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Among the challenging materials used in high-temperature applications is Inconel 625. Due to its low thermal coefficient and greater strength, traditional methods tend to produce poor results when it comes to turning Inconel 625. In order to overcome these issues, a new approach has been proposed that utilizes unconventional techniques. WEDM is a variant of the electrical discharge manufacturing process that is commonly used in the production of complex components. It is mainly utilized for the hard to machine parts. A study on the process parameters of WEDM for the machining of Inconel 625 was performed by utilizing the analysis of Taguchi. The study focused on the various parameters of the process, such as peak current, pulse on time, and off time. The performance measures that were considered in this study included surface roughness and material removal rate. The results of the analysis revealed that the various process variables affected the performance indicators.

Nickel-based superalloys are frequently adopted in various engineering applications, such as the production of food processing equipment, aerospace parts, and chemical processing equipment. Because of higher strength and thermal conductivity, they are often regarded as difficult-to-machine materials in certain processes. Various methods were evolved for machining the hard materials such as Nickel-based superalloys more effective. One of these is wire electrical discharge machining. In this paper, we will discuss the development of an artificial neural network model and an adaptive neuro-fuzzy inference system that can be used to predict the future performance of Wire Electrical Discharge Machining (WEDM). The paper uses the Taguchi and Analysis of Variance (ANOVA) design techniques to analyze the model’s variable input. It aims to simulate the various characteristics of the process and its predicted values.

The results of emprical investigations of the drilling, tapping, and surface grinding characteristics of D6aC alloy steel hardened to 54 Rc are reported. This material is machined readily by these processes under appropriate cutting conditions; however, this hardness level appears to be an upper limit to the use of steel drills and taps. A new type of tap, characterized by a unique land geometry, has been developed and tested; this has proved substantially superior to conventional geometries for the present application. Recommendations are formulated for machining this material by each of these processes, and for certain design limitations attendant thereto.

AA 2014 is a copper based aluminium alloy which is having exceptional mechanical characteristics such as better strength, ductility and lesser fatigue. AA 2014 is most generally employed in various engineering applications such as fabrication of structural components, defence applications and manufacturing of aerospace components. Also, this material possess better resistant to corrosion which makes this material best suitable for numerous engineering applications. Unconventional methods of machining have been evolved for producing intricate shapes in electrically conductive components. Wire Electrical Discharge Machining (WEDM) is one among the unconventional machining method which is used for making intricate shape on any electrically conductive work material. In this work, an experimentation has been carried out on WEDM of AA 2014 alloy, employing Taguchi’s technique.

The advanced lifestyle demands materials that are light and robust, and aluminum and its alloys are commonly used in various engineering components due to their exceptional properties such as light weight, enhanced strength, and being economically affordable. Due to their superior mechanical properties, such as strength and flexibility, are commonly used in various industrial applications. Metal Matrix Composites (MMCs) are very essential materials used in several applications as they are more robust and harder than any conventional material. In this study, a metal matrix composite made of aluminum and Boron Nitride (BN) is investigated to analyze its various properties. The study is performed by using Wire Electrical Discharge Machining (WEDM). The three independent parameters of the composite are its pulse on time, peak current, and pulse off time. The study aims to analyze the effects of various process variables on the desired performance of the metal matrix composite.

The use of additives to improve the machinability of powder metal parts is discussed. It is concluded that no single additive has yet been identified satisfying all requirements in relation to cost, toxicity, effectiveness, and influence on sintered strength. The significant influence of mix composition (copper and graphite levels) on machinability is illustrated by drilling force measurements. Elemental manganese additions were found to be beneficial provided graphite content did not exceed 0.6%.

Inconel 600 is a face-centered cubic structure and nickel-chromium alloy. Alloy 600 has good resistance to oxidation, corrosion-resistant, excellent mechanical properties, and good creep rupture strength at a higher temperature. Alloy 600 is used in heat treating, phenol condensers, chemical and food processing, soap manufacture, vegetable, and fatty acid vessels. In this context, the present paper investigates the machinability characteristics of Alloy 600 under dry environment. Also, the parametric effect of cutting speed, feed rate, and cutting depth on the force, surface roughness, and tool wear is carried out using 3-Dimensional surface and 1-Dimensional plots. The optimal parameters are determined systematically based on Taguchi-desirability analysis with turned with TiAlN coated carbide insert. From the graphical analysis of collected data, the low rate of feed and moderate cutting for roughness and cutting force and average feed rate for tool wear with low cutting depth.

