Sustainable machining of Ti-6Al-4V using cryogenic cooling: an optimized approach

Abstract Cryogenic machining is an effective, sustainable cooling approach in machining hard-to-cut materials. In this work, two multi-objective optimization techniques, namely; non-dominated sorting genetic algorithm, and grey relational analysis, were used to optimize the cutting performance during turning Ti-6Al-4V alloys under flood and cryogenic cooling. The machining performance was optimized in terms of surface roughness, material removal rate, tool performance and cutting forces. The optimal solutions, including cutting conditions and cooling technique, were determined for different machining strategies (i.e. roughing, finishing, and productivity). It was found that cryogenic cooling offers better cutting performance with a higher optimization index than flood approach

Multi-Objective Optimization During Machining Ti-6Al-4V Using Nano-Fluids

Several properties make titanium and its alloy the primary candidate to attain weight and functional advantages because of its promising properties such as high strength to weight ratio, high corrosion resistivity, and high yield stress. Although titanium alloys have superior properties, some inherent characteristics such as high chemical reactivity and low thermal conductivity lead to poor machinability and result in premature tool failure and shortened tool life. In order to overcome the heat dissipation challenge during machining of titanium alloys, nano-cutting fluids are utilized as they offer higher observed thermal conductivity values compared to the base oil. Thus, in the current work, multi-walled-carbon nanotubes (MWCNTs) cutting fluids along with minimum quantity lubrication (MQL) have been employed during machining Ti-6Al-4V. On the other hand, developing a multi-objective optimization model for machining titanium alloys is a promising step in order to minimize machining cost, achieve excellent surface quality, and increase the cutting tool life by selecting the optimal cutting conditions (i.e. cutting speed, feed rate, depth of cut). In this study, response surface methodology (RSM), and genetic algorithm (GA) are employed to model and optimize three main machining responses: tool wear, surface quality, and power consumption. Three main independent processes parameters are considered when machining titanium alloys, namely; cutting speed, feed rate, and percentage of added nano-additives

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

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multi-criteria decision making respectively. This integrated approach, to best of the authors’ knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design

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%

Design for additive manufacturing of composite materials and potential alloys: a review

As a first step of applying additive manufacturing (AM) technology, plastic prototypes have been produced using various AM Process such as Fusion Deposition Modeling (FDM), Stereolithography (SLA) and other processes. After more research and development, AM has become capable of producing complex net shaped in materials which can be used in applicable parts. These materials include metals, ceramics, and composites. Polymers and metals are considered as commercially available materials for AM processes; however, ceramics and composites are still considered under research and development. In this study, a literature review on design for AM of composite materials and potential alloys is discussed. It is investigated that polymer matrix, ceramic matrix, metal matrix, and fiber reinforced are most common composites through AM. Furthermore, Functionally Graded Materials (FGM) is considered as an effective application of AM because AM offers the ability to control the composition and optimize the properties of the built part. An example of FGM through using AM technology is the missile nose cone which includes an ultra-high temperature ceramic graded to a refractory metal from outside to inside and it used for sustaining extreme external temperatures. During this work, different applications of AM on different classifications of composite materials are shown through studying of industrial objective, the importance of application, processing, results and future challenges

Towards Sustainable Machining of Inconel 718 Using Nano-Fluid Minimum Quantity Lubrication

Difficult-to-cut materials have been widely employed in many engineering applications, including automotive and aeronautical designs because of their effective properties. However, other characteristics; for example, high hardness and low thermal conductivity has negatively affected the induced surface quality and tool life, and consequently the overall machinability of such materials. Inconel 718, is widely used in many industries including aerospace; however, the high temperature generated during machining is negatively affecting its machinability. Flood cooling is a commonly used remedy to improve machinability problems; however, government regulation has called for further alternatives to reduce the environmental and health impacts of flood cooling. This work aimed to investigate the influence of dispersed multi-wall carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3) gamma nanoparticles, on enhancing the minimum quantity lubrication (MQL) technique cooling and lubrication capabilities during turning of Inconel 718. Machining tests were conducted, the generated surfaces were examined, and the energy consumption data were recorded. The study was conducted under different design variables including cutting speed, percentage of added nano-additives (wt.%), and feed velocity. The study revealed that the nano-fluids usage, generally improved the machining performance when cutting Inconel 718. In addition, it was shown that the nanotubes additives provided better improvements than Al2O3 nanoparticles

