In an age of swift technological progress and increasingly complex market demands, industries are leveraging advanced manufacturing methods to streamline processes and enhance output. This surge in high-volume production, while advantageous in terms of efficiency and capacity, also brings forth significant challenges—specifically in terms of quality control. Balancing speed and volume with stringent quality assurance is no trivial endeavor. Yet, it is a critical facet of modern manufacturing design that industries must navigate with agility and precision.
This article explores the dynamic landscape of advanced methods for maintaining quality control in high volume manufacturing design. From state-of-the-art technologies like Artificial Intelligence (AI) and Machine Learning (ML) to sophisticated neural networks, we delve into an array of strategies that industries are employing to ensure product quality in the era of mass production.
Understanding High-Volume Manufacturing
High-volume manufacturing (HVM) is a process that involves the mass production of goods on a scale requiring highly sophisticated machinery and careful planning to meet the demands of large-scale production. It is a systematic approach that focuses on design, control, and management of systems and processes to create sizable quantities of goods in the most cost-effective and efficient manner.
This process heavily relies on standardization, ensuring all products meet specific standards for consistency and uniformity. Automation plays a critical role in HVM, integrating everything from automated machinery and conveyor systems to robotics and artificial intelligence. The drive towards maximum efficiency is also a crucial feature of HVM as it seeks to minimize waste, optimize supply chains, and continuously improve processes to achieve economies of scale. However, the high capital investment in machinery, infrastructure, and technology necessary for HVM cannot be overlooked.
Despite the potential rewards of high-volume manufacturing, maintaining quality control in such an environment presents a unique set of challenges. Ensuring consistency is a significant challenge as even minor deviations in the manufacturing process can lead to substantial quality issues due to the scale of production. Detecting and addressing defects quickly and efficiently is crucial to prevent waste and additional costs.
High-volume manufacturing's complexity, including its intricate processes and supply chains, can create difficulties in monitoring and controlling quality. Likewise, due to the high degree of automation and standardization, ensuring staff are appropriately trained and that their skills are continually updated is vital for maintaining quality. Let’s jump into some modern techniques that can help engineers ensure quality control in high volume manufacturing environments.
Machine Learning and Edge Computing: Enhancing Quality Control
The integration of machine learning and edge computing in quality control can significantly improve the efficiency and reliability of high-volume manufacturing processes. Machine learning, a subset of artificial intelligence, enables systems to recognize patterns and laws based on existing data and algorithms, thereby developing autonomous solutions. On the other hand, edge computing allows data processing and model application to be performed on the edge, resulting in shorter response times, more efficient processing, and less pressure on the network.
Machine learning techniques can be applied to various quality tasks in manufacturing, including the description of product/process quality, classification of quality, quality prediction, and parameter optimization. For instance, a predictive model can map available quality-related input information and data to the resulting product quality. This model can then be used to predict quality feature values from a given set of input parameters, enabling a range of measures to be applied to achieve economic manufacturing. These measures include the reduction of scrap through early control interventions, optimization of process parameter settings and product quality, stabilization of processes, dynamization of inspection plans, and the design of model-based inspection processes.
Edge computing complements machine learning by addressing the need for real-time data processing. High-level applications often require large data volumes and sophisticated machine learning models, which can strain network bandwidth and reliability. Edge computing alleviates these challenges by reducing network latency and bandwidth problems, thereby enabling faster and more efficient quality control processes.
The integration of machine learning and edge computing in quality control can be implemented in the existing IT plant infrastructure. A real-world example of this integration is seen in Surface Mount Technology (SMT) manufacturing, where the proposed method significantly reduced inspection volumes, leading to economic advantages.
The combination of machine learning and edge computing offers a promising approach to improving quality control in high-volume manufacturing design. By enabling predictive, model-based quality inspection and efficient data processing, these technologies can help manufacturers maintain high quality standards while enhancing economic efficiency.
Case Study: Detecting Problematic Slab Shapes in Steel Rolling
In a case study, a deep learning approach was used to detect steel sheets that have a high risk of getting telescoped when coiled. Telescoping occurs when a steel sheet is obliquely reeled, causing the resulting coil to be skewed. This is a costly problem that requires a procedure to straighten out the steel sheet. Therefore, it is important to detect deformations in the steel slab that would increase the risk of telescoping as early as possible.
The data used for this study consists of a time series of measurements over steel slabs. The challenge with this data is that it is non-stationary and non-linear, and multiple kinds of products with different sizes are manufactured, resulting in different numbers of measurements between different slabs. To overcome this challenge, a recurrent neural network (RNN) with long short term memory cells (LSTM-cells) was used to analyze the shape of the slabs.
One drawback of using an RNN with LSTM-cells is that it is difficult to interpret and understand the reasoning behind the predictions made. To overcome this drawback, an attention mechanism was added to the network. This allows for the highlighting of areas of the shape of the slab from which the network draws its final conclusion, and, hence, allows the operator to better understand and therefore take a more suitable action in order to correct the highlighted flaws and possibly minimize the risk of telescoping.
The RNN with LSTM-cells, combined with an attention mechanism, achieved an area under the receiver operating characteristic curve (AUC-ROC) score of 0.85. This allows for the detection of more than 80% of all flawed steel coils while having less than 25% false positive predictions (false alarms). In addition to resulting in a high AUC-ROC score, the presented method also shows how the recurrent neural network reasons, in contrast to most other deep learning methods. Using the attention mechanism, the reasoning behind the prediction can be visualized in order to extract knowledge about the process and to make suitable process improvements.
The necessity for advanced methods in maintaining quality control in high volume manufacturing design cannot be overstated. It is not merely an operational requirement, but a critical determinant of an organization's success in the hyper-competitive modern manufacturing landscape. By leveraging cutting-edge technologies such as AI, ML, robotics, and IoT, industries are not only enhancing their capacity for quality control but are also driving innovation, reducing waste, and improving efficiency.