Connecting rods manufactured by the Precision Powder Forged (P/F) process offer several distinct advantages over those produced by all other methods including the state-of-the-art forged steel process. Precision P/F connecting rods have mechanical properties equivalent to those made from forged steel, with the added benefits of greater design flexibility, superior dimensional and weight precision, simplified finish machining and assembly, better machinability, and increased consistency because of highly stable metallurgy and a robust and reliable manufacturing process. The inherent flexibility of the P/F process also facilitates tailoring materials to achieve the optimal balance of strength and machinability for a given application. In combination, these advantages result in a product that requires less capital investment for finish machining, is more environmentally conscious by generating substantially less waste, exhibits better total performance, and has lower total cost.

The relationship of machinability to the carbon, sulfur, phosphorus, nitrogen, and other alloy contents of cold-drawn steels is discussed. Two useful measures of machinability are provided by procedures which relate the solid-solution hardening effects of the alloying elements to carbon equivalents in hot-rolled steels, and the eutectoid-carbon lowering effects of the alloys to microstructures in annealed steels. Finally, attention is directed to the fact that cold working by extrusion improves the machinability of low-carbon steel.

A small addition of magnesium (0.3%) to prime 380 die casting alloy (SAE 308) was found to improve the alloy's machinability. Magnesium hardens the matrix and by doing so reduces the tendency to build up on a tool edge, results in shorter and tighter chips, provides a better workpiece surface finish, giving prime 380 desirable machining characteristics similar to those of secondary alloy. However, tool wear rates for the magnesium-modified prime alloy were significantly lower than those for secondary alloy. Other minor/impurity element alloy variations also affected machining characteristics but less dramatically than the magnesium. Some elements traditionally credited with improving machinability were found in this study to be of little benefit.

Modern aircraft increasingly require the use of new and advanced high-strength lightweight materials to ensure that the performance criteria of the aircraft are met: under all flight conditions. The introduction of these advanced materials may, however, escalate manufacturing costs because of potential difficulties in machining and assembly operations. Consequently, it becomes essential to perform extensive tests to establish optimum machining/assembly methods for these materials to bring down manufacturing costs. This paper presents in detail the test parameters, data evaluation techniques, results, recommendations and machinability characteristics for advanced aerospace materials – i.e., Kevlar, AF1410 steel, aluminum-lithium alloy and titanium powder metal – in comparison with those of more conventional materials – i.e., HP9-4-20 steel, aluminum 7075-T73 and titanium 6Al-4V.

Since the advent of P/M technology as a near net shape production process, millions of mechanical components of various shapes and sizes have been produced. Although P/M continues to be one of the fast growing shaping processes, it suffers from the inability to produce intricate geometry's such as internal tapers, threads or recesses perpendicular to pressing direction. In such cases application of machining as a secondary forming operation becomes the preferred alternative. However, machining of P/M parts due to their inherent porosity is known to decrease tool life and increase tool chatter and vibration. Consequently, several attempts have been made to improve the machinability of P/M materials by either addition of machinability enhancing elements such as sulfur, calcium, tellurium, selenium, etc., or by resin impregnation of P/M parts.

Values for Shear stress, Specific power and Energy for gray and ductile cast irons have been determined using two orthogonal machining setups. Procedures for determining machinability by tool life testing are reviewed, and compared to orthogonal machining methods. A new method which will produce very precise values for shear stress, specific power and energy is described.

