Browse Topic: Quality control
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
Fused Deposition Modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability, making it well-suited for use in industries such as footwear, automotive, and consumer goods. Hoses, gaskets, seals, external trim, and interior components are just a few of the many uses for thermoplastic polyurethanes (TPU) in the automobile industry. The objective of this study is to enhance the performance of Fused Deposition Modeling (FDM) by optimizing the parameters specifically for Thermoplastic Polyurethane (TPU) material. This will be achieved by employing a Taguchi-based Grey Relational Analysis (GRA) method. The researchers conducted experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses
The objective of this research is to develop an optimization strategy for the Electrochemical Drilling process on Nimonic alloy material, taking into account various performance factors. The optimization strategy relies on the integration of the Taguchi method with Grey Relational Analysis (GRA). Nimonic is extensively utilized in aerospace, nuclear, and marine industries, specifically in situations that are prone to corrosion. The experimental trials are structured based on Taguchi's principle and encompass three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This inquiry examines performance indicators like the rate of material removal, surface roughness, as well as geometric parameters such as overcut, shape, and orientation tolerance. Based on the investigation, it is determined that the feed rate is the primary factor that directly affects the intended performance criteria. In order to enhance the accuracy of predictions, multiple regression
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
Wire Electrical Discharge Machining (WEDM) is an important method engaged to make intricate shapes in conductive materials like Cupronickel, which is well-known for its ability to resist corrosion and conduct heat. The intention of this exploration is to enhance the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for Cupronickel material by utilizing a Taguchi-based Grey Relational Analysis (GRA). The study examines the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on key machining outcomes such as surface roughness (Ra), material removal rate (MRR). A comprehensive dataset is generated for analysis through a systematic series of experiments designed using the Taguchi method. Grey relational grades are assessed to measure the connections between the input parameters and machining responses, making it easier to determine the best parameter settings. The Taguchi-based GRA approach provides a systematic approach for
Spot welds are integral to automotive body construction, influencing vehicle performance and durability. Spot welding ensures structural integrity by creating strong bonds between metal sheets, crucial for maintaining vehicle safety and performance. It is highly compatible with automation, allowing for streamlined production processes and increased efficiency in automotive assembly lines. The number and distribution of spot welds directly impact the vehicle's ability to withstand various loads and stresses, including impacts, vibrations, and torsion. Manufacturers adhere to strict quality control standards to ensure the integrity of spot welds in automotive production. Monitoring spot weld count and weld quality during manufacturing processes through advanced inspection techniques such as Image processing by YOLOv8 helps identify the number of spots and quality that could compromise safety. Automating quality control processes is paramount, and machine vision offers a promising
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a highly promising method for creating intricate shapes using different materials. Polyethylene Terephthalate Glycol (PETG) is a highly utilized thermoplastic that is recognized for its exceptional strength, resistance to chemicals, and effortless processing. This study aims to optimize the process parameters of the FDM technique for PETG material using Taguchi Grey Relational Analysis (GRA). An empirical study was carried out to examine the impact of various FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on important outcome variables like dimensional accuracy, surface quality, and mechanical properties. The Taguchi method was used to systematically design a series of experiments, while GRA was used to optimize the process parameters and performance characteristics. The results unveiled the most effective parameter combinations for attaining
In recent years, battery electric vehicles (BEVs) have experienced significant sales growth, marked by advancements in features and market delivery. This evolution intersects with innovative software-defined vehicles, which have transformed automotive supply chains, introducing new BEV brands from both emerging and mature markets. The critical role of software in software-defined battery electric vehicles (SD-BEVs) is pivotal for enhancing user experience and ensuring adherence to rigorous safety, performance, and quality standards. Effective governance and management are crucial, as failures can mar corporate reputations and jeopardize safety-critical systems like advanced driver assistance systems. Product Governance and Management for Software-defined Battery Electric Vehicles addresses the complexities of SD-BEV product governance and management to facilitate safer vehicle deployments. By exploring these challenges, it aims to enhance internal processes and foster cross
An industry-first 3D laser-based, computer-vision system can monitor and control the application of adhesive beads as tiny in width as two human hairs. This unique inspection system for electronic assemblies operates at speeds of 400 to 1,000 times per second, considerably quicker and more effective than conventional 2D systems. “Difficulty in precisely dispensing adhesives or sealants, especially in extremely small or complex electronic assemblies, can lead to over-application, under-application, bubbles, or incorrect location of the adhesive bead,” Juergen Dennig, president of Ann Arbor, Michigan-headquartered Coherix, told SAE Media. Improper application of joining material on electronic control units (ECUs) and power control units (PCUs) can result in poor adhesion, material voids and short circuits.
