined according to American Society for Testing and Materials (ASTM) D3039M, ASTM D3171, ASTM D 792 and ASTM D2583. A 130 Watts carbondioxide (CO₂) laser cutting machine was used for drilling the two different composite plates with a thickness of 1.6–1.5 mm. Three variables were considered as process parameters including laser power (in three levels of 84.50, 104.00 and 127.40 W), cutting speed (in three levels of 4, 6, 8 mm/s) and 14 mm fixed focal position.

The fibers could not be cut due to insufficient melting in the experiments performed using 84.50 and 104.00 W laser power but the cutting was successfully completed when the laser power was 127.40 W. However, as the cutting speed increased, the contact time of the laser beam with the material decreased, so the kerf decreased, but the increased laser power created a thermal effect, causing an increase in hardness around the cutting surface. This increase was lower in MWCNTs doped composites compared to pure composites. It has been found that the addition of nanoparticles to layered glass fiber composite materials played an effective role in the strength of the material and affected the CO₂ laser cutting quality.

This study is a unique study in which the CO₂ laser cutting method of MWCNT-doped composite materials was investigated and the machinability without cutting errors, such as delamination, splitting, distortion and burring using the most suitable laser cutting parameters was revealed.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

This paper aims to work exhibits the temperature distribution over the surface of the workpiece during plasma arc cutting process.

The moving heat source is taken into consideration for calculating the heat created by plasma arc. The heat is generated at the plasma – liquid metal boundary. The heat of fusion is also considered for estimation because of molten layer separates the plasma and solid layer. This causes to hamper the heat transfer towards the melting front. Eliminating the heat resistance may calculation error at high cutting speed. Power required to melt the material depends on the speed of the cut.

Higher cutting speed increases the power required. The temperature drop over the layer of molten front increases as the speed of cut increases at higher Peclet number. Different thickness of the molten layer was taken for calculation i.e. zero thickness, 10 and 20 per cent.

The estimated results are shown in non-dimensional form. So, the method can be applied for any other types of material.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The purpose of this study is, to compare laser-based additive manufacturing and subtractive methods. Laser-based manufacturing is a widely used, noncontact, advanced manufacturing technique, which can be applied to a very wide range of materials, with particular emphasis on metals. In this paper, the governing principles of both laser-based subtractive of metals (LB-SM) and laser-based powder bed fusion (LB-PBF) of metallic materials are discussed and evaluated in terms of performance and capabilities. Using the principles of both laser-based methods, some new potential hybrid additive manufacturing options are discussed.

Production characteristics, such as surface quality, dimensional accuracy, material range, mechanical properties and applications, are reviewed and discussed. The process parameters for both LB-PBF and LB-SM were identified, and different factors that caused defects in both processes are explored. Advantages, disadvantages and limitations are explained and analyzed to shed light on the process selection for both additive and subtractive processes.

The performance of subtractive and additive processes is highly related to the material properties, such as diffusivity, reflectivity, thermal conductivity as well as laser parameters. LB-PBF has more influential factors affecting the quality of produced parts and is a more complex process. Both LB-SM and LB-PBF are flexible manufacturing methods that can be applied to a wide range of materials; however, they both suffer from low energy efficiency and production rate. These may be useful when producing highly innovative parts detailed, hollow products, such as medical implants.

This paper reviews the literature for both LB-PBF and LB-SM; nevertheless, the main contributions of this paper are twofold. To the best of the authors’ knowledge, this paper is one of the first to discuss the effect of the production process (both additive and subtractive) on the quality of the produced components. Also, some options for the hybrid capability of both LB-PBF and LB-SM are suggested to produce complex components with the desired macro- and microscale features.

An AFRC funded Link research project between the University of Bristol and the Institute of Food Research, Bristol is examining the use of robots for meat cutting.

THE METAL‐WORKING industry is firmly established as one of the foundations of a modern industrial society. In Britain, which may be taken as fairly representative of such a society, it constitutes the country's largest manufacturing industry employing nearly 3¾ million workers in over 16 theusand plants. Furthermore, apart from the steel industry, which supplies its basic raw material, its anticipated rate of growth is larger than any other industry.

