SPECIALTY NATURAL RUBBER LATEX FOAM: FOAMABILITY STUDY AND FABRICATION PROCESS

ABSTRACT Specialty natural rubber (SpNR) latex, namely, deproteinized natural rubber (DPNR) latex and epoxidized natural rubber (ENR) latex, has been produced to meet specific product's requirements. However, SpNR is normally used in the form of block rubber to manufacture dry rubber products such as tires and automotive parts. The applications of SpNR latex into latex foam products will be diversified. Findings indicate that foamability of SpNR latex is lower compared to normal latex (LATZ) but shows longer stability time after foamed. Findings also indicate that foam collapse and foam coagulate are two main challenges in the fabrication process of SpNR latex foam. Despite these challenges, SpNR latex foam can be fabricated at different density levels. During the foaming process, additional foaming agent is required to fabricate a SpNR latex foam, which is different from fabricating a normal NR latex foam, especially at low latex foam density. Consequently, a higher level of sodium silicofluoride, used as the gelling agent, is required to set the cell structure of the foam. Findings also indicate that foam density influenced the gelling time and volume shrinkage of the SpNR latex foam. An ideal compounding, foaming, and gelling formulation to fabricate SpNR latex foam via Dunlop batch foaming process has been developed. Morphological study showed that all latex foams are open-cell structure, with lower density foam exhibiting higher porosity and mean pore size. Comparison on hysteresis behavior between DPNR and ENR latex foam indicated that ENR latex foam exhibits higher hysteresis loss ratio compared to DPNR latex foam.

Different Phenomena Encountered during Dilatometry of Low-density Steels

In recent decades, highly alloyed low-density steels are being developed to reduce the weight of different automotive parts. Dilatometry can be a very useful experimental technique to understand phase transformations during heating or cooling of new low-density steel alloys. When performing dilatometry measurements some assumptions are made such as the homogeneity of the sample material tested during the experiment. In this study, dilatometry tests were performed for two different low-density steels, and the variations of the composition between the surface and the inner part of the sample were analyzed. The migration of manganese by diffusion from the interior of the samples and finally its evaporation on the surface under vacuum were observed. This compositional gradient generated in the samples may influence the veracity and interpretation of the results obtained in dilatometry when working with high manganese steel alloys. The detachment of surface grains created by this compositional change near the surface of the samples is also investigated.

Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.