Vehicle HVAC noise performance is an important vehicle design validation criterion since it significantly links the brand image of a vehicle. It affects the customer’s buying decision and the business of selling vehicles because it directly affects driving comfort. Customers expect continuous improvement in HVAC noise without compromising cooling performance. The process of cascading vehicle-level acoustic performance to subsystem and component levels becomes an important factor in the vehicle NVH development process. It was found that the component-level [HVAC unit without duct] performance of an HVAC system measured in an anechoic chamber was at par when compared to targets, whereas the subsystem-level performance [HVAC unit with duct and dashboard] was on the higher side of the targets. Advanced NVH tools were used to identify the source of noise at the subsystem level. It helped to locate the source and its transfer path. A design modification done at the transfer path location
To provide a method by which to assess the cleanliness of new hydraulic fluids. The method is applicable to new mineral and synthetic hydraulic fluids - regardless of packaging. This SAE Standard is not intended as a procedure for operating equipment.
This SAE Aerospace Recommended Practice (ARP) covers the requirements for the types of glass to be utilized in the fabrication of cover glasses and lighting wedges used in aerospace instruments. It defines the maximum extent of physical defects and recommends standard methods of inspection and evaluation. Definitions of terminology used in this document are covered in 2.2.
A company says that its digital twin alignment system, incorporating a sophisticated AI algorithm and an off-the-shelf camera, has the potential to revolutionize the auto industry, potentially saving it up to a staggering $20 billion in the effort to detect defects on the manufacturing line. Generally, such inspections of spot welds, bolt holes and the like are handled one of three ways: Slow manual inspections that can have high error rates. Even slower inspection with coordinate-measuring machines (CMMs) that can take hours to inspect 150 spot welds. Tremendously expensive technology, such as lasers, that still aren't perfect.
Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras. A time resolution of less than 1ms eventually allows for the true localization of the initial and subsequent sound events as well as a clear separation of direct from
A sensing technology that can assess the quality of components in fields such as aerospace could transform UK industry. University of Bristol, Bristol, UK In a study, published in the Journal Waves in Random and Complex Media, researchers from the University of Bristol have derived a formula that can inform the design boundaries for a given component's geometry and material microstructure. A commercially viable sensing technology and associated imaging algorithm to assess the quality of such components currently does not exist. If the additive manufacturing (3D printing) of metallic components could satisfy the safety and quality standards in industries there could be significant commercial advantages in the manufacturing sector.
In a study, published in the Journal Waves in Random and Complex Media, researchers from the University of Bristol have derived a formula that can inform the design boundaries for a given component’s geometry and material microstructure.
The aviation, space, and defense industries rely on the development and manufacture of complex products comprised of multiple systems, subsystems, and components each designed by individual designers (design activities) at various levels within the supply chain. Each design or manufacturing activity controls various aspects of the configuration and specifications related to the product. When a change to design or process is requested or required, the change is typically required to be evaluated against the impacts to the entire system. Proposed changes to design data/information that the design activity identifies to be minor and have no effect on the product requirements or specifications, have the potential to be implemented and approved, where authorized to do so, but requires notification. Changes that affect customer mandated requirements or specifications shall be approved prior to implementation. In many cases, the design activity is not conducted by the DAH or design authority
In this paper, in order to obtain the specified communication range, this work provides a novel optimization approach for antenna placement inside a design space of a vehicle is proposed. The design community can undertake antenna design iterations and quickly investigate antenna placement areas with the help of the optimal placement of antenna utilizing computational electromagnetic (CEM) based optimization approach. The design of experiments (DOE) for various antenna positions and orientations was produced using the Taguchi method [5]. To examine the impact of near field E throughout communication range, Quasi-Newtonian gradient technique and high frequency simulation software (HFSS) are employed. Response curves were created from the received near field E in order to examine the mean, large mean, and cost-wise approaches in order to find the antenna's ideal position and orientation for a strong signal across the communication range.
The Auto industry has relied upon traditional testing methodologies for product development and Quality testing since its inception. As technology changed, it brought a shift in customer demand for better vehicles with the highest quality standards. With the advent of EVs, OEMs are looking to reduce the going-to-market time for their products to win the EV race. Traditional testing methodologies have relied upon data received from various stakeholders and based on the same tests are planned. The data used is highly subjective and lacks variety. OEMs across the world are betting big on telematics solutions by pushing more and more vehicles with telematics devices as standard fitment. The data from such vehicles which gets generated in high levels of volume, variety and velocity can aid in the new age of vehicle testing. This live data cannot be simply simulated in test environments. The device generates hundreds of signals, frequently in a fraction of seconds. Multiple such signals can
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