Reduced painting defects in the 4-wheeled vehicle industry on product type H-1 using the lean six sigma-DMAIC approach

The current era provides challenges for several automotive industries to be able to compete and maintain the quality of their products. For four-wheeled auto­motive companies, satisfying customers regarding the visual appear­ance of the vehicle body is very important. However, internally, automotive companies still found many defects or failures in painting, amounting to 32.6%. Apart from that, rework also results in additional costs that the company must incur during the painting process. This study aims to clarify types of painting defects, analyze root causes, provide solutions, improve process capabilities, and in­crease the sigma level in the painting process in the four-wheeled vehicle industry. This study uses the Lean Six Sigma method, which is integrated into the DMAIC approach and other improve­ment tools. As a result, this study clarifies four critical defects in the orange peel defects of the painting section, craters, melting, and blur. This study has resulted in several corrective action solutions, including tightening supervision of the performance of painting section operators so that they are consistent and committed to working according to the Standard Operational Procedure (SOP) or work instructions that have been created. A competency matrix is used to evaluate operator performance, which is reported to super­iors and subordinates by the supervisory depart­ment. After carrying out corrective action, this study increased the process capability from 1.17 to 1.92. The higher the capability value, the higher the sigma level. This study also has increased the sigma level from 2.76 to 3.42, meaning an increase of 78%.

Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation

Autonomous surface vehicles (ASVs) are becoming more and more popular for performing hydrographic and navigational tasks. One of the key aspects of autonomous navigation is the need to avoid collisions with other objects, including shore structures. During a mission, an ASV should be able to automatically detect obstacles and perform suitable maneuvers. This situation also arises in near-coastal areas, where shore structures like berths or moored vessels can be encountered. On the other hand, detection of coastal structures may also be helpful for berthing operations. An ASV can be launched and moored automatically only if it can detect obstacles in its vicinity. One commonly used method for target detection by ASVs involves the use of laser rangefinders. The main disadvantage of this approach is that such systems perform poorly in conditions with bad visibility, such as in fog or heavy rain. Therefore, alternative methods need to be sought. An innovative approach to this task is presented in this paper, which describes the use of automotive three-dimensional radar on a floating platform. The goal of the study was to assess target detection possibilities based on a comparison with photogrammetric images obtained by an unmanned aerial vehicle (UAV). The scenarios considered focused on analyzing the possibility of detecting shore structures like berths, wooden jetties, and small houses, as well as natural objects like trees or other kinds of vegetation. The recording from the radar was integrated into a single complex radar image of shore targets. It was then compared with an orthophotomap prepared from AUV camera pictures, as well as with a map based on traditional land surveys. The possibility and accuracy of detection for various types of shore structure were statistically assessed. The results show good potential for the proposed approach—in general, objects can be detected using the radar—although there is a need for development of further signal processing algorithms