Lexan polycarbonate for automotive forward lighting

The use of plastics in the automotive industry is increasing due to their versatility, light weight and affordability. To improve their sustainability credentials, incorporation of recycled content is desired, driven by recent European Union mandates for 25% recycled content for automotive plastics. Current methods to quantify recycled content, however, are imprecise and could promote fraud. This paper extends a method to quantify recycled content in packaging plastics using a fluorescent brightener, 4,4′‐bis(2‐benzoxazolyl)stilbene (BBS), to automotive‐grade polymers. BBS aggregates above threshold concentrations to exhibit aggregation‐induced enhanced emission (AIEE). This study investigates whether fluorescence emission, lifetime and colour analyses can be used to quantify recycled content in four automotive plastics with different chemical structures, matrices or fillers. Quantification using BBS AIEE was most successful for semicrystalline polymers with minimal additives (polyamide‐6 and polyamide‐6,6), with high confidence correlations between fluorescence emission and lifetime measurements and recycled content. Detection was suppressed in amorphous polycarbonates and filled polypropylenes, suggesting this quantification technique may be application‐limited. These new insights into quantifying recycled content in automotive plastics point to both opportunities and limitations for fluorescence‐based techniques in assuring a more circular world. © 2026 The Author(s). Polymer International published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The CO2 emission regulations have an increasing impact on the automotive industry, as these regulations are connected with severe penalties in case the average fleet emission is exceeding the limits for Europe, as imposed by the European Commission. These measures are enforcing the automotive industry to a significant increase in fuel efficiency. Basically, the emissions can be reduced, by reducing the wear losses in the powertrain and by reducing the weight of the cars, by using more and more plastic materials. Many of these parts do also have a decorative function and therefor are Chrome plated. As the plating process has to handle heavy poisoning liquids and consumes a lot of water, the industry is looking for green low cost alternatives. In this chapter the Cromatipic® process is introduced which combines a painting with a PVD coating and fulfils all requirements of the automotive industry for interior and exterior decorative parts. Finally will be introduced an integrated inline factory for performing the Cromatipic® process very cost effective.

For every mill the ideal roll can be considered as one that can be used in a mill stand indefinitely. Unfortunately for the roll user, such ideal rolls do not exist. One major reason for work roll changes (planned or unplanned) is “wear”. In the context of cold rolling and temper rolling, “wear” primarily refers to loss of roughness and/or surface texture. Another limitation to roll performance is the fact that mill incidents – minor as well as major incidents – inflict damage on the work rolls, requiring redressing in the roll shop. If a major defect is present in a forged roll, e.g. due to a mill incident, catastrophic roll failure may occur in a violent, explosive manner. Improvement of the value-in-use of a work roll implies a superior wear resistance, a superior damage resistance, and elimination of the safety risks associated with explosive roll failures. In order to deal with this in a concerted R&D approach, a consortium of two roll users in the steel industry, a leading roll manufacturer, a high-tech supplier of roll damage detection equipment, and specialised research institutes have joined forces. Cold rolling trials have been performed, using pilot mills as well as industrial mills, with both novel forged HSS work rolls and conventional forged 3–5%Cr steel work rolls. Separate trials have been designed to focus on either the aspect of roughness evolution or damage resistance. Complementary data from various laboratory tests and industrial mill and roll shop data have been collected. Models have been developed for damage evolution in a roll, and for roughness evolution of the roll surface. In addition, novel non-destructive detection systems and sensors have been designed and tested. This paper provides a concise overview of the results achieved.

<div class="section abstract"><div class="htmlview paragraph">At present, biodiesel fuels using natural-origin materials are expanding in share, and there are many different kinds. Biodiesel fuel generates organic acid when it deteriorates, so care is needed when evaluating the influence of the fuel on automotive fuel-line materials. A model biodiesel fuel was designed taking into account deterioration of the fuel and mixing of impurities into it. Durability of automotive fuel-line rubber and plastic materials were evaluated by using the model fuel. From the evaluation results, it was found that fluoroelastomer (hereafter referred to as FKM) and polyacetal resin (hereafter referred to as POM) deteriorate depending on specific fuel properties and deterioration state. In this paper, we report evaluating results of biodiesel fuels on the automotive fuel-line rubber and plastic materials, and the importance of biodiesel fuel property management.</div></div>

