Analysis and Optimization of Machining Hardened Steel AISI 4140 with Self-Propelled Rotary Tools.

It is necessary to improve the machinability of difficult-to-cut materials such as hardened steel, nickel-based alloys, and titanium alloys as these materials offer superior properties such as chemical stability, corrosion resistance, and high strength to weight ratio, making them indispensable for many applications. Machining with self-propelled rotary tools (SPRT) is considered one of the promising techniques used to provide proper tool life even under dry conditions. In this work, an attempt has been performed to analyze, model, and optimize the machining process of AISI 4140 hardened steel using self-propelled rotary tools. Experimental analysis has been offered to (a) compare the fixed and rotary tools performance and (b) study the effect of the inclination angle on the surface quality and tool wear. Moreover, the current study implemented some artificial intelligence-based approaches (i.e., genetic programming and NSGA-II) to model and optimize the machining process of AISI 4140 hardened steel with self-propelled rotary tools. The feed rate, cutting velocity, and inclination angle were the selected design variables, while the tool wear, surface roughness, and material removal rate (MRR) were the studied outputs. The optimal surface roughness was obtained at a cutting speed of 240 m/min, an inclination angle of 20°, and a feed rate of 0.1 mm/rev. In addition, the minimum flank tool wear was observed at a cutting speed of 70 m/min, an inclination angle of 10°, and a feed rate of 0.15 mm/rev. Moreover, different weights have been assigned for the three studied outputs to offer different optimized solutions based on the designer's interest (equal-weighted, finishing, and productivity scenarios). It should be stated that the findings of the current work offer valuable recommendations to select the optimized cutting conditions when machining hardened steel AISI 4140 within the selected ranges.

Preparation, characterization, and analysis of multi-walled carbon nanotube-based nanofluid: an aggregate based interpretation.

Nanofluids are gaining attention as an attractive solution for the sustainable machining of difficult-to-cut materials. Despite the enormous recent work in the literature, there are still contradictions concerning the effect of different preparation factors on the characteristics of nanofluids and the underlying mechanisms governing them. In the present study, the effect of varying the preparation factors, namely, multi-walled carbon nanotube (MWCNT) concentration, sonication time, and surfactant amount on various nanofluid characteristics and the interactions among these characteristics were studied. The characteristics are divided into two categories: (a) dispersion/stability and (b) viscosity/wettability. The analysis showed strong interactions between these two categories which were mainly attributed to aggregates' formation and dynamics. For the stability/dispersion responses, the effect of aggregation and saturation phenomena is discussed in relation to the different preparation factors. Our analysis shows that the nanofluid viscosity is strongly dependent on aggregate morphology. As for wettability, a novel mechanism is proposed and used to explain the nanoparticles' influence on wettability based on the nanolayering theory. Finally, multi objective optimization (MOO) based on grey relational analysis (GRA) was performed. It was found that moderate MWCNT concentration, high sonication time, and low surfactant amount show the optimal characteristics within the current study design variables search domain. The novelty in the present study lies in its consideration of the simultaneous interaction between the nanofluids' properties and stability. Unlike the common practice in the literature, which focuses on one or two aspects of nanofluids, our approach broadens the analysis and provides in-depth insights into the nanofluid as a complete physical system.

Comparative Evaluation of Surface Quality, Tool Wear, and Specific Cutting Energy for Wiper and Conventional Carbide Inserts in Hard Turning of AISI 4340 Alloy Steel.

