Abstract. Cooling systems is a key point for hot forming process of Ultra High Strength Steels (UHSS). Normally, cooling systems is made using deep drilling technique. Although deep twist drill is better than other drilling techniques in term of higher productivity however its main problem is premature tool breakage, which affects the production quality. In this paper, analysis of deep twist drill process parameters such as cutting speed, feed rate and depth of cut by using statistical analysis to identify the tool condition is presented. The comparisons between different two tool geometries are also studied. Measured data from vibrations and force sensors are being analyzed through several statistical parameters such as root mean square (RMS), mean, kurtosis, standard deviation and skewness. Result found that kurtosis and skewness value are the most appropriate parameters to represent the deep twist drill tool conditions behaviors from vibrations and forces data. The condition of the deep twist drill process been classified according to good, blunt and fracture. It also found that the different tool geometry parameters affect the performance of the tool drill. It believe the results of this study are useful in determining the suitable analysis method to be used for developing online tool condition monitoring system to identify the tertiary tool life stage and helps to avoid mature of tool fracture during drilling process..
Life Cycle Assessment or LCA method is believed to be a good solution to improve sustainability in a manufacturing process. This method allows designers to identify opportunities to improve the environmental aspects of products at various points in their life cycle. In this paper, the implementation of LCA through the development of an Environmental Impact Assessment Tool (EIAT) is demonstrated via a case study of Volkswagen pulley crankshaft. EIAT is a tool that aids designers to improve the environmental impact in a manufacturing process by designing or producing products with minimal environmental impact and minimal use of resources, such as the material and energy. EIAT also offers the optimization of design solutions to reduce potential environmental impact of a specific product according to its design features. A pulley crankshaft was modelled in a CAD system where the form is fixed to maintain its function. Pulley crankshaft features, such as the type of material, diameter of pocket, stock thickness and diameter are the parameters that were optimized through the Genetic Algorithm encoded in EIAT. EIAT was validated with Eco-It (an established LCA tool) and with actual experiments. Results show a difference of less than 9% error between EIAT with the results produced by Eco-It and the actual experiments.
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