AN EMPIRICAL STUDY ON THE CONROL OF REJECTION RATE IN INSTRUMENT CLUSTER ASSEMBLY LINE BY USING QUALITY CONTROL TOOLS AND KAIZEN

In today’s competitive world, any organization has to do mass production but mass production has both advantages and disadvantages. More rejection and more waste formation occurs in mass production. Further wastage leads to decreases in productivity and improvements. Lean production leads to minimizing the wastes and also improving the productivity. Hence profits would be considerably increased and further improvements takes place. Lean production has several benefits over mass production. It reduces the storage area and also helps in cost savings. Quality control tools played an important role in industrial engineering. They use 7 different tools to find the root cause for the defects and also prioritize it. Why – why analysis used to find the causes for the problem in accurate manner. Lot of questions arises which paved the way for the solutions to solve the problems. Kaizen culture should be encouraged. We must conduct Kaizen event weekly to motivate the workers by providing increments and gifts and also share the new ideas among our industrial peoples. Lean manufacturing brings less inventory, less material wastage than other methods. Flow of materials should be properly maintained. This paper discuss about how to control the rejection rate in instrument cluster assembly line by using the seven Quality control tools and the famous Kaizen (Lean technique).

An Audification and Visualization System (AVS) of an Autonomous Vehicle for Blind and Deaf People Based on Deep Learning

When blind and deaf people are passengers in fully autonomous vehicles, an intuitive and accurate visualization screen should be provided for the deaf, and an audification system with speech-to-text (STT) and text-to-speech (TTS) functions should be provided for the blind. However, these systems cannot know the fault self-diagnosis information and the instrument cluster information that indicates the current state of the vehicle when driving. This paper proposes an audification and visualization system (AVS) of an autonomous vehicle for blind and deaf people based on deep learning to solve this problem. The AVS consists of three modules. The data collection and management module (DCMM) stores and manages the data collected from the vehicle. The audification conversion module (ACM) has a speech-to-text submodule (STS) that recognizes a user’s speech and converts it to text data, and a text-to-wave submodule (TWS) that converts text data to voice. The data visualization module (DVM) visualizes the collected sensor data, fault self-diagnosis data, etc., and places the visualized data according to the size of the vehicle’s display. The experiment shows that the time taken to adjust visualization graphic components in on-board diagnostics (OBD) was approximately 2.5 times faster than the time taken in a cloud server. In addition, the overall computational time of the AVS system was approximately 2 ms faster than the existing instrument cluster. Therefore, because the AVS proposed in this paper can enable blind and deaf people to select only what they want to hear and see, it reduces the overload of transmission and greatly increases the safety of the vehicle. If the AVS is introduced in a real vehicle, it can prevent accidents for disabled and other passengers in advance.