Automotive Manufacturing Research Articles

Reducing Energy Consumption and Greenhouse Gas Emissions of Industrial Drying Processes in Lithium-Ion Battery Cell Production: A Qualitative Technology Benchmark

As the world’s automotive battery cell production capacity expands, so too does the demand for sustainable production. Much of the industry’s efforts are aimed at reducing the high energy consumption in battery cell production. A key driver is electrode drying, which is currently performed in long ovens using large volumes of hot air. Several drying technologies from other industries could reduce energy consumption and greenhouse gas emissions if successfully applied to battery cell production. High process and quality requirements must be met when adapting these technologies for battery cell production. Evaluating the technologies against these requirements is difficult due to the technological novelty of this industry and the associated lack of data. Furthermore, the significant differences in drying technologies render a comparison even more challenging. One objective of this study was to evaluate drying technologies and identify those that could be best adapted to lithium-ion battery cell production. Near-infrared and laser drying were found to be the best in terms of energy efficiency, cost savings and other parameters. Another aim was to analyse, in more detail, the technological challenges and the advantages and disadvantages of the top-ranked drying technologies. Finally, the saving potential for greenhouse gas emissions of near-infrared and laser drying was calculated for a global production scenario of LIB cells in 2030. The saving potential in this scenario would amount to 2.63 million metric tonnes (Mt) CO2eq per year if near-infrared drying was applied in all global LIB cell production facilities within the mentioned scenario and 1.47 million Mt CO2eq per year for laser drying.

Surface Modification of Montmorillonite Clay Towards the Fabrication of Nanocomposites for Engineering Applications

Montmorillonite (MMT) is a layered clay mineral composed of silica and alumina sheets with a layer thickness of about 1 nm qualifies it as a two-dimensional nanomaterial. MMT is found in natural clay ores in the Murunkan, Kirinda, and Okanda areas in Sri Lanka. Nanocomposites with enhanced physical and mechanical properties can be prepared by dispersing MMT in a polymer matrix. However, the hydrophilic nature of MMT hinders the incorporation of MMT into hydrophobic polymer matrices. Hence, a suitable surface modification is required to make MMT compatible with polymers. The present study was based on the preparation and analysis of organically modified calcium Montmorillonite (Ca-MMT)-polypropylene nanocomposites as a green technology initiative. Homo-ionic Ca-MMT was prepared by treating commercial MMT-K10 with a CaCl2 solution. The predominant exchangeable divalent cations present in MMT-K10 and Ca-MMT were determined using AAS. The cation exchange capacity of Ca-MMT was about two times greater than that of MMT-K10. Furthermore, XRD analysis showed a slight increase in d- spacing of Ca-MMT compared to MMT-K10. A novel ligand, triethylene glycol dodecyl methyl ether was synthesized by treating triethylene glycol monomethyl ether (TGME) with 50% w/w aqueous solution of sodium hydroxide followed by 1-bromododecane with the molar ratio of TGME:NaOH:1-bromododecane in 1:2:2. The crude product was purified by column chromatography and characterized by FTIR and GCMS analysis. Hydrophilic Ca-MMT was surface modified with the organic ligand presumably via the coordination of triethylene glycol moiety with Ca2+ cations at the outer edge of the MMT particles. The long aliphatic chains in the periphery of the modified MMT particle make it more organophilic. Modified Ca-MMT was characterized by XRD, TGA, and FTIR analysis. The XRD revealed a slight increase in the d-spacing related to the 001 plane of modified Ca-MMT compared to unmodified clay. The effect of modified Ca-MMT loadings (0.5%, 1%, and 1.5%) on the mechanical properties of the nanocomposite was studied. The results implied an enhancement of the mechanical properties in terms of impact resistance, hardness, and flexural properties in the nanocomposite compared to the virgin polymer. The fabricated nanocomposite is a promising engineering material that may have applications in automotive, construction, and packaging products.
 