High strength materials have desirable mechanical properties but often cannot be machined economically, which results in unacceptably high finished component cost. MADI™ (machinable austempered ductile iron) overcomes this difficultly and provides the highly desirable combination of high strength, excellent low temperature toughness, good machinability and attractive finished component cost. The Machine Tool Systems Research Laboratory at the University of Illinois at Urbana-Champaign performed extensive machinability testing and determined the appropriate tools, speeds and feeds for milling and drilling (https://netfiles.uiuc.edu/malkewcz/www/MADI.htm). This paper provides the information necessary for the efficient and economical machining of MADI™ and provides comparative machinability data for common grades of ductile iron (EN-GJS-400-18, 400-15, 450-10, 500-7, 600-3 & 700-2) for comparison.

Machinability of SAE 8620 hot rolled steel bare was evaluated. All samples were tested in the normalized condition. Machinability was determined using a CNC turning center with titanium nitride coated cutting inserts. Several heats from different steel suppliers were tested, some contained the machining enhancers calcium and tellurium as well as various sulphur levels. One heat did not contain enhancers for determination of a machinability baseline. Two machining parameters were used: 800 and 1200 c.s.f.m. (constant surface feet per minute), both at a feed rate of .015 i.p.r. (inches per revolution) and .075 in. d.o.c. (depth of cut). All the materials were chemically, mechanically, and metallurgically characterized. This report will show that these machining enhancers can produce less insert damage (flank and crater) than the same grades without machining enhancers.

Powder metallurgy (P/M) industry has been known for the capability of producing near-net-shape parts. Its specific characteristics have resulted in lower production costs and eliminating many secondary machining. However, more and more P/M parts do require additional operations to fulfil their complex geometry features and surface roughness. Many of the machining factors that influence the machinability of cast and wrought steel parts, such as cutting speed, feedrate, coolant, tool geometry and shape, are also considered in the machining of P/M parts. However, composition, structure, and porosity of P/M are additional factors to be considered. Porosity in the P/M structure can decrease the machinability and shorten the tool life. Different variables have been considered in the material composition. Material densities and the free-machining additive manganese sulphide (MnS) are the two main factors of material composition, which dominate the machining performance.

In almost all-manufacturing processes, there is a trade-off between cost and quality. Parameters that indicate quality include surface texture, dimensional accuracy, mechanical properties and uniformity of physical appearance. Unfortunately, parts with good surface finish, precise geometry and high mechanical strength are usually the most expensive to produce. In order to achieve high quality components at reasonable cost, optimisation of machining is essential. This is usually accomplished by judicious selection of proper cutting tool, cutting method, and the various cutting parameters that control the process. It is the intention of this paper to demonstrate research efforts aimed at characterizing and machining of CAPTAL‘90’, a hydroxyapatite (HA) material-suitable for human bone tissue replacement. It is expected that experimental knowledge gained and the results will form basis for modification of hydroxyapatite material for better machining performance.

Based on a previous paper of the author [1] an oil condition index OC is defined. The index is composed of the values of a set of normalized oil parameters weighted by their importance to the machine and the application. The concept of Dimensional Analysis is used to determine the relationships between machine, oil and operating parameters. This process leads to dimensionless groups, which characterize the oil condition monitored for a machine. Defining OC for a specific machine or machine type and including specifics of the use and the environment the machine is operated in, provides operators and managers with Statistical Process Control (SPC) tools to predict maintenance and prevent catastrophic failure. Data from a reliability experiment are presented which illustrates the relationship between the oil properties, machine operating parameters and oil contamination.

Continuous process manufacture, such as oil refineries and breweries, all employ monitoring techniques, based on sensors measuring critical parameters. S.C.A.D.A. software (Supervisory Control And Data Aquisition), is used to analyse data and provide information. This technique allows performance monitoring of equipment, both instantaneously and historically. Failure prediction, using known deterioration patterns, allow Maintenance to minimise production losses, by being able to forward plan corrective activities. Batch-manufacturing industry has been slow to use S.C.A.D.A. monitoring systems because of various incompatible requirements. Systems will often not interface to numerical controls used on, for example, fastening machines. The relatively slow S.C.A.D.A. data scan rates and cyclic machinery has restricted their introduction.

Use of sensors to monitor dynamic performance of machine tools at Ford’s powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up.