Automotive Wiring Harness Research Articles

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

In response to the challenges associated with the misassembly, omission, and low manual inspection efficiency in automobile wiring harness relay assemblies, a novel online detection system has been engineered. This system consists of a mobile-based visual imaging system and an improved YOLOv5-based detection algorithm that tracks human movement to acquire images and videos. The system is coupled with deep learning for real-time detection and recognition for error-proofing the installation process of automotive wiring harness relays. This innovation aims to facilitate error-proof inspection during the assembly process of automotive wiring harness relays. The YOLOv5s model is augmented with an Adaptive Spatial Feature Fusion (ASFF) module, enhancing multi-scale feature integration capabilities. A Global Context Network (GCNet) is incorporated into the C3 module to emphasize target information from a global perspective. Additionally, the replacement of standard Convolution (Conv) modules with Global Sparse Convolution (GSConv) modules in the Neck section effectively reduces computational costs while sustaining overall performance efficacy. The experimental results show that the detection system achieved a comprehensive accuracy rate of 99.2% and an F1 score of 99.29. The system possesses high accuracy and stability, enabling flexible and intelligent target detection applications in the automotive industry.

Specifying task allocation in automotive wire harness assembly stations for Human-Robot Collaboration

Wire harness assembly is normally a manual assembly process that poses ergonomic challenges. As a consequence of the rapidly expanding electrification of vehicles and transportation systems, the demand for wire harnesses can be expected to grow radically, further increasing assembly operator challenges. Thus, automating this assembly process is highly prioritised by production engineers. The rapid development of industrial robot technology has enabled more human-robot collaboration possibilities, simplifying the automation of wire harness process tasks. However, successful automation applications involving humans require efficient and safe allocation of tasks between humans and technology. Unfortunately, present assembly system design methods may be obsolete and insufficient in light of the capabilities of emerging automation technologies such as collaborative robots. This paper presents a design and specification methodology for human-centred manufacturing systems and focuses on collaborative assembly operations in complex production systems. A case study on human-robot collaboration provides an application example from a wire-harness collaborative assembly process. The proposed design methodology combines hierarchical task analysis with assessments of cognitive and physical Levels of Automation (LoAc and LoAp). The assessments are then followed by evaluations of the Levels of human-robot Collaboration (LoC) and the Levels of operator Skill requirements (LoSr) respectively. A task allocation matrix supports the identification of possible combinations of automation and collaboration solutions for a human-centred and collaborative wire harness assembly process. System designers and integrators may utilise the design and specification methodology to identify the potential and extent of human-robot collaboration in collaborative manufacturing assembly operations.

2-Mbps Power-Line Communication Transmitter Based on Switched Capacitors for Automotive Networks

Nowadays, automotive wire harnesses have become very complex systems as the number of electronic components and Electronic Control Units (ECUs) inside a vehicle have increased dramatically. To manage this complexity, and to support the automotive systems of tomorrow, new communication techniques need to be investigated. In this work, a direct modulated Power-Line Communication (PLC) method is proposed to drastically reduce the number of interconnections and give advantages in cost, complexity, and weight. Two transmitter topologies based on a Switching Capacitor approach are presented and implemented in a Test Chip fabricated in a 180 nm HV-CMOS SOI technology. The proposal is validated by communication tests connecting the designed chip and a discrete component-based demonstrator receiver through an unshielded twisted pair cable. Compared to commercially available solutions, the proposed approach can reach a data rate of 2 Mbps, making it able to implement high-speed event-driven networks, such as the Controller Area Network (CAN).

Designing of Integrated Tactical Production-Quality Planning in a Rolling Horizon Under Demand Uncertainty: Case Study in the Automotive Wire Harness Industry

To minimize waste and scrap, reworking becomes an integral part of many manufacturing companies. In this study, the integrated production-quality planning in the tactical horizon considering the customer demand uncertainty is presented. A mixed-integer programming model is developed to find optimal dynamic planning production which minimizes costs and reduces the scrap. After each production cycle, the quality indicators and relative costs for good and poor quality are performed. These indicators are introduced into the developed model to determine the optimal production orders and stock levels considering the reworked and scrapped products. A real case study is presented to illustrate the applicability of the proposed model. By comparing the obtained results with real results from studied company, we proved that this new model gives an optimal production plan.

