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The purpose of this paper is to propose the swimsuit patternmaking method suitable for the body shapes of middle-aged women and to propose the swimsuit pattern development procedures that enable automated swimsuit patternmaking.

To propose the patternmaking method of the swimsuit, which is suitable for the body shape of middle-aged women, the swimsuit patterns were developed in three stages in this study. The measurements of the middle-aged woman body model were compared with the size of the developed pattern in each stage, and the optimal stretch rate of the fabric was defined. In total, 22 items were used for size analysis of developed patterns in each stage.

The swimsuit patternmaking method proposed in this study was derived by considering the body shapes of middle-aged women, desired design and fabric characteristics. Also, a series of processes, including obtaining a raw pattern from the surface of the three-dimensional (3D) human body, designing patterns by the expert, evaluating patterns, drafting the final pattern and the final patternmaking method, was presented.

This study has great significance to provide a manual of swimsuit patternmaking for middle-aged women, which has high tightness and movement compatibility.

The swimsuit patternmaking method proposed in this study is relatively simple because it is based on the human body measurements and delivers the lines and the calculated values clearly and objectively rather than the patterner's intuition does so that it is suitable for the automation of the swimsuit patternmaking for the middle-aged women.

The purpose of this study is to propose the parametric sleeve patternmaking method that enables mass customization.

To propose the parametric sleeve patternmaking method, the required components for parametrization were identified, and required elements for the application of mass customization were extracted as an objective sleeve pattern.

Overall drafting processes, the formulas for lines and points, the values of notches, and the value of the ease of ESMOD method, the angles and the formulas for darts of Müller’s method, and the size of Sleeve Opening and the way of pattern control of Secoli’s method were combined in the new method proposed.

Further study requires a comprehensive analysis of the proposed sleeve patternmaking method and developing the parametric sleeve pattern CAD for mass customization to utilize and commercialize the parametric sleeve patternmaking method.

The parametric sleeve patternmaking method is suitable for mass customization. Especially, even beginners would apply the parametric sleeve patternmaking method proposed in this study to any type of bodice pattern without confusion.

The parametric sleeve patternmaking method proposed is simple and delivers the lines and the calculated values for the sleeve patternmaking clearly and objectively rather than the patterner’s intuition does.

To review a newly developed PCB fabrication process based on a parallel lamination technique.

This paper has been written to introduce the SAVIA process, a new parallel lamination technique for PCB fabrication. The basic concept of the SAVIA process has been described along with the individual process steps and the reliability issues. The advantages of SAVIA process have been also discussed in both economical and technological aspects.

It was found that the parallel lamination technique, a key process for SAVIA, was not only highly flexible and reliable but also a cost‐effective fabrication method for high performance PCB. With the SAVIA process, manufacturing lead‐times can be substantially reduced due to the nature of the parallel processing. It was also confirmed that a highly reliable metal alloy interconnection was created between the core and the adhesive layers during the lamination process. The formed metal alloy contacts showed excellent electrical and physical characteristics. The between layers was precise.

The value of this paper is to introduce a novel PCB fabrication process based on a parallel lamination technique that is superior to conventional build‐up processes from both technological and economical viewpoints. By applying a parallel lamination technique, it is expected that fabrication costs can be lowered due to reductions in manufacturing lead‐time.

Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines.

By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset.

By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped.

Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

The purpose of this paper is to present a methodology for estimating the “price,” or the not-to-loss value, of individual highway assets, which reflects not only the assets’ capital value but also economic productivity, by adopting a productivity-based asset valuation framework. The price tags can be used in prioritizing highway assets in support of transportation asset management processes.

The methodology adopts the utility theory to consider multiple performance measures reflecting the economic productivity generated by the assets, as well as their capital value. Key performance measures are first selected, and their values are retrieved from highway asset management databases. Next, the utility functions representing decision makers’ preferences convert the performance measures into utility values, which adjust the replacement cost (RC) of each highway asset to estimate price tags. To demonstrate its applicability, case studies were conducted for the highway networks of Texas and Washington State in the USA.

The methodology yielded price tags that better reflect the importance of highways’ roles in the economy in comparison to methods where only RCs are used. Furthermore, it was proven to be flexible enough to accommodate local conditions such as varying data availability.

The research provides a practical and reasonable way to prioritize critical highway assets in purport of maintenance and rehabilitation resource allocations, based on their economic productivity as well as physical condition and historical cost information, enhancing the overall efficiency and effectiveness of highway asset management.

As an emerging visualization technology, virtual reality (VR) falls into the dilemma of having great potential but a low adoption degree in the architectural, engineering and construction (AEC) industry. However, few studies paid attention to studying barriers affecting VR’s adoption and their inner mechanisms. This makes AEC users hard to catch the key points for VR’s implementations. This study aims to get a clear structure of these barriers and provide insights for the improvement.

First, 12 major VR-AEC adoption barriers were identified by a systematic literature review and expert interviews (EI). Second, EI and similarity aggregation method were conducted to achieve reliable barrier relationships. Third, interpretive structural modeling was used to establish a multi-level model for barriers. Finally, ten crucial barriers were targeted with a comprehensive strategy framework.

The findings help AEC stakeholders get a thorough understanding of the VR-AEC adoption barriers. Besides, the inner mechanism among barriers is revealed and analyzed, followed by a systematic strategy framework. It is anticipated that users could conduct more effective VR-AEC promotions in the future.

This paper is the first to propose a comprehensive literature review on the VR-AEC adoption barriers. In addition, this paper is novel in building a hierarchy model that explores barriers’ inner mechanism, where structural strategies are proposed.