 Keywords: Montmorillonite, Polypropylene, Nanocomposite, TGME, Compatibility

Influence of Electrolyte Choice on Zinc Electrodeposition.

Zinc electrodeposition serves as a crucial electrochemical process widely employed in various industries, particularly in automotive manufacturing, owing to its cost effectiveness compared to traditional methods. However, traditional zinc electrodeposition using aqueous solutions faces challenges related to toxicity and hydrogen gas generation. Non-aqueous electrolytes such as ionic liquids (ILs) and deep eutectic solvents (DESs) have gained attention, with choline-chloride-based DESs showing promise despite raising environmental concerns. In this study, zinc electrodeposition on mild steel was investigated using three distinct electrolytes: (i) halide-free aqueous solutions, (ii) chloride-based DES, and (iii) halide-free acetate-based organic solutions. The study examined the influence of deposition time on the growth of Zn on mild steel substrates from these electrolytes using physical characterization techniques, including scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results indicate that glycol + acetate-based non-aqueous organic solutions provide an eco-friendly alternative, exhibiting comparable efficiency, enhanced crystalline growth, and promising corrosion resistance. This research contributes valuable insights into the impact of electrolyte choice on zinc electrodeposition, offering a pathway towards more sustainable and efficient processes. Through a comprehensive comparison and analysis of these methods, it advances our understanding of the practical applications of zinc electrodeposition technology.

Quality costs and Industry 4.0: inspection strategy modelling and reviewing

AbstractInspection strategy (IS) is a key component impacting quality costs. Although often considered an inflexible output of initial quality plans, it may require revisions given the dynamic quality situation of the manufacturing system. It is from this background that the present study aims to model and compare different IS based on the cost of quality (CoQ) approach for a case study in the automotive manufacturing industry. While many computational inspection strategy models (ISMs) are available in the literature, most of them face application challenges and struggle to incorporate real-world data. The present study addresses this gap by developing a model that not only represents a real testing station in a manufacturing line but also uses historical production data. Additionally, in relation to model inputs, this study explores the challenges and opportunities of acquiring reliable quality cost estimates in the Industry 4.0 context. Among the main contributions of this work, the developed CoQ-based ISM can be used as a decision-making aiding tool for inspection revision and improvement, while conclusions about quality cost data collection in the industrial digitalization context can help advance the CoQ approach in practice.

Analysis of Production and Failure Data in Automotive: From Raw Data to Predictive Modeling and Spare Parts

The present analysis examines extensive and consistent data from automotive production and service to assess reliability and predict failures in the case of an engine control device. It is based on statistical evaluation of production and lead times to determine vehicle sales. Mileages are integrated to establish the age of the vehicle fleet over time and to predict the censored data. Failure and censored times are merged in a multiple censored data and combined by the Kaplan-Meier estimator for survivals. The Weibull distribution is used as parametric reliability model and its parameters identified to assure precision in predictions (>95%). An average time to failure >80 years and a slightly increasing failure rate ensure a low risk. The study is based on real-world data from various sources, acknowledging that the data are not homogeneous, and it offers a comprehensive roadmap for processing this diverse raw data and evolving it into sophisticated predictive models. Furthermore, it provides insights from various perspectives, including those of the Original Equipment Manufacturer, Car Manufacturer, and Users.

Simulation of screw size effect on performance of electro-mechanical braking system

Electro-mechanical brakes (EMB) are promising alternatives to conventional braking systems, as they are eco-friendly and have lower maintenance requirements. Currently, researchers and automotive manufacturers are interested in developing the performance of EMB systems for implementation in commercial vehicles. Current studies are either conducted experimentally and/ or modeled using mathematical based simulation models using different vehicles kinematic and dynamic models. These models are time-consuming, and expensive in case of experimental implementation. This paper presents a simple and easy-to-construct simulation model using MATLAB Simulink Graphical user interface (GUI) without using conventional mathematical modelling techniques. The presented simulation model studies the effect of the lead screw size on performance of EMB in terms of the generated clamping force and system response time. The accuracy and reliability of the proposed model were statistically verified using ANOVA. In addition, its performance was verified by comparing its results with the same input data used in previously validated experimental and mathematical-based simulation models from the literature review. The proposed model can be used to improve the performance of EMB systems and facilitate comparisons among different EMB designs.

