Flexible scheduling of diagnostic tests in automotive manufacturing

The car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

Reduce Carbon Emissions of Logistic Transportation Using Eight Steps Approach in Indonesian Automotive Industry

The global competition encourages Indonesia to advance the economy, especially in manufacturing by implementing sustainable manufacturing. Companies must consider transportation costs and concern for the environment due to the large increase in greenhouse gas emissions and the increase in NOx, Particulate, and various other harmful pollutants. Emissions from transportation activities cause global climate change and damage air quality and human health in regional and urban areas. At the same time, the movement of empty containers can result in air pollution due to CO2 emissions which have a negative impact on sustainable development. This study aims to reduce carbon emissions in the logistics transportation chain in the Automotive Manufacturing Industry. The method used is the Eight Step Approach. The method used is systematic and structured from defining the problem to standardizing improvements. Analysis of the causes of the problem and proposed improvements are determined by Focus Group Discussion (FGD) with expert judgment. The source of the data obtained comes from field observations, FGD, company reports from 2019 to 2021. This research has proven that reducing carbon emissions has an impact on company profits. The largest decrease was contributed by improvements in transportation routes. The ratio of reducing carbon emissions by 2020 is 2.6% or an increase in efficiency compared to the previous year.

Slippery Surface with Petal-like Structure for Protecting Al Alloy: Anti-corrosion, Anti-fouling and Anti-icing

The harsh working environment affects the performance and usage life of Al and its alloys, thus limiting their application. In recent years, Slippery Liquid-infused Porous Surface (SLIPS) has attracted much attention due to excellent anti-corrosion, anti-fouling and anti-icing properties. This may be an effective way to improve the properties of Al and its alloys. Here, the SLIPS with petal-like structure was constructed on the Al alloy via simple hydrothermal reaction, Stearic Acid (STA) modification and lubricant injection. A variety of droplets (including oil-in-water emulsions) can slide on the SLIPS at a low angle, even the Sliding Angle (SA) of the water droplet is only 3°. Furthermore, the SLIPS exhibits outstanding mechanical and chemical properties. It can maintain fine oil-locking ability under high shearing force and keep slippery stability after immersion in acid/alkaline solutions. In addition, the SLIPS possesses excellent anti-corrosion, anti-fouling and anti-icing properties, which provides a new way to promote the application of Al and its alloys. Therefore, the SLIPS is expected to be an effective way to improve the properties of Al and its alloys, as well as play a role in anti-fouling and self-cleaning in construction, shipbuilding and automotive manufacturing industries, thereby expanding the practical application of Al and its alloys.

A machine learning approach for predictive warehouse design

Warehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems.

An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistically significant for FP variation, while the BN model, that considered conditional rules to reflect the dynamic relationship between the failures, had a greater impact on predicting the FP. Additionally, the integrated FFTA–BN model was used in the optimization model to find the optimal maintenance intervals according to the estimated FP and total expected cost. As a case study, the proposed model was implemented in a fluid filling system in an automotive assembly line. The FPs of the entire system and its three critical subsystems, such as the filling headset, hydraulic–pneumatic circuit, and the electronic circuit, were estimated as 0.206, 0.057, 0.065, and 0.129, respectively. Moreover, the optimal maintenance interval for the whole filling system considering the total expected costs was determined as 7th with USD 3286 during 5000 h of the operation time.

Industry quantum computing applications

Quantum computing promises to overcome computational limitations with better and faster solutions for optimization, simulation, and machine learning problems. Europe and Germany are in the process of successfully establishing research and funding programs with the objective to advance the technology’s ecosystem and industrialization, thereby ensuring digital sovereignty, security, and competitiveness. Such an ecosystem comprises hardware/software solution providers, system integrators, and users from research institutions, start-ups, and industry. The vision of the Quantum Technology and Application Consortium (QUTAC) is to establish and advance the quantum computing ecosystem, supporting the ambitious goals of the German government and various research programs. QUTAC is comprised of ten members representing different industries, in particular automotive manufacturing, chemical and pharmaceutical production, insurance, and technology. In this paper, we survey the current state of quantum computing in these sectors as well as the aerospace industry and identify the contributions of QUTAC to the ecosystem. We propose an application-centric approach for the industrialization of the technology based on proven business impact. This paper identifies 24 different use cases. By formalizing high-value use cases into well-described reference problems and benchmarks, we will guide technological progress and eventually commercialization. Our results will be beneficial to all ecosystem participants, including suppliers, system integrators, software developers, users, policymakers, funding program managers, and investors.

Hydrogen Absorption and Desorption Behavior on Aluminum-Coated Hot-Stamped Boron Steel during Hot Press Forming and Automotive Manufacturing Processes

Our study mainly focused on diffusible hydrogen in aluminum–silicon-coated hot-stamped boron steel during a hot press forming process and in pre-treatment sequential lines of the automotive manufacturing process using a thermal desorption spectroscopy (TDS) technique. First, in the hot stamping procedure, as the soaking time increased in the heating furnace at a specific dew point when austenitizing, a high concentration of diffusible hydrogen was absorbed into the hot-stamped boron steel. Based on the TDS analysis of hydrogen absorbed from hot stamping, the activation energy value of hydrogen trapping in 1.8 GPa grade steel is lower than that of 1.5 GPa grade steel. This means that diffusible hydrogen can be more easily diffused into defective sites of the microstructure at a higher level of the tensile strength grade. Second, in sequential pre-treatment lines of the automotive manufacturing process, additional hydrogen did not flow into the surface, and an electro-deposition process, including a baking procedure, was effective in removing diffusible hydrogen, which was similar to the residual hydrogen of the as-received state (i.e., initial cold rolled blank). Based on these results, the hydrogen absorption was facilitated during hot press forming, but the hydrogen was sequentially desorbed during automotive sequential lines on aluminum-coated hot-stamped steel parts.