scholarly journals Energy-Based Novel Quantifiable Sustainability Value Assessment Method for Machining Processes

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6144 ◽  
Author(s):  
Aqib Mashood Khan ◽  
Saqib Anwar ◽  
Munish Kumar Gupta ◽  
Abdullah Alfaify ◽  
Saqib Hasnain ◽  
...  

Sustainability assessments of cooling/lubrication-assisted advanced machining processes has been demanded by environment control agencies because it is an effective management tool for improving process sustainability. To achieve an effective and efficient sustainability evolution of machining processes, there is a need to develop a new method that can incorporate qualitative indicators to create a quantifiable value. In the present research work, a novel quantifiable sustainability value assessment method was proposed to provide performance quantification of the existing sustainability assessment methods. The proposed method consists of three steps: establishing sustainable guidelines and identifying new indicators, data acquisition, and developing an algorithm, which creates the Overall Performance Assessment Indicator (OPAI) from the sustainability assessment method. In the proposed algorithm, initially, both quantitative and qualitative sustainability indicators are normalized. After weight assignment and aggregation, the OPAI is obtained. The developed algorithm was validated from three literature case studies, and optimal cutting parameters were obtained. The present methodology provides effective guidelines for a machinist to enhance process performance and achieve process optimization. The study also offers a relationship between sustainable and machining metrics for the support of industrial sustainability.

2017 ◽  
Vol 753 ◽  
pp. 183-187 ◽  
Author(s):  
Muhammad M. Liman ◽  
Khaled Abou-El-Hossein ◽  
Abubakar I. Jumare ◽  
Peter Babatunde Odedeyi ◽  
Abdulqadir N. Lukman

Contact lens manufacture requires high accuracy and surface integrity. Surface roughness an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. This research work is therefore aimed at developing a predictive surface roughness model and investigate a finish cutting conditions of ONSI-56 contact lens polymer with a monocrystalline diamond cutting tool. In this work, a novel surface roughness prediction model, in which the feed rate, cutting speed and depth of cut are considered is developed. This combined process was successfully modeled using a Box–Behnken design (BBD) with response surface methodology (RSM). The effects of feed rate, cutting speed and depth of cut were investigated. Analysis of variance (ANOVA) showed that the proposed quadratic model effectively interpreted the experimental data with coefficients of determination of R2 = 0.89 and adjusted R2 = 0.84. The worse surface value was obtained at high feedrate and low spindle speed.


Author(s):  
Lei Pan ◽  
ZR Wu ◽  
Lei Fang ◽  
YD Song

Machined surface condition of nickel-based superalloys has an important influence on the functional performance of the components. Proper selection of cutting parameters could improve surface finish and increase service life of parts and components. This research work bases on an experimental and statistical study of turning GH4169 nickel-based superalloy with cemented carbide tool. Surface damages like feed marks, tips, and surface tearing were discussed. The second-order polynomial model was used to describe the surface roughness response. Variance analysis was selected to eliminate the insignificant variables in the roughness model. The response surface methodology was used to investigate the combined effect of cutting parameters on two different dimensions surface roughness parameters. The optimization of cutting parameters for minimum surface roughness was obtained using desirability function method. The results demonstrate that feed rate has the most significant effect on surface roughness. High cutting speed and low feed rate result in better surface quality, but too low feed rate exacerbates built-up edge phenomenon and deteriorates surface condition. Optimal cutting parameters leading to the minimum surface roughness were highlighted.


Author(s):  
E. Gutierrez Romo ◽  
J. Caldero´n

As machining processes become one of the most common kinds of manufacturing processes in industry, it becomes imperative to optimize cutting parameters in order to reduce machining times and increase surface quality. This is specially true when piece geometry demands high rates of material removal. In previous work by the authors, piezoelectric dynamometers have been used to find cutting forces which in turn allows finding of optimal cutting parameters. Although the methodology reported has proved to be very effective, its application in the production line has not been straightforward as the use of a piezoelectric dynamometer requires an expensive setup and skilled technicians. The objective of this work is to propose and validate an experimental methodology that allows the determination of optimal cutting parameters for material-tool pairs by measuring the electrical power consumed by the machine-tool during cutting. This latter approach is more economical and easy to apply in the manufacturing line. Optimized parameters obtained through this methodology yield improvements up to more than twice on removal rates compared to those recommended by tool suppliers for the same process requirements.