Circularity of new composites from recycled high density polyethylene and leather waste for automotive bumpers. Testing performance and environmental impact

New composite materials (suitable for automotive bumpers), composed of recycled high-density polyethylene (rHDPE) and leather buffing dust waste (BF) ranging from 20 to 50 wt%, were produced and investigated for mechanical properties. Optimal mechanical performance was achieved with composites containing 30 % wt BF. The environmental performance of automotive bumper production from both virgin and recycled HDPE reinforced with 30 % wt BF (HDPE-BF, rHDPE-BF) composites was compared to that of conventional polypropylene (PP) by performing a cradle to gate life cycle assessment.A component-based approach, instead of a comprehensive LCA assessment for the entire car was adopted using various functional units (FU) such as mass (FU1), volume (FU2), and volume of raw material fulfilling a specific impact strength requirement (FU3), thus enriching the paper with methodological discussions.The rHDPE-BF system provided better environmental performance compared to the virgin PP system, when considering both mass and volume-related functional units, mainly due to the avoidance of virgin polymer production. Even with the inclusion of the use phase in FU2 and a slightly higher density (+1.7 %) of composites than PP-based bumpers, the rHDPE system still provides better environmental performance (10 % less impact). The sensitivity analysis highlighted the significance of car type and final density of the bumper on the impact results. Finally, when using FU3, due to its higher impact strength, HDPE-BF system is clearly the best environmental alternative (50 % less impact) followed by rHDPE-BF system. In all cases, rising the content of recycled materials in the bumpers increases its circularity.The paper illustrates the importance of selecting a suitable functional unit, based on a specific application (i.e., automotive bumpers), to evaluate the environmental impact of new composite materials in comparison to traditional options. Expanding the assessment to encompass multiple functions provides a more accurate portrayal of reality but also introduces greater result uncertainty.

ANALISA PERBANDINGAN KINERJA KEUANGAN PERUSAHAAN DENGAN METODE DU PONT

This research was conducted at automotive and component manufacturing companies listed on the Indonesia Stock Exchange at PT Gajah Tunggal Tbk and PT Multistrada Arah Sarana Tbk for the period 2015 - 2019. The purpose of this study was to determine the company's financial performance at PT Gajah Tunggal Tbk and PT Multistrada Arah. Sarana Tbk in 2015-2019 using the Du Pont System method and what factors affect differences in financial performance. This research uses descriptive quantitative research methods and data analysis techniques using the du pont method. The data used in this research is the financial statement data of the companies PT Gajah Tunggal Tbk and PT Multistrada Arah Sarana Tbk for the last five years in 2015-2019. The results of the research that has been done show that the financial statements of PT Gajah Tunggal Tbk and PT Multistrada Arah Sarana Tbk for five years, the authors can conclude that based on the average Return On Assets (ROA), the average Net Profit Margin (NPM), and the average Total Assets Turn Over (TATO), during 2015-2019 shows that PT Gajah Tunggal Tbk, has a better financial performance than PT Multistrada Arah Sarana Tbk. As for the factors causing the decline in the value of TATO, NPM and ROA, namely the decrease in net income due to the influence of total costs and the company's ineffectiveness in managing existing assets, so that the profit resulting from the comparison of the value of net income to total assets is very small.

Review of AI Maturity Models in Automotive SME Manufacturing

This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs employ a large segment of the USA's workforce. The benefits of operational efficiency, quality improvement, cost reduction, and innovative culture have made SMEs more aggressive about digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI applications in SMEs are examined through the lens of an AI maturity model.