Purpose – The purpose of this paper is to supply indications that may be useful in the process of development of new products that fully exploit the value potential of Internet of Things (IoT) technologies in the automotive industry. To this aim, the authors investigate how applications of the IoT to smart vehicles are perceived by consumers and describe different ways to increase their satisfaction. Design/methodology/approach – After a literature review focused on IoT and consumer behaviour in the automotive industry, the authors apply the Kano model to find the drivers for achieving customer satisfaction with new product developments in smartcars. Findings – Automotive companies need to consider what is attractive to drivers and what consumers consider to be “driver-friendly”. Using an empirical analysis, the authors highlight the motivations for developing a smartcar to fits the expectation of Italian drivers. Research limitations/implications – While the global framework given by this paper can be useful in all contexts, the empirical part is based on the Italian consumer. Further research may extend the application of the model to other countries to improve the generalizability of the results. Practical implications – This study supplies an analysis of qualitative data that may prove useful to researchers and managers in planning their strategic and operative activities with the aim of improving the development of IoT-related products in the automotive industry. Originality/value – This paper fulfils the need for an original research framework that sheds light on relevant insights and gives useful hints on the development of IoT technologies in the automotive sector.

Earnings management practices are common in financial reporting, especially for companies listed on the Indonesia Stock Exchange. This is supported by several examples of cases of earnings management practices which are not aligned on one of the automotive industry and components as occuring at PT. Indomobil Sukses International Tbk and PT Indofarma Tbk. Such cases encourage the researcher to conduct a research on the automotive sub-sector industry and their components. The aim of this study is to obtain empirical evidence about the effect of firm size, and good corporate governance (board of directors, independent directors, audit committee) to earnings management. The populations of this study were 12 companies of sub-sector automotive industry and components listed in Indonesia Stock Exchange. Based on purposive sampling method, the samples obtained were 10 companies that have registered in term of the Indonesia Stock Exchange more than 20 years. Data were obtained from the company's financial statements of automotive industry sub-sector companies and their components of 2017-2021. The hypothesis was tested using multiple linear regression analysis.

In the last two decades, lean product development and design processes, which have rapidly penetrated the automotive industry companies with all their functions and spread globally, have come to life with measurements, benchmarks and flow maps on value creation or value addition in new product project stages. In the literature review, two customer-oriented approaches, namely lean product development and user experience design, have been widely adopted within the discipline of new product design. However, the lack of comparative efficiency measurements or studies addressing their integration in scientific publications constitutes the primary motivation of this research. Furthermore, the selection of the research scope and boundaries is based on convenience sampling, enabled by direct qualitative research with companies managerial or design positions in the Turkish automotive supplier industry. The main objective of this study is to conduct a comparative analysis aimed at integrating user experience activities with lean product development process flows, and to reveal metric values that enhance efficiency through overall system optimization. On the other hand, the most important recommendation of the study is that the basic process will benefit positively by including user experience activities, which are the most important application of customer-oriented design approaches, into the highly efficient lean product development flow. Therefore, the study conducted with the mixed research method was completed under the preference of quantitative face-to-face interview method in the in-depth questioning of the qualitative numerical data obtained by the questionnaire method. User experience activities, which are newer and less widespread than lean product development processes, have also revealed the frequency of project-based, customer-oriented design activities, along with the demographic organizational structure of the participating companies in the automotive industry, such as the age of establishment, number of employees, number of designers, etc.

All of Automotive companies, were quite badly affected during the Covid-19 pandemic. The automotive industry players do not simply surrender to this situation. As Idul Fitri 2020 approaches, the Association of Indonesian Automotive Industries (Gaikindo) synergizes with the Government to maintain the performance of the automotive industry. The situation improved as the Covid-19 cases in the country slumped. In 2020, wholesale sales for the automotive industry in Indonesia will only reach 530 thousand units. This figure is down by 50% from usual due to very limited mobility of people and goods. The automotive sector is gradually improving, car sales are picking up again in August 2021. In the first half of 2022, a number of automotive companies managed to score encouraging performances. This encouraging performance cannot be separated from funding decisions, availability of assets, ability to meet short and long term debts. Research objective to examine and analyze effect of capital structure, liquidity ratios, coverage and activity on financial performance. A total of 13 companies out of 16 automotive companies are listed Indonesia Stock Exchange for the period 2018 – 2021. Using hypothesis testing with multiple linear regression. The results showed only coverage ratio had an effect on financial performance. Novelty in this study uses two state conditions, namely before and during the Covid-19 pandemic.

