Browse Topic: Production control
This SAE standard establishes the requirement for suppliers to plan a reliability program that satisfies the following three requirements: a The supplier shall ascertain customer requirements b The supplier shall meet customer requirements c The supplier shall assure that customer requirements have been met
Automating harvesters started out as a necessary solution to a severe labor shortage in 1990, Trebro Manufacturing states on its website. The Billings, Montana-based manufacturer has been producing turf harvesting machines since 1999, and its automated sod harvesters and entire harvesting process feature self-driving, automated-control functions. The company's tag line, “The Future of Turf Harvesting,” refers to its position of being the first in the industry to offer automated turf harvesting products. Trebro's AutoStack 3 harvester is an automated combine for turf that steers itself while an operator monitors and performs quality control actions when needed. The harvesting process combines several automated control processes.
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 single source of truth for all stakeholders. This tool enables auto selection of control charts based on sample size, frequency & type of parameters (unilateral, bilateral, GD&T) to accommodate variation in Quality standard of parts & manufacturing Process. Additionally Real-time adjustment of control limits based on time period selection make this tool accurate & reliable for monitoring Quality. Incorporating Industry 4.0 and smart manufacturing principles, this tool represents a significant advancement in Quality control. Integration with the Internet of Things (IoT) enables automatic data collection and monitoring, enhancing the efficiency and accuracy of the Quality control process with the SPC based Analytic model. This IoT integration also supports predictive maintenance of tooling and equipment and early detection of potential issues, reducing downtime and improving overall productivity. By minimizing the incidence of faulty parts on the shop floor, the tool significantly reduces rejections, contributing to more sustainable manufacturing practices. This proactive approach ensures high-Quality standards and aligns with smart manufacturing goals by optimizing resource use and enhancing operational efficiency.
Composite materials are created by combining two or more different materials, such as a filler or fibrous reinforcement dispersed in a polymer matrix. The primary goal of developing composites is to improve properties while reducing weight, making them ideal for the sustainable development of the automotive industry. Poly(lactic acid) (PLA) has emerged as a promising polymer matrix for composites due to its ecological and biodegradable nature, as well as its good mechanical properties (tensile strength and modulus of elasticity), though it remains limited when compared to engineering polymers such as acrylonitrile butadiene styrene (ABS) and acrylonitrile styrene acrylate (ASA). Cotton fibers have gained visibility in recent years as reinforcement in various matrices due to their low cost, renewable origin, and relative abundance. Incorporating cotton fibers into PLA can improve its mechanical properties, enhancing attributes such as tensile strength and stiffness, which makes the composite more suitable for applications requiring greater structural integrity. This article aims to study how to achieve a uniform distribution and strong chemical interaction between the PLA matrix and cotton fibers, that being crucial for the optimal composite performance required for the automotive industry. This study employed PRISMA protocol to conduct a systematic review of articles retrieved from four databases: ScienceDirect, Web of Science, Scopus and Engineering Village. The search query combined the following keywords: “PLA” AND “composite” AND “cotton”. The analysis of the research articles suggests that twin-screw extrusion is the preferred method for post-dispersion mixing due to its ability to control the process and optimization capabilities, resulting in homogeneous composite materials. Pre-dispersion techniques, including masterbatch, hydraulic presses or rheometers, produce concentrated mixtures of additive and polymer, effectively reducing fiber agglomeration during subsequent mixing. The use of solvent casting becomes an unfavorable option on an industrial scale due to the time required for production. However, it is still necessary for the validation and characterization of the material in laboratory scale, so that, in the future, an optimal formulation can be scaled up industrially with more robust machinery.
