Search results

The purpose of this paper is provide a broad view for the standardization efforts of color quality evaluation of color 3D printing techniques. Further, this review paper demonstrates the processes and color properties of most color 3D printing techniques with specific devices and applications to extend the range of possible memberships of standardization group.

Six color 3D printing techniques including plastic-based, paper-based, powder-based, organism-based, food-based and metal-based color 3D printing have been introduced and illustrated with colorization principles and forming features in detail. Moreover, for printed 3D color objects, literature about color measurement, color specification and color reproduction are described and analyzed, respectively.

Four color 3D printing techniques including plastic-based, paper-based, powder-based and food-based color 3D printing show great affinity toward standardization of color quality evaluation, while their colorization principles indicate that it is difficult with a single standard frame. It is possible to develop a completed color quality evaluation standard for color 3D printing based on approaches in color 2D printing when color measurement method and devices are standardized together.

The paper provides an important guide focusing on the efforts to standardize the colorization processes and color quality evaluation of the color 3D printing techniques.

The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values.

A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts.

This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity.

Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study.

The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities.

This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.