Improving efficiency of DNN-based relocalization module for autonomous driving with server-side computing

The substantial computational demands associated with Deep Neural Network (DNN)-based camera relocalization during the reasoning process impede their integration into autonomous vehicles. Cost and energy efficiency considerations may dissuade automotive manufacturers from employing high-computing equipment, limiting the adoption of advanced models. In response to this challenge, we present an innovative edge cloud collaborative framework designed for camera relocalization in autonomous vehicles. Specifically, we strategically offload certain modules of the neural network to the server and evaluate the inference time of data frames under different network segmentation schemes to guide our offloading decisions. Our findings highlight the vital role of server-side offloading in DNN-based camera relocation for autonomous vehicles, and we also discuss the results of data fusion. Finally, we validate the effectiveness of our proposed framework through experimental evaluation.

Pengaruh Harga dan Kualitas Produk terhadap Keputusan Pembelian Sepeda Motor Honda Matic di PT Astra Honda Motor Cabang Negara Kabupaten Jembrana

In line with the public's need and demand for adequate transportation facilities, city streets are increasingly crowded and traffic is increasingly congested by motorized vehicles. Encouraging the development of the automotive industry in Indonesia more quickly and tends to increase every year. The market demand for motorized vehicles is faced by automotive motorbike manufacturers, one of which is PT Astra Honda Motor, Jembrana Regency State Branch. This research uses a quantitative approach with the results of the research showing that variables X1 (Price) and ≤ 5% (0.05).

A new lean tool to enhance internal logistics in Engineer and Design-to-Order industrial environment: plan for every order

ABSTRACT The paper presents a new Lean Logistics tool specifically designed for Engineer and Design-To-Order (ETO and DTO) industrial environments. The new tool, namely Plan For Every Order (PFEO), conceptually derives from the well-known Plan For Every Part used in the automotive serial production context. PFEO is proposed as a tool for industrialization, focusing on the design and logistics optimization of internal material flows at ETO and DTO plants. In the paper, the principles, and concepts behind PFEO are presented and developed. The tool collects information on both suppliers and materials, improving both the management of materials and spaces and communication between the company and suppliers. The operability of the tool is proven in a real business case involving an shipyard. The PFEO has provided a solid database for the development of auxiliary monitoring and visualization tools, the application of which increases order in the warehouse and assembly areas and reduces costs and errors.

PENINGKATAN PELAYANAN BISNIS PADA IKM KOMPONEN OTOMOTIF DI CIKARANG KABUPATEN BEKASI

PT Nikko Teknik Indonesia is a tier-3 Small and Medium Industry company which operates in the field of manufacturing automotive components in the city of Bekasi. Tier-3 companies are automotive component manufacturing companies at the lowest level, such as PT Nikko Teknik Indonesia which focuses on making jigs, dies and welding from automotive components. The urgency of this research, there are many obstacles faced by tier-3 companies such as lack of use of technology information to support business, marketing aspects. It often managed conventionally (so it difficult to get new buyers/vendors) and also problems with automation of tracking components which deliver to customer. So, implementing comprehensive supply chain management e-dashboard is very helpful for tier-3 automotive component SME’s businesses. The research methods used are Data Envelopment Analysis (DEA) and Rapid Development Application (RAD) methods in automotive component production companies. The results of this research are that it has succeeded in building an e-Dashboard system for Supply Chain Management recording information, which provides facilities: statistics and indicators starting from raw materials, work-in-process and finished goods that are ready to be sent to buyers. .
 