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly nature, affordability, and ease of processing. This study aims to optimize the parameters of Fused Deposition Modeling (FDM) for PLA material using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The researchers performed experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including dimensional accuracy, surface finish, and mechanical properties. The methodology of design of experiments (DOE) enabled a systematic exploration of parameters. The TOPSIS approach, a technique for making decisions based on multiple criteria, was used to analyze the experimental data and determine the best parameter settings. TOPSIS provides a comprehensive method for optimizing parameters in FDM by taking into account both the closeness to the ideal solution and the distance from the negative ideal solution. The results demonstrated the efficacy of the TOPSIS method in pinpointing the most advantageous parameter combinations for improving the printing quality and efficiency of PLA components. The optimization framework that has been developed offers valuable insights into the optimization and control of processes, thereby facilitating the wider implementation of FDM technology across different industries. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides useful techniques for optimizing FDM parameters. Manufacturers can improve printing productivity, quality, and sustainability by utilizing the TOPSIS approach. This, in turn, will help promote the wider use of AM technology in various applications.
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized the manufacturing sector by enabling the production of complex geometries using various materials. Polylactic Acid (PLA) is a biodegradable thermoplastic often used in additive manufacturing (AM) because to its eco-friendliness, cost-effectiveness, and processing simplicity. This research seeks to enhance the parameters of Fused Deposition Modeling (FDM) for PLA material with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The researchers conducted experimental trials to investigate the influence of key FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on essential outcomes such as dimensional accuracy, surface quality, and mechanical qualities. The design of experiments (DOE) technique facilitated a systematic investigation of parameters. The TOPSIS method, a decision-making tool based on several criteria, was used to assess the trial data and identify the optimal parameter values. TOPSIS offers a thorough approach for improving parameters in FDM by considering both proximity to the ideal solution and distance from the negative ideal solution. The findings revealed the effectiveness of the TOPSIS technique in identifying the optimal parameter combinations for enhancing the printing quality and efficiency of PLA components. The proposed optimization framework provides significant insights into the optimization and control of processes, hence promoting the broader use of FDM technology across many sectors. This work improves the understanding of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) and offers effective methods for improving FDM settings. Manufacturers may enhance printing productivity, quality, and sustainability via the use of the TOPSIS methodology. This will subsequently facilitate the broader use of additive manufacturing technologies across many applications.
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly nature, affordability, and ease of processing. This study aims to optimize the parameters of Fused Deposition Modeling (FDM) for PLA material using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The researchers performed experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including dimensional accuracy, surface finish, and mechanical properties. The methodology of design of experiments (DOE) enabled a systematic exploration of parameters. The TOPSIS approach, a technique for making decisions based on multiple criteria, was used to analyze the experimental data and determine the best parameter settings. TOPSIS provides a comprehensive method for optimizing parameters in FDM by taking into account both the closeness to the ideal solution and the distance from the negative ideal solution. The results demonstrated the efficacy of the TOPSIS method in pinpointing the most advantageous parameter combinations for improving the printing quality and efficiency of PLA components. The optimization framework that has been developed offers valuable insights into the optimization and control of processes, thereby facilitating the wider implementation of FDM technology across different industries. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides useful techniques for optimizing FDM parameters. Manufacturers can improve printing productivity, quality, and sustainability by utilizing the TOPSIS approach. This, in turn, will help promote the wider use of AM technology in various applications.
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 solution. Leveraging the YOLOv8 model, this research proposes an efficient technique for automatic detection and counting of spot welds on automotive sheets. Through analysis of a comprehensive dataset of annotated images, our approach demonstrates superior accuracy and efficiency in tracking and quantifying spot welds. Quantitative evaluation validates the effectiveness of this vision-based inspection method, highlighting its potential for enhancing car body welding quality control processes.