Automation and industrialisation through global value chains: North Africa in the German automotive wiring harness industry

Debates on the impact of automation on the geography of production are intensifying. Automation, it is argued, will eliminate the comparative advantage of developing countries and drive processes of re-shoring. While some studies have examined this issue at a macro level, there is a need for further research into specific cases and specific global value chains (GVCs). This paper contributes to this discussion by examining the case of the German automotive wiring harness industry. This industry has passed through a phase of offshoring as German firms sought low-cost locations in Eastern Europe and North Africa. Today, German OEMs are pushing their suppliers to automate manufacturing and relocate closer to assembly production in Europe. Through research at the headquarters and in North Africa, we argue that dispersed global production is seen by German OEMs as an unsustainable and costly organisational and geographical fix that is necessitated by the slow progress of automation solutions and facilitated by the availability of low-cost labour within a logistically feasible geographical space. Through this case, we argue that automation represents a threat to the position of some developing countries in GVCs and highlight the need to move beyond the dichotomy of off-shoring/re-shoring into analysing how automation might drive restructuring of GVCs with varied outcomes in production locations.

Simulation Model for Wire Harness Design in the Car Production Line Optimization Using the SimPy Library

The automotive industry is one of the most important economic sectors in the world. At the beginning, vehicles only had mechanical components, so the use of an automotive wire harness was not indispensable. Cars today are equipped with electronic components that, in addition to the basic operations of moving, turning, and stopping, perform more and more functions every day. Wiring harnesses are indispensable for controlling these electronic components. Automotive wiring harnesses have hundreds of variants, are principally manufactured with customized designs, and are measured specifically for each car. A large number of production variants increase labor hours, as well as rework, inventory, and manufacturing costs. Even when technologies exist to assist in the design of production lines, today, the design of production lines is mainly based on experience from previous cases. This paper aims to show how a discrete event simulation permits support for decision making for the proper design of assembly lines, as well as identifying possible unbalances in production lines and overloaded processes. In our work, we design and implement a discrete event simulation model of this production using the SimPy Python library. Finally, a case study in the automotive sector is presented, a production week is simulated, and the current plant configuration and possible improvement scenarios are analyzed.

Evaluation of Engineering Changes Based on Variations from the Model of PGE – Product Generation Engineering in an Automotive Wiring Harness

AbstractEngineering change management is a central part of the product development process. This paper investigates how variations from the PGE - Product Generation Engineering can improve the evaluation of engineering changes from the wiring harness. Engineering changes that occur in an automotive wiring harness development process are analysed in a case study, evaluated in expert interviews with regard to the risk and effort connected to the implementation and compared to the types of variations. Additional influencing factors are discussed. The variations provide an indication on risk and effort.

Improving engineering change management by introducing a standardised description for engineering changes for the automotive wiring harness

Engineering change management is a key part in the development of products that requires a lot of resources and time. A key problem is the lack of a shared ontology to describe engineering changes. This creates problems, additional effort and hinders the digitalisation of the engineering change management. This is especially true for the development of the automotive wiring harness where a low degree of automation together with the occurrence of many changes in a multi-variant system poses a big challenge. A description that is unambiguous, comprehensive and coherent is needed. The research presented in this paper tackles this problem. A standardised description for the engineering change management for the automotive wiring harness is introduced in this publication. The authors outline the approach that has been used to create a systematic description. The validation of the standardised description is based on two approaches: a case study of a development project and an ongoing development project. The validation shows that 94% of all engineering changes can be described in the proposed standardised way. Concepts where the standardised descriptions can be used to improve the engineering change process are outlined at the end of the paper. The paper thereby presents a way that directly improves the engineering change process and the product development process. It enables the further improvement of the engineering change management by providing a basis for an automatic processing, evaluation and implementation of engineering changes.

Enabling deep learning using synthetic data: A case study for the automotive wiring harness manufacturing

The wiring harness manufacturing follows a high-variant manufacturing philosophy with a high degree of manual labor to produce customized products. Therefore, the process step of optical inspection is crucial to monitor, assess, and proactively safeguard the quality of produced wiring harnesses. To automate the optical inspection, which is currently manually conducted by operators, deep learning has become a powerful algorithm outperforming traditional computer vision approaches. Deep learning-based automated optical inspection systems can robustly and reliably detect rigid and deformable components, such as connectors, clips, single wires, and wire bundles. However, the bottleneck for scalable deep learning solutions in the industrial environment and high deep learning model performance is the database for model training and optimization. To address this research gap, we propose a deep learning-based data processing pipeline for automated optical inspection of wiring harnesses using real and synthetically generated point clouds. This paper outlines the process of real and synthetic data generation and evaluates the potential of synthetic data to enrich real data for model training. The data processing pipeline is implemented and experimental findings are generated to deduct important parameters for synthetic data generation and deep learning model training which impact model performance.