 Keywords: Automotive Components SMEs, Business Services, E-Supply Chain Management

Powering Ahead: Current Landscape of Electric Vehicles (EV) in India

Electric vehicles have gained widespread acceptance globally, driven by increasing awareness of environmental issues and government initiatives promoting sustainable transportation. The market for electric vehicles has witnessed exponential growth, with major automotive manufacturers investing heavily in research and development to bring innovative and competitive electric models to the market. The core technology of electric vehicles revolves around electric powertrains, which replace traditional internal combustion engines with electric motors powered by rechargeable batteries. This shift not only reduces reliance on fossil fuels but also significantly decreases greenhouse gas emissions, contributing to the mitigation of climate change. Recent advances in battery technology, particularly in energy density and cost reduction, play a pivotal role in enhancing the range and affordability of electric vehicles. The transition towards electric vehicles (EVs) has gained significant momentum in India, in recent years. This paper examines the current status of electric vehicles in the Indian market, including government initiatives, infrastructure development, challenges, and future prospects. The analysis highlights the key factors driving the adoption of EVs, the state of charging infrastructure, policy framework, and the impact of electric mobility on the environment and the economy.

A Deep Learning-Based CAE Approach for Simulating 3D Vehicle Wheels Under Real-World Conditions

The implementation of deep learning (DL) in computer-aided engineering (CAE) can significantly improve the accuracy and efficiency of simulating 3D vehicle wheels under real-world conditions. While traditional CAE methods can be time-consuming and computationally expensive, DL can reduce simulation time and development cycles across all industries. This work explores the role of DL and AI in virtual manufacturing and CAE and investigates how they can be used to improve the accuracy and efficiency of simulations for 3D vehicle wheels. Deep learning models can learn the complex relationships between different wheel design parameters, such as tire load distribution, stress distribution, and fatigue life. Once trained, these models can be embedded into CAE software, allowing for faster and more accurate simulations of wheel performance. This interdisciplinary study uses various deep learning techniques, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and recurrent neural networks (RNNs), to create a more efficient and accurate relationship between CAD modeling and CAE simulation. The research aims to leverage the potential of deep learning models to automate 3D CAD design, accurately predict CAE results, and provide in-depth explanations and verifications. The benefits of this research are expected to extend to the automotive industry's pursuit of more robust and resilient wheel designs. By streamlining the product development process from conceptual design to engineering performance evaluation, this study has the potential to revolutionize the automotive industry's product development cycle.

Functionalized palm biomass-derived activated carbon for the removal of Pt(IV) from a simulated leachate

Over the years, demand for Platinum Group Metals (PGMs) has grown steadily due to increased production of various advanced technologies, such as automotive and electronic products. PGMs are predominantly used in automotive catalysts in the automotive industry. Along with the increase in automotive production, deactivated automotive catalysts pose environmental and health hazards. These wastes are excellent alternative sources of PGMs, which can be exploited to bridge the gap between the demand and supply of PGMs. Adsorption is one of the most popular metal removal/recovery methods due to its various advantages, such as ease of use and cost-effectiveness. In consideration of this method, developing an inexpensive and efficient adsorbent is a crucial point. Thus, activated carbon (AC), derived from a palm kernel shell that is abundantly available in Indonesia, was functionalized using ionic liquid (ACIL) and used for Pt(IV) removal from a simulated automotive catalyst waste leachate. The functionalized AC showed a high adsorption capacity (178.6 mg g−1), in which the adsorption of Pt(IV) followed a chemisorption route, fitting with the monolayer model. The functionalized adsorbent also showed excellent performance during continuous Pt(IV) adsorption from simulated leachate. Recovery of precious metals, such as Pt(IV) and Pd(II), from the simulated leachate containing other metals was possible by maintaining the high hydrochloric acid concentration. Furthermore, targeted separation of Pt(IV) was achieved through sequential desorption using NaClO4. In addition, ACIL showed remarkable reusability after being used for three cycles without showing a noticeable decrease in performance. Thus, this study highlights the capability of a functionalized adsorbent from palm oil industry biomass to recover precious metals from simulated leachate of automotive waste.

