• Journal of the Korean Professional Engineers Association
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• v.34 no.2
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• pp.10-13
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• 2001

Workpiece materials may be relatively easy to machine by traditional methods but workpiece geometry also may be a constraint. Many shapes that are geometrically difficult to handle conventionally may be candidates for nontraditional processes. Nontraditional processes provide new opportunities for product design innovation and productivity improvements. Difficult-to-machine materials of geometric shapes difficult o produce with traditional equipment and tooling, may often be easily and cost effectively machined using nontraditional processes. Notraditional machining processes are relative newcomers o the manufacturing arena. Nontraditional chemical solutions, or even electrolytic current as the working medium rather than a conventional cutting tool or abrasive to remove or shape materials.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.94-100
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• 2010

Nowadays, several nontraditional machining(NTM) processes are widely used to machine a complex and accurate shape part of hard materials, such as titanium, ceramics, high strength temperature resistant and refractory materials which are difficult to machine and having high strength, hardness, toughness. Machining of these complex shapes in such materials by traditional machining processes are very difficult. The NTM processes is important in the areas of micro- and nano scale machining, where high accuracy and superior surface characteristics are required, which can only be achieved using these NTM processes. So, for effective selection of different NTM processes, careful decision making for a given NTM application is often necessary. An appropriate NTM process for a given material and shape condition is very difficult for the novice engineers. In this paper, an expert system based on an analytic network process(ANP) is suggested for a best selection of NTM process in a NTM application considering an prior interdependency effect among various factors.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.157-164
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• 2001

The grinding for hard-to-machine materials, such as ceramics, super alloys etc., has proven to be a very difficult and consuming process utilizing ordinary methods. In order to conduct high efficiency machining of such materials, grinding processes using metallic bond diamond wheels and applying electrolytic in-process dressing(ELID) have been attempted on a surface grinding machine. In this study, the effects of grinding parameters, and grit sizes have been evaluated in view of surface roughness, grinding force as well as step difference in simultaneous grinding of different materials. The study and experimental results are presented in this paper.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.1-6
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• 2008

Machine operator and quality are affected by chip during cutting process to product machine parts. This paper presents a study of the influence of cutting conditions on the surface roughness obtained by turning using Taguchi method for safety of turning operator. In the machining of titanium alloy, high cutting temperature and strong chemical affinity between the tool and the work material are generated because of its low thermal conductivity and chemical reactivity. Therefore titanium alloys are known as difficult-to materials. An orthogonal array, the signal-to-noise ratio, the analysis of variance are employed to investigate the cutting characteristics of implant material bars using tungsten carbide cutting tools of throwaway type. Also Experimental results by orthogonal array are compared with optimal condition to evaluate advanced reliability. Required simulations and experiments are performed, and the results are investigated.

• Nuclear Engineering and Technology
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• v.52 no.4
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• pp.784-790
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• 2020

The accelerated corrosion by Flow Accelerated Corrosion (FAC) has caused unexpected rupture of piping, hindering the safety of nuclear power plants (NPPs) and sometimes causing personal injury. For the safety, it may be necessary to select some pipes in terms of condition monitoring and to measure the change in thickness of pipes in real time. Direct current potential drop (DCPD) method has advantages in on-line monitoring of pipe wall thinning. However, it has a disadvantage in that it is difficult to quantify thinning due to various thinning shapes and thus there is a limitation in application. The machine learning approach has advantages in that it can be easily applied because the machine can learn the signals of various thinning shapes and can identify the thinning using these. In this paper, finite element analysis (FEA) was performed by applying direct current to a carbon steel pipe and measuring the potential drop. The fundamental machine learning was carried out and the piping thinning model was developed. In this process, the features of DCPD to thinning were proposed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.266-269
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• 2002

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing process. STD11 has been known as difficult-to-cut materials. For this study, the micro-grooving machine was developed. The experiments were performed using diamond blade and CBN blade f3r machining STD11. Evaluating the machining conditions, frequency spectrum analysis of acoustic emission (AE) signals according to each conditions were applied.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.359-364
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• 1999

Due to rapid growth of die and mould industries, it is urgently required to maximize the productivity and the efficiency of machining. In recent years, owing to the development of new kinds of material, die and mould materials are much harder and it is more difficult to cut. In this study, the workpiece SKD11(HRC45) is cut with TiAlN coated tungsten-carbide cutting tools. To find the general characteristics of difficult-to-cut materials, orthogonal turning test is performed. Orthogonal cutting theory can be expanded to oblique cutting model. The oblique cutting process in the small cutting edge element has been analyzed as orthogonal cutting process in the plane containing the cutting velocity vector and chip-flow vector. Hence, with the orthogonal cutting data obtained from orthogonal turning test, the cutting forces can be analyzed through oblique cutting model. The simulation results have shown a fairy good agreement with the test results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.473-476
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• 2005

Today's manufacturing industry is facing challenges from advanced difficult-to-machine materials (WC-Co alloys, ceramics, and composites), stringent design requirements (high precision, complex shapes, and high surface quality), and machining costs. Advanced materials play an increasingly important role in modem manufacturing industries, especially, in aircraft, automobile, tool, die and mold making industries. The greatly-improved thermal, chemical, and mechanical properties of the material (such as improved strength, heat resistance, wear resistance, and corrosion resistance), while having yielded enormous economic benefits to manufacturing industries through improved product performance and product design, are making traditional machining processes unable to machine them or unable to machine them economically. In this paper, mechanical characteristic evaluation test of fine powder type WC-Co alloy was accomplished to obtain clear data for miniaturized special die parts machining with high reliability and high quality.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1167-1174
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• 2022

Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. The main drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operating environment. Even though ultrasonic non-destructive testing has been conducted in major nuclear components and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonic signals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonic technique (UT) and machine learning (ML) models. Several different ML models, specifically the K-nearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models, were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identified that the ML models can predict the category of ultrasonic signals effectively according to the aging condition.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.40-52
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• 1998

Among the non-traditional machining methods, Abrasive waterjet machining process shows big promise in drilling difficult-to-machine materials due to its numerous advantages such as absence of heat affect zone and thermal distortion. Acoustic emission signal technique is used to understand about material removal mechanisms during abrasive waterjet drilling process. More information about the drilling process is derived through frequency decomposition of auto regressive moving average modeling representing acoustic emission signals.