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Surface roughness and burr formation are among the most important surface quality metrics which determine the quality of the fabricated parts. High precision machined microparts with complex features require micromachining process to achieve the desired yet stringent surface finish and dimensional accuracy. In this research, the effect of cutting speed (m/min), feed rate (µm/tooth), depth of cut (µm) and three types of tool coating (AlTiN, nACo and TiSiN) were analyzed to study their effect on surface roughness and burr formation during the micromachining of Inconel 718. The analysis was carried out using an optical profilometer, scanning electron microscope and statistical technique. Machining tests were performed at low speed with a feed rate (µm/tooth) below the cutting-edge radius for 10 mm cutting length using a carbide tool of 0.5 mm diameter on a CNC milling machine. From this research, it was determined that the depth of cut was the main factor affecting burr formation, while cutting velocity was the main factor affecting the surface roughness. In addition, cutting tool coating did not significantly affect either surface roughness or burr formation due to the difference in coefficient of friction. The types of burr formed during micromilling of Inconel 718 were mainly influenced by the depth of cut and feed rate (µm/tooth) and were not affected by the cutting velocity. It was also concluded that the results for the surface finish at low-speed machining are comparable to that of transition and high-speed machining, while the burr width found during confirmation experiments at low-speed machining was also within an acceptable range.

For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

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.

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

The accelerated growth of 3D printing technologies has revolutionized the potential of ceramic materials, offering unprecedented control over microstructures, saving labor cost, material, and process time. Stereolithography-based 3D printing has grown to fabricate advanced ceramics materials with the flexibility of mass production. 3D printing generally offers unprecedented versatility, fast-printing tangible design, custom freedom, and excellent laying accuracy. More specifically, ceramic materials offer unique mechanical properties to make them superior for various applications. A description of how digital light processing (DLP) 3D printing can play a pivotal role in fabricating oxide ceramics in terms of complex shape, material used, ceramic resin, debinding and sintering control, and theoretical background for accuracy and high resolution is presented along with their distinctive features. Within 2021–2026, the DLP printing market is expected to reach a GR of 5.9%. The current findings shed light on the potential of DLP printing open windows in ceramic materials, which is are very promising, a step forward to achieving sophisticated structures with great versatility and efficiency. This review article is devoted to ceramics and their oxides (Al 2 O 3 , ZrO 2 , and kaolin), non-oxides (Si 3 N 3 and SiC), and Al 2 O 3 -reinforced ZrO 2 composites, whose morphologies are elaborated in depth. Some key factors are prioritized, such as the ceramic resin, photoinitiator, monomers, and suspension, which may facilitate the scalability of the desired printing. A summary table concludes with the operating conditions, materials, and fundamental aspects of physiochemical and thermomechanical features in large-scale manufacturing.

Fused filament fabrication (FFF), an economic additive manufacturing (AM) method, is largely used for the fabrication of customized components (of medical, engineering, architectural, toy, artistic, etc. industries). However, the poor mechanical and surface properties are critical barriers limiting the growth of FFF. Therefore, a novel heat treatment approach has been utilized to improve the overall performance of printed parts. The parts were made with acrylonitrile-butadiene-styrene (ABS) with three infill densities (20, 60, and 100%) and annealing was carried out by changing the levels of temperature (105, 115, and 125 °C) and time duration (20, 25, and 30 min). The experimental design was conducted by Taguchi orthogonal array while the optimization was conducted using Taguchi S/N approach. The investigated responses were surface roughness, hardness, dimensional accuracy, tensile strength, flexural strength, and impact strength. Moreover, the reliability of the mechanical properties, with higher error (α > 5%), was verified by using the Weibull statistic to determine the survival rate of annealed FFF parts for functional applications. The adopted annealing approach was found to improve the physical and mechanical properties. The SEM analysis of fractured specimens revealed the type of failure (ductile or brittle). In recapitulation, the annealing process improved the quality characteristics of FFF parts.

Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque–Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%.

To eliminate the negative effect of traditional metal-working fluids and achieve sustainable manufacturing, the usage of nano-enhanced biolubricant (NEBL) is widely researched in minimum quantify lubrication (MQL) machining. It’s improved tool wear and surface integrity have been preliminarily verified by experimental studies. The previous review papers also concluded the major influencing factors of processability including nano-enhancer and lubricant types, NEBL concentration, micro droplet size, and so on. Nevertheless, the complex action of NEBL, from preparation, atomization, infiltration to heat transfer and anti-friction, is indistinct which limits preparation of process specifications and popularity in factories. Especially in the complex machining process, in-depth understanding is difficult and meaningful. To fill this gap, this paper concentrates on the comprehensive quantitative assessment of processability based on tribological, thermal, and machined surface quality aspects for NEBL application in turning, milling, and grinding. Then it attempts to answer mechanisms systematically considering multi-factor influence of molecular structure, physicochemical properties, concentration, and dispersion. Firstly, this paper reveals advanced lubrication and heat transfer mechanisms of NEBL by quantitative comparison with biolubricant-based MQL machining. Secondly, the distinctive filmformation, atomization, and infiltration mechanisms of NEBL, as distinguished from metal-working fluid, are clarified combining with its unique molecular structure and physical properties. Furtherly, the process optimization strategy is concluded based on the synergistic relationship analysis among process variables, physicochemical properties, machining mechanisms, and performance of NEBL. Finally, the future development directions are put forward aiming at current performance limitations of NEBL, which requires improvement on preparation and jet methods respects. This paper will help scientists deeply understand effective mechanism, formulate process specifications, and find future development trend of this technology.

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.

Now-a-days, the application of hard tuning with CBN tool has been massively increased because the hard turning is a good alternative to grinding process. However, there are some issues that need to be addressed related to the CBN grades and their particular applications in the area of hard turning process. This experimental study investigated the effects of three different grades of CBN insert on the cutting forces and surface roughness. The process of hard turning was made using the AISI H13 die tool steel at containing different hardness (45 HRC, 50 HRC and 55 HRC) levels. The work material were selected on the basis of its application in the die making industries in a range of hardness of 45–55 HRC. Optimization by the central composite design approach has been used for design and analysis. The present study reported that the cutting forces and surface roughness are influenced by the alloying elements and percentage of CBN in the cutting tool material. The work material hardness, feed rate and cutting speed are found to be statistically significant on the responses. Furthermore, a comparative performance between the three different grades of CBN inserts has been shown on the cutting forces and surface roughness at different workpiece hardness. To obtain the optimum parameters from multiple responses, desirability approach has been used. The novelty/robustness of the present study is represented by its great contribution to solve practical industrial application when is developed a new process using different CBN grades for hard turning and die makers of workpiece having the hardness between 45 and 55 HRC.