Tin-Chih Toly Chen and Horng-Ren Tsai
The purpose of this study is to model a multisource uncertain unit-cost learning process to estimate the future unit cost of manufactured products.
Abstract
Purpose
The purpose of this study is to model a multisource uncertain unit-cost learning process to estimate the future unit cost of manufactured products.
Design/methodology/approach
A multilayer fuzzy neural network (FNN) is constructed to model a multisource uncertain unit-cost learning process. A fuzzy constrained gradient descent algorithm is proposed to train the FNN.
Findings
The proposed methodology was applied to a wafer fabrication factory. Wafer fabrication, a well-known additive manufacturing process, is a highly competitive industry; therefore, the manager of a wafer fabrication factory is concerned about the unit cost of each product. This cost can be reduced through learning processes, but these involve much uncertainty, making the estimation of the unit cost a challenging task. Existing methods for modeling these processes and outcomes cannot account for multiple learning sources. However, the multilayer FNN constructed in this study successfully addressed these problems and improved the accuracy of the unit cost estimation by 88 per cent in a real case study.
Originality/value
Modeling an uncertain unit-cost learning process is an innovative application of an FNN. In addition, the proposed methodology is the first attempt to separate the effects of several learning sources, which is considered conducive to the estimation performance.
Details
Keywords
Hsin-Chieh Wu and Tin-Chih Toly Chen
This study aims to investigate issues of quality and quality control (QC) in three-dimensional (3D) printing by reviewing past work and current practices. Possible future…
Abstract
Purpose
This study aims to investigate issues of quality and quality control (QC) in three-dimensional (3D) printing by reviewing past work and current practices. Possible future developments are also discussed.
Design/methodology/approach
After a discussion of the major quality dimensions of 3D-printed objects, the applications of some QC techniques at various stages of the product life cycle (including product design, process planning, incoming QC, in-process QC and outgoing QC) are introduced.
Findings
The application of QC techniques to 3D printing is not uncommon. Some techniques (e.g. cause-and-effect analysis) have been applied extensively; others, such as design of experiments, have not been used accurately and completely and therefore cannot optimize quality. Taguchi’s method and control charts can enhance the quality of 3D-printed objects; however, these techniques require repetitive experimentation, which may not fit the work flow of 3D printing.
Originality/value
Because quality issues may discourage customers from buying 3D-printed products, enhancing 3D printing quality is imperative. In addition, 3D printing can be used to manufacture diverse products with a reduced investment in machines, tools, assembly and materials. Production economics issues can be addressed by successfully implementing QC.