Across all industries, material specifications are tightening beyond previously understood process capabilities. Slight shifts in material grade, microstructure, heat treatment, or alloy composition can significantly impact long term material integrity. This study examines the feasibility of noncontact, 100% inline magneto-inductive testing on materials and components to ensure material quality standards. To investigate the hypothesis that material grade, carbon content, density, and alloy composition can be accurately tested in real time during production, an experiment was conducted using magneto-inductive test instrumentation and an encircling coil. The results of the investigation confirmed that 100% of the material in a component could be thus tested, accurately, efficiently, and autonomously verifying that the specified material grade with the proper composition and properties had been used.

Carburization is a common method of hardening steel surfaces to be wear-resistant for a wide range of mechanical processes. One critical characteristic of the carburization process is the increase in carbon content that leads to the formation of martensite in the surface layer. Combustion and spark-OES are two common methods for determination of carbon in steels. However, these techniques do not effectively separate carbon from near surface contaminants, carburized layers, and base material composition. Careful consideration of glow discharge spectroscopy as a method of precisely characterizing carbon concentration in surface layers as part of a production process should be evaluated in terms of how the resulting data align with other common analytical and metallurgical measurements. When used together, glow discharge spectroscopy, optical microscopy, and microhardness testing are all useful, complementary techniques for characterizing the elemental composition, visually observable changes in material composition, and changes in surface hardness throughout the hardened case, respectively. Close agreement between related measurements can be used to support the use of each of these techniques as part of a strong quality program for heat treatment facilities.

Retained austenite may be helpful or detrimental to the life of heat-treated components, but it can be difficult to accurately measure in manufactured steels. Commonly used visual sample investigations are subjective and often incorrect, magnetic measurements require part-specific calibration, and electron backscattering involves expensive equipment, intensive sample preparation, and long measurement times. Recent developments in X-ray diffractometry, however, provide measurements in minutes and can compensate for the influence of carbides in high-carbon steels as well as texture orientations in rolled sheet metals. This paper discusses the use of X-ray diffraction for measuring retained austenite and compares and contrasts it with other methods. It also provides a brief review of the formation of austenite and its effect on carburized gears, TRIP steels, and bearings.

This paper discusses the growing use of automation in heat treating and some of the benefits that have been realized in early applications. It provides examples showing how articulated robots are used to load and unload parts on fixtures, how inline 3D cameras facilitate dimensional and distortion control, and how test coupons placed by robots at strategic locations throughout a load are weighed before and after heat treatment to determine if parts in different areas of the load are likely to be carburized to the same degree. It also includes an example of an automatically generated report and explains how binary codes on base trays can be used to automatically upload recipes for specific heat treatments.

The objective of this work is to develop the material and numerical models needed to simulate the carburizing process of an automotive gear. The paper discusses the factors that influence calculation time and accuracy and presents important equations and material property data. It describes how the simulation predicts local carbon content based on diffusion and how quenching computation provides information on stress states and residual stresses. It also explains how to account for the effects of grain growth, volume variation due to phase changes, and transformation plasticity.