Automotive Wiring Harness development with the use of „Follow the Sun” – teams located in different time zones

Demands of automotive industry is especially focused on new solutions. Nowadays, these needs are stronger than have ever been. Manufacturers are constantly looking for new ways to release their products in the shortest possible time. The research described in the paper concerns work organization and project management in automotive industry. It is a part of PhD project, dedicated to the implementation of remote team collaboration, and is focused on wiring harness development. The first part of the paper is devoted to the review of different forms of work organization with special attention paid to Follow The Sun approach. In the second part of the paper, the characteristic of the wiring harness production was described. Especially, the methodology and design processes. The third part of the article presents a proposition of transforming the currently existing development process with the use of remote teamwork solutions. The article concludes with a description of the implementation of the test projects. Selected indicators were introduced to determine the profitability of this implementation.

Defective judgment for automotive wire harness using convolutional neural network

Defective judgment for automotive wire harness using convolutional neural network

Development and application of load profiles for thermal qualification testing of receptacle automotive connectors

Abstract Receptacle contacts often are a weak spot of the reliability in electronic systems. During the application phase of the lifecycle, especially in the automotive wiring harness, connectors experience severe loads such as temperature changes. During qualification testing, accelerated tests simulate these thermal stresses. Yet, only the damage mechanisms relevant for service life must be triggered. However, the increasing complexity of electronic components and wiring harnesses demands a continuous adaptation of test strategies. Therefore, this study develops and applies application-oriented load profiles for thermal qualification testing of connectors. Experiments include load profiles inspired by seasonal changes and daily car usage. The tests are carried out on a relative movement test bench as well as in a thermal cycling testing chamber. Contact resistance progression curves, the surface roughness of the contacts, and the area of the stressed contact zones assist in evaluating the effectiveness of the load profiles. All contacts tested during relative movement experiments show no change in contact resistance. The thermal cycling load profile however results in a significant rise in contact resistance. Additionally, the roughness values show varying damages on the contacts due to the different load profiles.

Vehicular Electrical Distribution System Simulation Employing a Current-Injection Algorithm

Modeling and simulation have been permeating all development areas within the automotive industry, such as structural simulation, powertrain, engine, thermal management, electronics, and also the electrical distribution system (EDS) design. Rapid adoption of simulation techniques has the potential of reducing prototype manufacturing and, thus, vehicle time-to-market. This article provides a methodology toward vehicle EDS simulation based on the current-injection algorithm to calculate electrical variables within the network and detect early phase design problems. This algorithm allows for a high convergence speed of the computed results and is applicable to combustion vehicles, hybrids, or full electrical vehicles (EVs). We compare the simulated results with the measurements on a test bench of a real automotive wire harness and a vehicle prototype to validate the calculated results.

Mechatronic Re-Design of a Manual Assembly Workstation into a Collaborative One for Wire Harness Assemblies

Nowadays, the wire harness assembly process is still manually performed due to the process complexity and product variability (e.g., wires of different kind, size and length). The Wire cobots project, in which this work was conceived, aims at improving the current state-of-art assembly process by introducing in it collaborative robotics. A shared workstation exploiting human abilities and machine strengths was developed to assembly automotive wire harness by means of insulated tape for a real industrial case. In the new workstation, the human deals with the complex task of wire handling, while the robot performs the repetitive and strenuous taping operations. Such a task allocation together with the workstation redesign allow for an improvement of the operator’s well-being in terms of postural conditions and for an increase of the production efficiency. In this paper, the mechanical and mechatronic design, as well as the realization and validation of this new collaborative workstation are presented and discussed.

Manufacturing automation for automotive wiring harnesses

Within the paradigm shift of electrification and autonomous driving, the automotive wiring harness has evolved to a safety-critical system, as it is responsible for the energy and information provision for safety-critical functions like steering and braking. To fulfill higher requirements concerning quality and safety, manufacturing automation is the key-enabler because it supports transparent, reproducible, and comprehensible processes. However, the state-of-the-art wiring harness manufacturing process is characterized by a high degree of manual work reaching up to 90%. In this paper, we systematically identified and clustered research publications and patents for wiring harness manufacturing automation, and analyzed the automation concepts as well as underlying technologies. Based on the findings, we derive research gaps that require an in-depth investigation in the future to increase the degree of automation in the wiring harness manufacturing.

