In machining of soft alloys, the sticky nature of localized material instigated by tool-work interaction exacerbates the tribological attitude and ultimately demeans it machinability. Moreover, the ...endured severe plastic deformation and originated thermal state alter the metallurgical structure of machined surface and chips. Also, the used tool edges are worn/damaged. Implementation of cooling-lubrication (C/L) agents to reduce friction at faying surfaces can ameliorate overall machinability. That is why, this paper deliberately discussed the influence of pure C/L methods, i.e., such as dry cutting (DC) and nitrogen cooling (N
2
), as well as hybrid C/L strategies, i.e., nitrogen minimum quantity lubrication (N
2
MQL) and Ranque–Hilsch vortex tube (RHVT) N
2
MQL conditions in turning of Al 7075-T6 alloy, respectively. With respect to the variation of cutting speed and feed rate, at different C/Ls, the surface roughness, tool wear, and chips are studied by using SEM and 3D topographic analysis. The mechanism of heat transfer by the cooling methods has been discussed too. Furthermore, the new chip management model (CMM) was developed under all C/L conditions by considering the waste management aspects. It was found that the R-N
2
MQL has the potential to reduce the surface roughness up to 77% and the tool wear up to 118%. This significant improvement promotes sustainability in machining industry by saving resources. Moreover, the CMM showed that R-N
2
MQL is more attractive for cleaner manufacturing system due to a higher recyclability, remanufacturing, and lower disposal of chips.
The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δ
fl
). Hence, before reaching the threshold of flatness deviation caused by the ...wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.
The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied ...with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.
Being one of the most important staple crops of the world, rice has played a vital role in slaking the calorie requirements of the masses in all the inhabitable continents of our planet. Regardless ...of this fact, there are many environmental concerns related to the rice production systems across the globe. One of the major worries is the emission of lethal greenhouse gases as a result of the different steps and procedures concerned with rice production and their contribution towards global warming. This study presents the status quo of the rice straw burning practice across the globe. It focuses on the greenhouse gas emissions as a result of the open field burning of rice residues and its direct effect on the environment, eventually contributing towards climate change. The study evidently shortlists the most profound regions contributing towards the open burning dilemma and the socio-political reasons associated with it. The study additionally discusses the different alternatives to straw burning with a clear-cut motive of throwing light on the opportunities that lie in the efficacious and sustainable utilization of homogeneous agricultural wastes. Different in-field straw management techniques related to the farmers and off-field methods related to the industry have been discussed. Predicated upon a survey of the life cycle assessment (LCA) studies across the globe, it is concluded that soil incorporation and electricity generation are the most environment friendly alternatives with an enormous scope of improvement in the coming future.
Sensors are the main equipment of the data-based enterprises for diagnosis of the health of system. Offering time- or frequency-dependent systemic information provides prognosis with the help of ...early-warning system using intelligent signal processing systems. Therefore, a chain of data-based information improves the efficiency especially focusing on the determination of remaining useful life of a machine or tool. A broad utilization of sensors in machining processes and artificial intelligence–supported data analysis and signal processing systems are prominent technological tools in the way of Industry 4.0. Therefore, this paper outlines the state of the art of the mentioned systems encountered in the open literature. As a result, existing studies using sensor systems including signal processing facilities in machining processes provide important contribution for error minimization and productivity maximization. However, there is a need for improved adaptive control systems for faster convergence and physical intervention in case of possible problems and failures. On the other hand, sensor fusion is an innovative new technology that makes decisions using multi-sensor information to determine tool status and predict system stability. It is currently not a fully accepted and practiced method. In a nutshell, despite their numerous advantages in terms of efficiency, time saving, and cost, the current situation of sensors used in the industry is not a sufficient level due to the investment cost and its increase with additional signal acquisition hardware and software equipment. Therefore, more studies that can contribute to the literature are needed.
Anti-abrasion thin-film-coated tool is well known for its enhanced micro machining performances. However, coating increases tool edge radius, which spurs additional ploughing and rubbing. Therefore, ...selecting appropriate thin-film thickness and suitable abrasion-resistant coating material for micro tool is necessary to reduce friction and size effects together. To meet these objectives, first, single-layer TiAlN coating having various thin-film thicknesses has been deposited on uncoated micro end mills by PVD process. By analyzing the cutting force, surface quality and edge radius of both fresh and worn tools in micro milling of P-20 steel, appropriate thin-film thickness has been found to be ≈ 1 μm. Next, single layer TiN and diamond-like-carbon (DLC) coating of thickness ≈ 1 μm have been coated on uncoated WC tools. Then coefficient of friction (COF) and hardness of all coated and uncoated surfaces are assessed. Finally, the performance of all the coated and uncoated tools have been evaluated analytically and experimentally by analyzing dynamic stability and machinability, respectively. All the coated tools manifested enhancement in performance by uplifting stability limit and reducing tool wear, cutting forces, surface roughness and burr heights compared to the uncoated tool. Owing to the least COF, the DLC-coated tool exhibited the best performance by uplifting stability limit by 23.37% and reducing cutting force, surface roughness and burr height by 27.35%, 67.7%, and 30.58%, respectively. However, for a long machining length (1200 mm), the DLC-coated tool could not exhibit such performance as compared to TiAlN-coated tool due to significant delamination.