2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.


2021 ◽  
Vol 13 (2) ◽  
pp. 825
Author(s):  
Jonas Ammenberg ◽  
Sofia Dahlgren

This article departs from the perspective of Swedish regional transport authorities and focuses on the public procurement of bus transports. Many of these public organizations on the county level have the ambition to contribute to a transition involving the continued marginalization of fossil fuels and improved sustainability performance. However, there are several renewable bus technologies to choose between and it can be difficult to know what alternative (or combination) is preferable. Prior research and the authors’ experiences indicate a need for improved knowledge and supportive methods on how sustainability assessments can support public procurement processes. The purpose of this article is to develop a multi-criteria assessment (MCA) method to support assessments of public bus technologies’ sustainability. The method, which was established in an iterative and participatory process, consists of four key areas and 12 indicators. The article introduces the problem context and reviews selected prior research of relevance dealing with green or sustainable public procurement and sustainability assessments. Further on, the process and MCA method are presented and discussed based on advice for effective and efficient sustainability assessments. In the companion article (Part II), the MCA method is applied to assess several bus technologies involving biodiesel, biomethane, diesel, electricity, ethanol and natural gas.


2020 ◽  
Vol 87 (12) ◽  
pp. 757-767
Author(s):  
Robert Wegert ◽  
Vinzenz Guski ◽  
Hans-Christian Möhring ◽  
Siegfried Schmauder

AbstractThe surface quality and the subsurface properties such as hardness, residual stresses and grain size of a drill hole are dependent on the cutting parameters of the single lip deep hole drilling process and therefore on the thermomechanical as-is state in the cutting zone and in the contact zone between the guide pads and the drill hole surface. In this contribution, the main objectives are the in-process measurement of the thermal as-is state in the subsurface of a drilling hole by means of thermocouples as well as the feed force and drilling torque evaluation. FE simulation results to verify the investigations and to predict the thermomechanical conditions in the cutting zone are presented as well. The work is part of an interdisciplinary research project in the framework of the priority program “Surface Conditioning in Machining Processes” (SPP 2086) of the German Research Foundation (DFG).This contribution provides an overview of the effects of cutting parameters, cooling lubrication and including wear on the thermal conditions in the subsurface and mechanical loads during this machining process. At first, a test set up for the in-process temperature measurement will be presented with the execution as well as the analysis of the resulting temperature, feed force and drilling torque during drilling a 42CrMo4 steel. Furthermore, the results of process simulations and the validation of this applied FE approach with measured quantities are presented.


Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3789
Author(s):  
Michele Lanzetta ◽  
Marco Picchi Picchi Scardaoni ◽  
Armin Gharibi ◽  
Claudia Vivaldi

This paper explores the modeling of incipient cutting by Abaqus, LS-Dyna, and Ansys Finite Element Methods (FEMs), by comparing also experimentally the results on different material classes, including common aluminum and steel alloys and an acetal polymer. The target application is the sustainable manufacturing of gecko adhesives by micromachining a durable mold for injection molding. The challenges posed by the mold shape include undercuts and sharp tips, which can be machined by a special diamond blade, which enters the material, forms a chip, and exits. An analytical model to predict the shape of the incipient chip and of the formed grove as a function of the material properties and of the cutting parameters is provided. The main scientific merit of the current work is to approach theoretically, numerically, and experimentally the very early phase of the cutting tool penetration for new sustainable machining and micro-machining processes.


Author(s):  
K. Koppiahraj ◽  
S. Bathrinath ◽  
V. G. Venkatesh ◽  
Venkatesh Mani ◽  
Yangyan Shi

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.


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