Optimization of the total production time by splitting complex manual assembly processes

In this article, the minimization of the learning content in the total processing time is studied. Research is based on manual automotive wiring harness assembly, with unstable demand, fluctuating order quantities and enormous product variety. Such instability in manual production environment results that assembly is always at the start-up or learning phase, thus, operational times are greater than standard and operational efficiency is significantly reduced. Since a lot of research is done on learning time calculation, there is still lacking studies that address learning time reduction in such production situation. The methodology proposed in this article addresses reduction of learning time by splitting and simplifying complex assemblies of automotive wiring harnesses. Experimental results from the company indicate that this approach enables to optimize learning time and increase operational efficiency.

Design for Automatic Assembly: A new Approach to Classify Limp Components

Abstract Part design is the most important enabler to automate assembly processes. Design work done in companies’ development departments determines most of the product and production costs through material selection and number of components. Part design mostly focuses on functionality but there is still high potential in production processes. To optimize part design for manufacturing and assembly processes first approaches were made between 1960 and 1980. Nowadays, these systematic procedures for optimized component development with respect to production processes are known as ‘Design for Manufacturing and Assembly’ and ‘Design for Automatic Assembly’. Existing part design rules mainly focus on rigid parts. Limp components are avoided in automatic assembly processes, due to limp material characteristics such as strong deformations due to external force or momentum and shape change during manipulation. However, many products, for example automotive wire harnesses, consist of a considerable amount of limp components. A better design for limp components and their joining-partners might be a solution to simplify subsequent automated production processes. The first step therefore is limp component classification. This paper introduces a new scheme of limp component classification oriented towards an abstraction of automated assembly processes. Different limp material characteristics influence several stages of this process. Therefore, relevant limp material characteristics are assigned to the affecting stages. With this classification, the specific challenges in the automated assembly process become clear. This serves as a basis to define challenges for future automated assembly. In addition, the definition of general design rules for limp components concerning automatic assembly becomes feasible. This paper highlights challenges in limp part assembly automation, abstracts automated assembly processes, reviews and discusses first approaches in limp part classification, and introduces a new scheme for limp component classification. The paper concludes with necessary future research based on the proposed limp component classification.

Limp Component Design for Automatic Assembly – Classification Rating System and Design Rules

The success of assembly process automation is strongly influenced by the part design. To support process automation, already in the 1960s and 70s part design rules were developed. These rules exist almost exclusively for rigid parts, however, in reality many products like for example automotive wire harnesses consist of a considerable amount of limp components. In this paper, we derive design rules to support assembly automation for parts containing limp components. The derived design rules are based on our approach for limp component classification. Therefore, we extend this approach with an attribute ranking system and a characteristic classification.

Overview of the State of the Art in the Production Process of Automotive Wire Harnesses, Current Research and Future Trends

Today’s automotive wire harness manufacturing process involves a high volume of manual work. However, current and future application requirements like miniaturization of electronic components, monitoring of process parameters, increasing demand of process documentation and rising wages call for a higher degree of automation.This paper presents the state of the art in wire harness assembly, reviews currently available special purpose machinery and equipment as well as their limitations and highlights the challenges of automation. It discusses potentials for automation, current innovations and research and concludes with an overview of current trends in the automotive industry.

Background noise mitigation of dual microphone system for defect detection in electrical cable connection

The automotive wiring harness forms the nervous system of the modern automobile and is dependent on electrical circuits joined together by a myriad of electrical connector designs. If a defect in a single connection occurs during vehicle assembly, the vehicle systems may not communicate correctly, and the entire vehicle can be sent for rework incurring a potentially significant added cost. Detection of these defects currently relies on human validation, and can be difficult to maintain consistent inspection reliability. In this work, the “clicking” sound corresponding to the successful connection of mating electrical connectors is analyzed in the presence of noise to quantify the ability to detect and classify these types of connections within a simulated work environment. The work herein investigates a methodology to separate this sound signal from background noise using spectral subtraction, and introduces a classification method, demonstrated in a controlled application. Experiments using lab sensing systems were performed to process and analyze signals and the results showed a significant reduction in noise, effectively distinguishing the signal of interest.