Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

Process Capability Assessment Using Vision System

Surface roughness assessment would help in predicting a component’s functionality. This clearly shows the significance of measuring the surface roughness of machined components. Thus, each machined component, depending upon its intended function, requires a certain surface finish. To predict the surface roughness of a machined component, a detailed understanding of the machining parameters is essential. This is because, surface roughness generated on a component, depends upon machining parameters speed, feed, and depth of cut. A stable manufacturing process gives a consistent surface finish on all the manufactured components. Thus, only by having a stable process, consistent quality of manufactured products is possible. The capability of the machine is defined as the capability of the machine to carry out the set process efficiently and effectively to produce parts as per the specification limits. Machining parameters, tools, coolant flow rate, etc. An effort has been made in this research work, to show how by measuring surface roughness of machined components process capability can be assessed. Thus, the method is a novel technique of assessing the process capability of a given process. A capable process would help a manufacturing company in meeting customer expectations. The proposed method is of non-contact type, quick, and industry-friendly.

Knowledge Management and Organizational Performance: The Neglected Role of Institutional Accounting Practices

The study highlights the part of institutional accounting practices in the relationship between specified KM capabilities and institutional performance. A theoretical model was tested based on the insight from literature and knowledge-based theory (KBT). The data collected from a survey of 322 staff in knowledge-based organizations (KBOs) were analyzed to test the extended model using partial least squares structural equation modeling approach. The result depicts that greater levels of specific KM infrastructure and process capability would positively influence institutional accounting management practices and, consequently organizational performance. Unlike KM process capability, KM infrastructure capability has a positive and significant impact on organizational performance. The study provides a new understanding to management and practice on the vital role played by institutional management accounting practice in KM success in Malaysia. The research offers fresh insight into further studies in diverse settings. The research is insightful as it deviates from the over-researched context in KM literature to extricate the role of accounting in the business KM strategy.

Modelling of Thermal Quantities for Quick Process Assessment and Process Capability Space Refinement at an Early Design Phase During Laser Welding

This research aims at understanding the impact of welding process parameters and beam oscillation on the weld thermal cycle during laser welding. A three-dimensional heat transfer model is developed to simulate the welding process, based on the finite element (FE) method. The calculated thermal cycle and weld morphology are in good agreement with experimental results from literature. By utilizing the developed heat transfer model, the effect of welding process parameters such as heat source power, welding speed, radius of oscillation, and frequency of oscillation on the intermediate performance indicators (IPIs) such as peak temperature, heat-affected zone volume (HAZ), and cooling rate is quantified. Parametric contour maps for peak temperature, HAZ volume, and cooling rate are developed for the estimation of the process capability space. An integrated approach for rapid process assessment, process capability space refinement, based on IPIs is proposed. The process capability space will guide the identification of the initial welding process parameters window and help in reducing the number of experiments required by refining the feasible region of process parameters based on the interactions with the IPIs. Here, the peak temperature indicates the mode of welding performed while the HAZ volume and cooling rate are weld quality indicators. The regression relationship between the welding process parameters and the IPIs is established for quick estimation of IPIs to replace time-consuming numerical simulations. The proposed approach provides a unique ability to simulate the laser welding process and provides a robust range of process parameters.

Process Capability Analysis in a Polypropylene Bag Manufacturing Company

Process capability analysis combines statistical tools and control charts with good engineering judgment to interpret and analyze the data representing a process. This work analyzes the process capability of a polypropylene bag producing company. The case study organization uses two plants for production and data was collected over a period of nine months for this study. Analysis showed that the output spread of plant 1 was greater than the specification interval spread which implies poor capability. There are non-conforming parts below the Lower Specification Limit (LSL: 500,000 metres) and above the Upper Specification Limit (USL: 600,000 metres) and that the output requires improvement. Similarly, the capability analysis of plant 2 shows that the overall output spread is greater than the specification interval spread (poor capability). The output centre in the specification and overall interval are vertically aligned, thus specifying that the output from plant 2 is also process centered and requires improvement. Recommendations were made to improve the outputs from each production plant.

Stability assessment and process capability analysis in a food pasta company

Statistical quality control is a set of tools and techniques that allows to verify, monitor and control the variability of processes to improve product quality and business competitiveness. The objective of this study was to evaluate the pasta production process of a company that belongs to the food industry sector in terms of stability and compliance of quality specifications. The Six Sigma improvement methodology was used, which focuses on identifying and eliminating the causes of variation in the processes. Data collection was accomplished by the use of different techniques, such as: interviews, brainstorming, review of documents, teamwork and direct observation. In addition, process documentation techniques and classical quality tools including Pareto chart, control charts, process capability analysis, histogram, Ishikawa diagram and experimental design were used. Multivariate data reduction techniques were also applied. The results showed for the quality characteristic Humidity that the process is out of statistical control and it is uncapable to meet the required specifications, for which the causes were investigated and improvement actions were proposed, achieving an increase in the sigma quality level.