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Abstract:The use of cooling air during machining is an environmentally conscious procedure, and its applicability to different processes is a research priority. We studied tapping operations, an important operation in the assembly process, using cooling air with unreinforced polyamide (PA66) and polyamide reinforced with glass fiber (PA66-GF30). These materials are widely used in industry, but their behavior with respect to tapping has not been studied. We analyze the outcomes regarding the thrust force, torque, and power at cutting speeds between 15 and 60 m/min. The experimental tests were executed using cooling air at 22 • C, 2 • C, and −18 • C in dry conditions. The M12 × 1.75 mm taps were high-speed steel, with cobalt as the base material and coatings of TiN and AlCrN. To identify the more influential factors, an analysis of variance was performed, along with multi-response optimization to identify the desirability values. This optimization shows that the optimum for PA66can be found in environments close to 3 • C, while the optimum for PA66-GF30 is found at the minimal temperature studied (−18 • C). Thus, cooling air can be considered an adequate procedure for tapping operations, to increase the sustainability of the manufacturing processes.

The uncertainty of demand has led production systems to become increasingly complex; this can affect the availability of the machines and thus their maintenance. Therefore, it is necessary to adequately manage the information that facilitates decisionmaking. This paper presents a system for making decisions related to the design of customized maintenance plans in a production plant. This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliable behavior in the medium-term, integrating this new functionality into classic Balance Scorecards, and making it possible to extend their current measuring function to a new aptitude: predicting evolution based on historical data. In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. In addition, this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time to failure.

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Cold expansion technology is a cold-forming process widely used in aeronautics to extend the fatigue life of riveted and bolted holes. During this process, an oversized mandrel is pushed through the hole in order to yield it and generate compressive residual stresses contributing to the fatigue life extension of the hole. In this paper, a parametric analysis of the mandrel geometrical data (inlet angle straight zone length and diametric interference) and their influence on the residual stresses was carried out using a finite element method (FEM). The obtained results were compared with the conclusions presented in a previous parametric FEM analysis on the influence of the swage geometry in a swaging cold-forming process of gun barrels. This process could be considered, in a simplified way, as a scale-up of the cold expansion process of small holes, and this investigation demonstrated the influence of the diameter ratio (K) on the relation between the mandrel or swage geometry and the residual stresses obtained after the cold-forming process.