Manufacturing firms are expected to implement green manufacturing and increase product complexity at a competitive price. However, a major problem for engineering managers is to ascertain the costs of embarking on green manufacturing. Thus, a planning and control methodology for costing of green manufacturing at the early design stage is important for engineering managers. The paper aims to discuss these issues.

This paper integrates “green manufacturing,” concepts of industrial dynamics, and product lifecycle aiming at developing a methodology for cost calculation. The methodology comprises of a process-based cost model and a systems dynamics (SD) model. The process-based cost model focusses mainly on carbon emission costs and energy-saving activities. Important metrics usually ignored in traditional static modeling were incorporated using SD model.

Equipment costs and carbon emission costs are major components of costs in manufacturing. The total life cycle cost of product in green manufacturing is lower than that of same product in conventional manufacturing.

The specific results of this study are limited to the case company, but can hopefully contribute to further research on ascertaining cost of implementing “green issues” in manufacturing. The proposed cost calculation model can be efficiently applied in any manufacturing firm on the basis of accessibility of real cost data. This necessitates a comprehensive cost database. At the development of the model and database management system, time and cost resources could be demanding, but once installed, use of the model becomes less demanding.

The cost model provides cost justifications of implementing green manufacturing. The reality is that green manufacturing will see its development peak with cost justifications. The results of the application show that the proposed detailed cost model can be effective in ascertaining costs of implementing green manufacturing. Manufacturing firms are recommended to adopt energy-saving activities based on the proposed detailed cost calculation model.

The main contributions of the study includes: first, to help engineering managers more accurately understand how to allocate resources for energy-saving activities through appropriate cost drivers. Second, to simulate with SD the dynamic behavior of few important metrics, often ignored in traditional mathematical modeling. The detailed model provides a pre-manufacturing decision-making tool which will assist management in implementing green manufacturing by incorporating a life cycle assessment measurement into manufacturing cost management.

Even though austenitic stainless steels have been extensively used in industries, owing to some of the characteristics of the material, its performance in machining is difficult to understand, in particular at high cutting speeds. There is no availability of dependable and in-depth studies pertinent to this matter. In this work, performance of AISI 304 austenitic stainless steel was studied in terms of surface roughness (Ra) and material removal rate (MRR) at high cutting speeds. Subsequently, parametric optimization and prediction for responses were carried out.

Turning operations were conducted using L9 orthogonal array and the outcomes were analyzed to attain optimal set of machining parameters for the responses using signal-to-noise (S/N) ratio and Pareto analysis of variance (ANOVA). In the present work, the cutting speed values were considered beyond the recommended range as designated by tool manufacturers. Finally, multiple regression models were developed to predict responses.

From the results, 350 m/min was found to be a significant speed. The investigation reveals that even though the speeds are taken beyond the recommended values, the results are favorable. The optimal machining parameters values for surface quality obtained were cutting speed of 350 m/min, feed of 0.15 mm/rev and depth of cut of 2.0 mm. In case of MRR, the optimal values were: cutting speed of 400 m/min, feed of 0.25 mm/rev and depth of cut of 2.0 mm. It was found out that there was an improvement in Ra and MRR (around 15 and 4%) due to optimization. The results indicate that Pareto ANOVA is easier than S/N ratio. This revealed that the feed rate and depth of cut were mostly affected parameters for Ra and MRR. The developed models are capable of predicting the responses accurately.

The outcome of the work reveals that even though the speeds were taken beyond the recommended value, the results are favorable for manufacturing industries when the tool cost is considered insignificant.

No work was reported on machining of the chosen material beyond the recommended cutting speed. Moreover, it was observed from the past works that cutting speeds were limited to 100–300 m/min.

Globalization of business, the worldwide competitive economy and the decrease in product life force companies to use new equipments that are continuously introduced to the market with the advances in technology. An improper selection can negatively affect productivity, precision, flexibility and company’s responsive manufacturing capabilities. Thus, selecting the most suitable machine from the increasing number of available machines can be highly demanding. A decision support system is developed for the selection of machine tools. It will guide the selection process and help a decision maker solve the selection problem. Multi‐criteria weighted average is used in decision‐making process to rank the machines evaluated with respect to several criteria. The method is demonstrated with an example.