Amazon Natural Fibers for Application in Engineering Composites and Sustainable Actions: A Review

The Amazon rainforest, spanning multiple countries in South America, is the world’s largest equatorial expanse, housing a vast array of relatively unknown plant and animal species. Encompassing the planet’s greatest flora, the Amazon offers a tremendous variety of plants from which natural lignocellulosic fibers (NLFs) can be extracted. In this century, NLFs, which have long been utilized by indigenous populations of the Amazon, have garnered interest as potential reinforcements for composites, whether polymer- or cement-based, in various technical applications such as packaging, construction, automotive products, and ballistic armor. A comparison with synthetic materials like glass, carbon, and aramid fibers, as well as other established NLFs, highlights the cost and specific property advantages of Amazon natural fibers (ANFs). Notably, the sustainable cultivation and extraction of ANFs, as alternatives to deforestation and livestock pasture, contribute to the preservation of the Amazon rainforest. This review article provides a comprehensive examination of recent studies directly related to ANF-reinforced polymer matrix composites. The specific advantages, proposed applications, and reported challenges are highlighted, shedding light on the potential of these unique natural fibers.

What are the root causes of material delivery schedule inaccuracy in supply chains?

PurposeThis study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?Design/methodology/approachA mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.FindingsThe effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.Practical implicationsThe findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.Originality/valueThe findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Research on joint strength and energy absorption of CFRP and Al sheets bonded-FDS hybrid joining

The application of a composite-metal alloy lightweight body structure is one of the ways to achieve lightweight in an automobile. Materials such as carbon fiber reinforced polymer (CFRP) and aluminum alloys are widely used in the automotive industry due to their excellent performance. However, the significant differences in performance parameters between CFRP and aluminum alloy materials make joining heterogeneous materials challenging. Therefore, a rational joining process is crucial for the development of CFRP–aluminum alloy structures. This study proposes a hybrid joining method that combines flow drill screw (FDS) and adhesive bonding to join commonly used aluminum alloy sheets with CFRP sheets in automotive body manufacturing. Finite element analysis simulations were used to compare and characterize the failure modes of the bonded-FDS hybrid joints in tensile tests. The results were verified for accuracy. Tensile tests were conducted to compare the mechanical performance and failure modes of bonded joints, FDS joints, and hybrid joints. By analyzing the load–displacement curve, it was found that the FDS process in the hybrid joining damages the internal structure of the adhesive layer. However, the joint strength is still higher than that of the single joint strength, and the energy absorption value is about 4.7 times that of the adhesive and 1.6 times that of the FDS. This study provides a new industrial solution and serves as a reference for failure performance in composite material structures in the automotive industry, especially in cases involving one-sided access joining points.

Scalable Generation of Hybrid Graphene Nanoscrolls for High-Performance Solid Lubricants

Graphene and other 2D materials have been extensively studied as solid lubricants in recent years. Low friction can sometimes be observed in those 2D lubricants, and one possible mechanism is that scroll-shaped nanostructures are formed during friction, which decreases the contact area and energy barrier, thus substantially reducing friction. The integration of graphene with metal or metal oxide nanostructures can further enhance its lubrication properties by increasing film formation ability and easy shearing of the nanosheets. However, it is not possible to reliably promote the formation of such nanoscroll-shaped low friction wear products, which limits the reproducibility and application of such nanostructures as solid lubricants. In this study, we address this issue by creating a scalable method for the synthesis of hybrid graphene-titanium oxide (G–TiO2) nanoscrolls and demonstrating their potential as solid lubricants with macroscopic coefficient of friction as low as 0.02 in ambient conditions. Our approach to generate the nanoscrolls is based on the in situ sol–gel synthesis of TiO2 on graphene followed by spray-freeze-drying–induced shape transformation. The solid lubrication performance of such G–TiO2 nanoscrolls can be further enhanced by applying a thin graphene oxide primer layer, which provides high affinity to both the substrate and the active materials. These hybrid nanoscrolls hold promising potential for applications in aerospace, automotive, and precision manufacturing fields as effective solid lubricants.Graphical