There are many backward and forward linkages between automotive industry and other industries because of technical and structural properties of that. After the oil industry, the automotive industry is one of the largest and most profitable industries in Iran. Also, this industry has received government extensive attention in order to become competitive. Thus, development of Iranian economy is closely related to the competitive advantage of Iranian automotive industry. So, owing to the importance of this matter, we have tried to develop a Group Method of Data Handling (GMDH) type neural network for stock price prediction of automotive industry. For stock price prediction by the GMDH-type neural network, we are using earnings per share, prediction earnings per share, dividend per share, price-earnings ratio, earnings-price ratio as input data and stock price as output data. For this work, data of eight automotive companies are gathered from Tehran Stock Exchange. The GMDH-type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modelling, reminders of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental results.

Change and innovation are increasingly exerting a significant influence on the daily activities of companies. To ensure optimal control, innovative solutions are employed that are encapsulated in the concept of change management. In the engineering change sector, the proposed approach involves developing solutions and making continuous adjustments to the manufacturing process to enhance productivity and to meet customer needs. Within the automotive industry, companies utilize innovations and process change management to continuously improve and strengthen their position in the market, such as KPI/KPRS and PCI. To achieve this, the present study gathers real digital data from the Romanian branches of two renowned automotive companies. The data regarding change requests include 215 registrations for the first company and 734 registrations for the second company. By employing complex statistical methods such as ANOVA, Student’s t-test, the Mann–Whitney test, and a regression model, the primary objective of this study is to model and to identify the best predictor of change request status. Additionally, this study aims to explore how this change process influences the economic performances of the companies and the performance indicators of change management in manufacturing processes. The findings indicate that, both in the organizations in general and within the automotive industry, when products experience high demand in the market, the number of change requests increases. This highlights the importance of internal optimization of the automation system. Moreover, the study results underscore the crucial role of an effective smart manufacturing and optimal change management system to uphold and to enhance the economic performance of automotive companies.

Purpose: Financial distress has become a critical issue in corporate finance as it reflects a company’s ability to maintain business continuity before experiencing bankruptcy. The purpose of this study is to analyze and compare the accuracy levels of the Altman Z-Score, Springate, and Zmijewski models in predicting financial distress among automotive sub-sector companies listed on the Indonesia Stock Exchange during the 2019–2022 period. Methodology: This study employs a quantitative research approach using secondary data obtained from the annual financial statements of automotive sub-sector companies listed on the Indonesia Stock Exchange for the 2019–2022 period. The sample consists of 44 companies selected based on predetermined criteria. The analysis method involves descriptive statistical analysis and the application of the Altman Z-Score, Springate, and Zmijewski models to measure prediction accuracy in identifying financial distress. Findings: The findings indicate that the Altman Z-Score and Springate models can be used to predict financial distress; however, their accuracy levels are relatively low, at 22% and 61% respectively, with higher type error rates. Conversely, the Zmijewski model demonstrates superior predictive performance with an accuracy rate of 93% and a type error of 7%, suggesting it is the most effective model for predicting financial distress potential among automotive sub-sector companies in Indonesia. These results highlight that the Zmijewski model provides the most reliable identification of financial distress compared to the other models. Originality: The originality of this study lies in its comparative analysis of three well-known financial distress prediction models, Altman Z-Score, Springate, and Zmijewski, explicitly applied to Indonesia’s automotive sub-sector during the post-pandemic period (2019–2022). This focus provides new empirical evidence on model accuracy within an industry significantly affected by economic fluctuations and supply chain disruptions. Practical implications: The findings provide valuable insights for investors, managers, and policymakers in assessing financial distress risk and improving financial decision-making within the automotive industry.