Design for Sustainable Manufacturing: Approach, Implementation, and Assessment

The implementation of sustainable systems is an essential requirement in modern manufacturing, in order to minimize the environmental and health concerns, and conserves energy and natural resources. The sustainable manufacturing approach is identified through three main levels, namely: product, process, and system scales. The interactions among these levels provide the required sustainable target. To achieve a sustainable manufacturing system, it is very important to understand and define the concepts and needs related to the sustainability approach. In addition, defining and understanding the implementation steps as well as the assessment method to build a sustainable manufacturing system is required. In this work, a study discussing the sustainable manufacturing approach is presented in terms of concepts, implementation steps, and assessment methods

Experimental Investigation of the Derivative Cutting When Machining AISI 1045 with Micro-Textured Cutting Tools

In the context of satisfying sustainability requirements nowadays, dry machining is one of the ideal strategies to eliminate the environmental and human health burdens of machining processes. In addition, micro-textured cutting tools are used to improve the performance of dry machining processes. Micro-textures reduce the chip-tool contact length and thus reduce friction and heat, which results in fewer cutting forces and temperature. However, the action of micro-cutting of the bottom side of the chip, which is known as derivative cutting, cuts down the gains of using textured tools, where derivative cutting leads to higher cutting forces, heat, and tool wear. This study aimed to investigate the effects of significant texture design parameters (i.e., micro-groove width) when cutting AISI 1045 steel using different machining parameters (i.e., 75 m/min and 150 m/min of cutting velocity, 0.05 and 0.10 mm/rev of feed). Three different textured cutting tool designs were prepared using the laser texturing technique and then utilized in machining experiments. In addition, the measured machining outputs were forces, power consumption, flank wear, and surface roughness. There were no marks for the derivative cutting when using the textured cutting tool with the narrowest micro-grooves according to the obtained microscopical images after the machining tests. In addition, the textured cutting tool, which included the narrowest micro-grooves, showed better performance compared to the non-textured cutting tool and the other textured tool designs in terms of cutting and feed forces, power consumption, flank tool wear, and surface roughness at the used cutting conditions. This confirmed that the careful optimal design of the micro-textured tools can reduce or eliminate the severity of the derivative cutting, and thus improve the overall machining performance

A Decision-Making Approach for Sustainable Machining Processes Using Data Clustering and Multi-Objective Optimization

Achieving sustainable machining processes has become crucial in many industries in order to support sustainable development goals (e.g., good health and well-being, decent work and economic growth, affordable and clean energy). Many attempts have been made to optimize the sustainability aspect during machining processes and to offer optimized cutting conditions. However, there is a vital need to develop a decision-making approach that can be flexible and offer optimal sustainable solutions for different machining scenarios. The current study offers a new decision-making approach for sustainable machining processes using data clustering (i.e., K-means clustering) and multi-objective optimization methods (i.e., grey relational analysis). Utilizing the multi-objective optimization after the clustering phase provides the decision maker with optimal and sustainable cutting conditions for different clusters. The developed approach is validated through a case study that includes five design variables (i.e., feed, speed, nose radius, cooling strategy, and rake angle), three machining outputs (i.e., surface roughness, specific energy, and unit volume machining time), and four different scenarios (i.e., finishing, roughing, balanced, and entropy). Three clusters were generated, and the obtained results were compatible with the physical meaning of each studied scenario. Such an approach can provide the decision maker with sufficient flexibility to select the optimal cutting settings for various scenarios, as well as the freedom to switch between clusters and/or scenarios with minimal effort