In this paper, the influence of the configuration of the geometric structure of the machined surface on the course of the wear process of frictional pairs is discussed. Arrangement of traces of ...machining determined the level of surface structure isotropy. The characteristics of surface layers are discussed, with particular emphasis on the surface structure isotropy. The results of experimental investigations carried out on the specially designed and made setup are presented. As the measures of the wear process, the following quantities were determined: the mass decrement of samples and changes of the surface roughness parameters, root mean square (RMS) of profile
R
q
and reduced peak height of profile
R
pk
. The results of experimental investigations were registered for structures with different levels of isotropy and, thus, traces of machining. The investigations confirm the influence of the tested factors on the intensity of the wear process.
The most important aspect of sustainability in manufacturing is the preservation of energy and natural resources. For modern production, optimized processes that minimize negative impacts on the ...environment are becoming increasingly important. This can be achieved by increasing energy efficiency through low, clean, and renewable energy consumption. There are many ways to produce less pollution, emissions, and waste in machining: by using more environmentally friendly cooling methods; by applying methods that reduce or eliminate the need for utilization of cooling lubrication; improving the energy efficiency of machining operations; determining the optimal cutting conditions that save resources by increasing machining productivity or reducing the metal removal rate (MRR); minimizing power consumption; and reducing carbon dioxide emissions. This article gives an idea of modern manufacturing with a focus on analyzing the current state of machining operations in terms of saving production resources and ensuring more environmentally friendly production using greener cooling methods of machining such as Dry, Conventional cooling systems, Minimum quantity of lubricant (MQL), Minimum quantity of cooling lubrication (MQCL), Nanofluids, Biodegradable Vegetable Oils, Cryogenic Lubrication, and High-Pressure Cooling (HPC). Finally, the important modern trends of providing resource-saving and environmentally efficient technologies in modern sustainable manufacturing are discussed in this paper.
•Present review article deals with the sustainability and resource saving aspects during machining operations.•Different cooling conditions were presented.•Waste minimizing, recycling, pollution etc. were discussed during machining oeprations.
•This review article covers the application of smart manufacturing system.•Smart monitoring of hole machining has been discussed in details.•Various sensors, signal processing and other methods have ...been discussed.•It gives information about how important indirect measuring methods are in hole machining.
This review paper summarizes the application of smart manufacturing systems utilized in drilling and hole machining processes. In this perspective, prominent sensors such as vibration, cutting forces, temperature, current/power and sound used in the contemporary indirect and direct tool condition monitoring systems are handled one-by-one according to their applications during machining of holes. Thus, it is aimed to show several operations with the application stages and literature papers which utilize the sensorial data such as grinding, reaming, broaching, boring, tapping, drilling and countersinking. The novel side of this paper is summarizing the all-hole machining processes utilizing sensor systems while benefitting their predictive ability for improved machinability characteristics such as surface integrity, tool wear, dimensional accuracy, chip morphology.
On-line monitoring of the machining processes provides to detect the amount and type of tool wear which is critical for the determination of remaining useful lifetime of cutting tool. According to ...Industry 4.0 revolution, the machining performance in terms of cutting forces, surface roughness, power consumptions, tool wear, tool life, etc. needs to be automatically monitored because the unfavorable conditions in machining cause chatter vibrations, tool breakage, and dimensional accuracy. Therefore, the usage of advanced sensor systems plays a key role in achieving the improved machining characteristics in terms of less human effort, errors, production time, etc. and fulfills the requirement of Industry 4.0. Hence, this review presents the holistic knowledge of online detection systems including sensors and signal processing software preferred in mechanical machining operations. Initially, this paper is starting with the up-to-date literature introduction section followed by type of sensors used in machining, online detection methods in machining, challenges and suggestions, etc. Eventually, the article concluded the findings and future remarks especially focused on the theme of Industry 4.0. In the end, it is worthy to mention that this review paper is very helpful for researchers and academicians working in the industrial sectors.
PDF