This paper presents an experimental study into the comparative response of wiper and round-nose conventional carbide inserts coated with TiCN + AL2O3 + TiN when turning an AISI 4340 steel alloy. The optimal process parameters, as identified by pre-experiments, were used for both types of inserts to determine the machined surface quality, tool wear, and specific cutting energy for different cutting lengths. The wiper inserts provided a substantial improvement in the attainable surface quality compared with the results obtained using conventional inserts under optimal cutting conditions for the entire range of the machined lengths. In addition, the conventional inserts showed a dramatic increase in roughness with an increased length of the cut, while the wiper inserts showed only a minor increase for the same length of cut. A scanning electron microscope was used to examine the wear for both types of inserts. Conventional inserts showed higher trends for both the average and maximum flank wear with cutting length compared to the wiper inserts, except for lengths of 200-400 mm, where conventional inserts showed less average flank wear. A higher accumulation of deposited chips was observed on the flank face of the wiper inserts than the conventional inserts. The experimental results demonstrated that edge chipping was the chief tool wear mechanism on the rake face for both types of insert, with more edge chipping observed in the case of the conventional inserts than the wiper inserts, with negligible evidence of crater wear in either case. The wiper inserts were shown to have a higher specific cutting energy than those detected with conventional inserts. This was attributed to (i) the irregular nose feature of the wiper inserts differing from the simpler round nose geometry of the conventional inserts and (ii) a higher tendency of chip accumulation on the wiper inserts.

On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts.

This article reports an experimental assessment of surface quality generated in the precision turning of AISI 4340 steel alloy using conventional round and wiper nose inserts for different cutting conditions. A three-factor (each at 4 levels) full factorial design of experiment was followed for feed rate, cutting speed, and depth of cut, with resulting machined surface quality characterized by resulting average roughness (Ra). The results show that, for the provided range of cutting conditions, lower surface roughness values were obtained using wiper inserts compared with conventional inserts, indicating a superior performance. When including the type of insert as a qualitative factor, ANOVA revealed that the type of insert was most important in determining surface roughness and material removal rate, with feed rate as the second most significant, followed by the interaction of feed rate and type of insert. It was found that using wiper inserts allowed simultaneous increases in feed rate, cutting speed, and depth of cut, while providing better surface quality of lower Ra, compared to the global minimum value that could be achieved using the conventional insert. These findings show that wiper inserts produce better surface quality and a material removal rate up to ten times higher than that obtained with conventional inserts. This clearly indicates the tremendous advantages of high surface quality and productivity that wiper inserts can offer when compared with the conventional round nose type in precision hard turning of AISI 4340 alloy steel.

Comprehensive analysis on orthopedic drilling: A state-of-the-art review.

Bone drilling is a well-known internal fixation procedure to drill a hole, fixing the bone fragments to reduce the susceptibility of permanent paralysis. The success of bone drilling is evaluated based on the extent of osteonecrosis in terms of heat generation, tissue damage, quality of hole, and drilling forces. The appropriate control of cutting conditions, drill geometric parameters, and bone-specific parameters offer bone drilling a viable solution through conventional and non-conventional drilling techniques. The majority of the published research work considers only limited parameters and tries to optimize the drilling parameters and performance measures. However, bone drilling involves numerous conventional and non-conventional drilling parameters and technologies. In order to develop a better understanding of all the studied parameters and performance measures, there is a dire need to develop a framework. The key objective of this review study is to establish a hierarchy of the framework by collecting almost all the parameters studied until now and addressed the relationship between parameters and performance measures to diminish the controversies in the published literature. Therefore, this framework is novel in nature, organizing all the parameters, performance measures, logical comparisons, and limitations of studies. This holistic review can help medical surgeons and design engineers to understand the complicated relationship among parameters and performance measures associated with this state-of-art technologies. Also, modeling, simulations, and optimization techniques are included to explore the application of such techniques in recent advancements in orthopedic drilling.

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach.

Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.

Towards Optimization of Surface Roughness and Productivity Aspects during High-Speed Machining of Ti-6Al-4V.

