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This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

TO make alloy steel we draw almost entirely upon material from outside the United States. We produce our own molybdenum. Our nickel comes from Canada and so does a part of our copper. Manganese and chromium are nearly all imported. We produce some tungsten and substantial amounts of vanadium. Tin, columbium and other vital materials are imported.

FROM a machinability aspect, stainless steels may be classified into three categories, the general analysis of which influences the machinability factor.

The purpose of this paper is to outline and document the failure root cause of a carbide cutting tool during machining of a hardened tool steel under automatic machining conditions.

Optical metallography and SEM/energy dispersive spectroscopy analysis, together with optical profilometry were employed for failure investigation. The use of an alternative cutting tool and modification of machining conditions are proposed as a failure preventive action.

Severe abrasive wear and adhesion of machining chips are observed in the flank zone, causing blunting of the cutting edge. The revision of cutting conditions, together with the use CBN-based tool insert leads to an overall improvement of the stability of the process and tool lifetime.

This paper places emphasis on a failure analysis case history following a structured approach in industrial machining problem solving, highlighting suggestions for process improvement.

Outlines a new method of discovering original documents related to management history. Uses seemingly insignificant statements in books, articles or original documents to locate documents not listed on any computer database or public archive records, but which are undiscovered in attics or basements. The method involves the use of sources not commonly used by management scholars: obituaries, wills, cemetery records, deeds, land‐ownership maps, city directories and court records. Provides two examples to illustrate the discovery of actual documents: (1) the discovery of ten years of correspondence between F.W. Taylor and S. Thompson on the time required to do work, and (2) new evidence on F.W. Taylor’s interest in high‐heat treatment of tool steel leading to high‐speed steel and in shovels and shovelling. Finally presents new evidence on Taylor’s secret agreement with Bethlehem Steel to give favourable testimony in a patent case in exchange for a free licence for the high‐speed steel process Taylor had sold to Bethlehem for more than $50,000 in 1901.

Chester Street, Aston, Birmingham, 6. The ‘Donald’ Patent Barrel Lifter Truck and Stand, the three‐in‐one appliance. Barrels up to 7 cwts. lifted and transported by one man. ‘Donald’ Patent Barrel Lifter Stands for Oil Stores.

Additive manufacture (AM) such as selective laser melting (SLM) provides significant geometric design freedom in comparison with traditional manufacturing methods. Such freedom enables the construction of injection moulding tools with conformal cooling channels that optimize heat transfer while incorporating efficient internal lattice structures that can ground loads and provide thermal insulation. Despite the opportunities enabled by AM, there remain a number of design and processing uncertainties associated with the application of SLM to injection mould tool manufacture, in particular from H13/DIN 1.2344 steel as commonly used in injection moulds. This paper aims to address several associated uncertainties.

A number of physical and numerical experimental studies are conducted to quantify SLM-manufactured H13 material properties, part manufacturability and part characteristics.

Findings are presented which quantify the effect of SLM processing parameters on the density of H13 steel components; the manufacturability of standard and self-supporting conformal cooling channels, as well as structural lattices in H13; the surface roughness of SLM-manufactured cooling channels; the effect of cooling channel layout on the associated stress concentration factor and cooling uniformity; and the structural and thermal insulating properties of a number of structural lattices.

The contributions of this work with regards to SLM manufacture of H13 of injection mould tooling can be applied in the design of conformal cooling channels and lattice structures for increased thermal performance.

This paper aims to examine the performance of the machining parameters used in the hard-turning process of DIN 1.2738 mold steel and identify the optimum machining conditions.

Experiments were carried out via the Taguchi L18 orthogonal array. The evaluation of the experimental results was based on the signal/noise ratio. The effect levels of the control factors on the surface roughness and flank wear were specified with analysis of variance performed. Two different multiple regression analyses (linear and quadratic) were conducted for the experimental results. A higher correlation coefficient (R²) was obtained with the quadratic regression model, which showed values of 0.97 and 0.95 for Ra and Vb, respectively.

The experimental results indicated that generally better results were obtained with the TiAlN-coated tools, in respect to both surface roughness and flank wear. The Taguchi analysis found the optimum results for surface roughness to be with the cutting tools of coated carbide using physical vapor deposition (PVD), a cutting speed of 160 m/min and a feed rate of 0.1 mm/rev, and for flank wear, with cutting tools of coated carbide using PVD, a cutting speed of 80 m/min and a feed rate of 0.1 mm/rev. The results of calculations and confirmation tests for Ra were 0.595 and 0.570 µm, respectively, and for the Vb, 0.0244 and 0.0256 mm, respectively. Developed quadratic regression models demonstrated a very good relationship.

Optimal parameters for both Ra and Vb were obtained with the TiAlN-coated tool using PVD. Finally, confirmation tests were performed and showed that the optimization had been successfully implemented.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

A CRITICAL stage in the process of making aircraft occurs with alarming frequency in the history of aviation.