This research aims to analyze the effect of Return on Assets (ROA), Debt to Equity Ratio (DER), and inflation on the share prices of automotive and component industry companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2024 period. The research method uses a quantitative approach with panel data regression analysis. The research population includes all automotive and component industry companies listed on the IDX, with a sample of 13 companies selected using purposive sampling, resulting in 65 observations. Secondary data were collected from the companies' financial statements, inflation data from BPS, and stock price data from the IDX. The analysis technique used panel data regression with a random effect model after conducting the Chow, Hausman, and Lagrange Multiplier tests. The results of the study show that simultaneously ROA, DER, and inflation have a significant effect on stock prices, with an F-value of 3.366161 and a p-value of 0.000. Partially, ROA has a positive and significant effect on stock price, with a coefficient of 122.0658 and a p-value of 0.000. Meanwhile, DER and inflation had no significant effect on stock price, with p-values of 0.2578 and 0.5027, respectively. The determination coefficient of 14.2035% indicates that the independent variables explain 14.2035% of the variation in stock prices, while the remaining 85.7965% is influenced by other factors outside the research model.

Plastic materials have played a very important role in the development of our modern civilization. It can be said that plastic materials are a tribute to man’s creativity and inventiveness. Plastic materials are the key to the innovation of many products and technologies in various sectors such as electrical and electronic equipment, packaging, civil engineering, agriculture, power generation, medical and pharmaceutical industry, aerospace and not last automotive industry. An increasingly important aspect in the automotive industry, in terms of fuel consumption is the lower weight of the components, while maintaining a very high stiffness. The difficulties of the design process of thermoformed parts is mainly due to the lack of knowledge by designers about the constraints and forces that are required during the process of thermoforming.

New road transportation systems solutions create significant changes in existing automotive manufacturing industry products and technologies, from design to use. The conveniences within the framework of new approaches brought by autonomous vehicle technologies primarily make individuals transition from driver duty to passenger and high-comfort alternative travel technologies. Therefore, the research: defining the path followed by the autonomous vehicle technologies, which lead to the development of the said new life model and automotive products within the future fiction, in the stages of designing new concept vehicles in practice or measuring the effect on the processes constitute important values for the future prediction of this sector. In addition, the research has focused on the effects of interdisciplinary studies at the automotive concept design stages, which are at the beginning of today’s lean and new product development process, where innovation goals or technologies emerge with more concrete needs. New autonomous vehicle technologies and the main purpose of revealing the interdisciplinary studies created by new disciplines in the current automotive concept design stages make significant contributions to the optimization of the lean product development process and value creation. For this reason, the automotive manufacturing industry, which is on the eve of a major transformation with the said new autonomous vehicle technologies; determining the needs or sustainable position in the flow of digital perception and orientation systems; determining value creation criteria related to the functioning of automotive concept design processes or new acceptance criteria through one-on-one interviews in the field; constitutes the focus of the research. The research has examined the new interdisciplinary studies and effects of new autonomous vehicle technologies in the automotive concept design phase, which is the first step of lean product development, with local and global automotive industry company comparisons in operation. Therefore, the differences and similarities between the concept design stages of global automotive companies that are both co-developers of new autonomous vehicle technologies and manufacturing automotive products and local automotive manufacturing companies that only assemble them determine the future competitive structuring of the industry.

This research investigates the adaptation of conventional injection-molding techniques for producing thick-walled polycarbonate optical components, specifically targeting their application in automotive light guides. With the automotive industry's growing demand for reliable yet cost-efficient optical products, the study examines how traditional injection-molding processes can be refined to enhance dimensional accuracy and reduce defects. Simulations and experimental trials were conducted to evaluate the impact of critical process parameters, such as melt temperature, mold temperature, injection pressure, and gate design, on the overall quality of the final components. The results show that by carefully optimizing these parameters, it is possible to significantly reduce common defects like warpage, surface imperfections, and dimensional instability. This research highlights the potential of existing molding techniques to meet high industry standards while maintaining cost-effectiveness, offering valuable guidance for manufacturers aiming to produce high-quality optical components for demanding applications like automotive lighting.