Nowadays, titanium alloys are achieving a significant interest in the field of aerospace, biomedical, automobile industries especially due to their extremely high strength to weight ratio, corrosive resistance, and ability to withstand higher temperatures. However, titanium alloys are well known for their higher chemical reactive and low thermal conductive nature which, in turn, makes it more difficult to machine especially at high cutting speeds. Hence, optimization of high-speed machining responses of Ti-6Al-4V has been investigated in the present study using a hybrid approach of multi-objective optimization based on ratio analysis (MOORA) integrated with regression and particle swarm approach (PSO). This optimization approach is employed to offer a balance between achieving better surface quality with maintaining an acceptable material removal rate level. The position of global best suggested by the hybrid optimization approach was: Cutting speed 194 m/min, depth of cut of 0.1 mm, feed rate of 0.15 mm/rev, and cutting length of 120 mm. It should be stated that this solution strikes a balance between achieving lower surface roughness in terms of Ra and Rq, with reaching the highest possible material removal rate. Finally, an investigation of the tool wear mechanisms for three studied cases (i.e., surface roughness based, productivity-based, optimized case) is presented to discuss the effectiveness of each scenario from the tool wear perspective.

Towards Optimization of Machining Performance and Sustainability Aspects when Turning AISI 1045 Steel under Different Cooling and Lubrication Strategies.

In this work, an extensive analysis has been presented and discussed to study the effectiveness of using different cooling and lubrication techniques when turning AISI 1045 steel. Three different approaches have been employed, namely dry, flood, and minimum quantity lubrication based nanofluid (MQL-nanofluid). In addition, three multi-objective optimization models have been employed to select the optimal cutting conditions. These cases include machining performance, sustainability effectiveness, and an integrated model which covers both machining outputs (i.e., surface roughness and power consumption) and sustainability aspects (carbon dioxide emissions and total machining cost). The results provided in this work offer a clear guideline to select the optimal cutting conditions based on different scenarios. It should be stated that MQL-nanofluid offered promising results through the three studied cases compared to dry and flood approaches. When considering both sustainability aspects and machining outputs, it is found that the optimal cutting conditions are cutting speed of 147 m/min, depth of cut of 0.28 mm and feed rate of 0.06 mm/rev using MQL-nanofluid. The three studied multi-objective optimization models obtained in this work provide flexibility to the decision maker(s) to select the appropriate cooling/lubrication strategy based on the desired objectives and targets, whether these targets are focused on machining performance, sustainability effectiveness, or both. Thus, this work offers a promising attempt in the open literature to optimize the machining process from the performance-sustainability point of view.

Internal Cracks and Non-Metallic Inclusions as Root Causes of Casting Failure in Sugar Mill Roller Shafts.

The sugar mill roller shaft is one of the critical parts of the sugar industry. It requires careful manufacturing and testing in order to meet the stringent specification when it is used for applications under continuous fatigue and wear environments. For heavy industry, the manufacturing of such heavy parts (>600 mm diameter) is a challenge, owing to ease of occurrence of surface/subsurface cracks and inclusions that lead to the rejection of the final product. Therefore, the identification and continuous reduction of defects are inevitable tasks. If the defect activity is controlled, this offers the possibility to extend the component (sugar mill roller) life cycle and resistance to failure. The current study aims to explore the benefits of using ultrasonic testing (UT) to avoid the rejection of the shaft in heavy industry. This study performed a rigorous evaluation of defects through destructive and nondestructive quality checks in order to detect the causes and effects of rejection. The results gathered in this study depict macro-surface cracks and sub-surface microcracks. The results also found alumina and oxide type non-metallic inclusions, which led to surface/subsurface cracks and ultimately the rejection of the mill roller shaft. A root cause analysis (RCA) approach highlighted the refractory lining, the hot-top of the furnace and the ladle as significant causes of inclusions. The low-quality flux and refractory lining material of the furnace and the hot-top, which were possible causes of rejection, were replaced by standard materials with better quality, applied by their standardized procedure, to prevent this problem in future production. The feedback statistics, evaluated over more than one year, indicated that the rejection rate was reduced for defective production by up to 7.6%.