Helicopters in high-speed forward flight often generate High-Speed Impulse (HSI) noise, presenting a major challenge for noise control and narrowing the range of helicopter use. This paper proposes a novel method for active noise reduction by adjusting the rotor diameter length, effectively delaying HSI noise onset and reducing HSI noise impact. Utilizing the CLORNS solver and the Ffowcs Williams-Hawkings (FW-H) equation, this approach was tested on the AH-1G rotor through simulation analysis. The study simulated the rotor's dynamic diameter length changes, analyzing the effect of crucial parameters on the sound field. Results indicate that this method significantly controls the production of rotor high-speed pulse noise, achieving a noise reduction of up to 2dB at critical operational points. This research aids in formulating specific rotor noise control laws and expands the range of scenarios for helicopter usage.
Medical component manufacturing must meet stringent regulations for quality and product consistency, making process control a critical issue with materials, machining, assembly and packaging. This is vitally important with fluid dispensing applications used in the assembly of medical devices, point-of-care testing and near-patient testing products, medical wearables and other life sciences applications, which require accurate and consistent deposition of fluid amounts of UV-cure adhesives, silicones and other fluids in their manufacture.
Chemical Vapor Deposition (CVD) and Atomic Layer Deposition (ALD) processes deposit material on all surfaces in a process chamber. Over time, the thickness of these deposits increases to the point that material begins to delaminate, producing gas-phase particulates that negatively impact process yield. Remote and in situ chemical etching processes are used to periodically remove these deposits from chamber walls, maintaining chamber cleanliness.
A battery intelligence pioneer will work with a venerable semiconductor yield-improvement firm in a partnership that promises to drastically accelerate the production ramp for the many new EV battery factories on the horizon. Voltaiq, the battery-analysis experts, and PDF Solutions announced the partnership in late March. Tal Sholklapper, Voltaiq's CEO and cofounder, said the EV battery industry is in sore need of help in reducing the manufacturing development cycle, which can take anywhere from four to 10 years from shovels in the ground to output of a consistent, quality product. “The automotive battery industry is really behind.” he said in an interview with SAE Media. “There is a lot of manual analysis and semi-empirical learning going on,” and that slows the discovery of future problems. He said the partnership had the potential to cut battery factory development time in half.
Leveraging the increased use of Structural Adhesive in Automotive Body Structure Design has many proven benefits. It is a well-known method used to enable weight reduction in vehicle design and can also drive more efficient structural performance during dynamic safety events. This is increasingly important as vehicle safety standards increase, and as vehicle mass increases due to electrification. Often the benefits of adhesive use are not fully optimized due to unnecessary design redundancies or process driven redundancies. Design redundancy; using both welds and adhesive, is often included because government safety regulations require very robust validation of structures, and when combined with the use of Process Quality Control methods such as Batch Control and Sampling, can infer confidence in the design and process, but don’t ensure it. This paper proposes a different and unique approach to Product Design and Process Control, which will create an opportunity to eliminate redundancy, and can unlock significant design and process operational and capital cost savings. To truly ensure Quality Control of the structural adhesive bead, real-time verification along with Adaptive Process Control (APC) of the dispensing process is required. With fully guaranteed quality, design redundancies can be eliminated, and the design and processes can be concurrently optimized. The commitment to APC technology must happen very early in the Program, to enable an early focused design optimization effort to minimize the number of spot welds while maximizing structural adhesive use. Too often, the decision to invest in enabling technology in the manufacturing process, is made without considering design optimization. Also, the high-level decisions that can enable capital investment and operating cost savings are often caught in complex organizational finance processes. APC technology and equipment is purchased by Manufacturing entities, but without the confirmed product variable cost savings being identified early enough, the capital expenses won’t be justified or approved. This paper seeks to unravel that Catch-22 issue. Very few vehicle manufacturers are taking this pro-active step to commit to the use of APC technology in dispensing early in a vehicle program. Most OEM manufacturers design products in a very serial development process, design, tool, produce; with limited synergy due to the very rapid design cycle. They are missing a readily available opportunity to eliminate sub-optimization, added cost, added labor, and redundancy. To accomplish full efficiency and optimization, requires very close and early collaboration between the product design engineers and manufacturing engineers, and cross-organizational agreement.
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