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.

Effect of Seaweed Extracts on Ornamental Plants: Article Review

Seaweed extracts have been used in organic agriculture to encourage the development and strengthen the quality performance of floricultural crops. The effectiveness of the seaweed extract is built entirely on hormone levels of plants or otherwise micro nutrients in the crude extract (primarily cytokines). A review of the use of seaweed on ornamental plants is carried out in the most modern research. Concerning their growth and flowering possibilities, the effectiveness of algae in ornamental plants has been validated. The purpose of this systematic review was to illustrate progress throughout the treatment of seaweeds for growth regulators to summarize the organic compounds of seaweeds as well as to investigate the challenges that encourage the application of macroalgae to manipulate various biotic and abiotic stress of crops. Seaweeds are still completely unaffected internationally; we emphasize several of the subsequent preferences for research and innovation. This whole review aims to facilitate the reader’s attention to utilize various seaweeds to increase the features and yield of ornamental crops.

The relationship between quality performance and employee innovation for total quality management practices in academic libraries

The study examined the relationship between quality performance and employee innovation for total quality management in academic libraries. The study focused on academic libraries in Ghana. Relevant literature was reviewed on TQM practices, employee innovation, and quality performance. The study adopted a descriptive survey design and a quantitative approach. The research protocol was a questionnaire. The study sampled 213 and retrieved 208 responses representing 97.6% of the valid sample size for the study. The study established that employee innovation and quality performance are critical components for TQM practices in academic libraries. The correlation analysis established that employee innovation had a significant relationship with total quality management while quality performance also demonstrated a significant positive relationship with total quality management. The study revealed that employee innovation and quality performance are significant components for TQM practices in academic libraries. The study serves as a new source of documented information for academic libraries regarding TQM practices. Also, the study is a pillar for enriching the existing literature on employee innovation and quality performance as critical success factors for TQM implementation in academic libraries.

Increasing life of pit dump truck motor-wheel gearboxes by introducing the monitoring of the actual technical condition

At present, Kuzbass coal strip mines pay great attention to improving quality performance of mining equipment operation, including reliability and durability of components and units. One of the ways of the performance improvement is decreasing number of unforeseen failures. To achieve this purpose a mine dump truck part diagnostics should be introduced into a maintenance service procedure. At the same time the process of diagnostics should not increase the machinery downtime, but effectively reveal a condition of motor-wheel gearboxes in the course of dump truck operation. The aim of the research is to increase the reliability and service life of motor-wheel gearboxes of large BelAZ dump trucks. Failure of a motor-wheel gearbox is a rare phenomenon, but the cost of a new gearbox can vary from 3.5 to 10 million rubles. That is why it is important to implement such methods of diagnostics, which allow revealing the condition of gearboxes in the shortest possible time and without disassembling corresponding units. Determination of the actual technical condition of motor-wheel gearboxes is possible by different methods: vibroacoustic; acoustic; thermal; physical and chemical analysis of spent operating materials. The studies showed that none of these methods can be used as a universal one. When justifying and selecting the most suitable method, different factors should be considered, including technological, or a combination of methods should be applied, which will reduce risks, but at the same time increase costs. It is necessary to develop a better diagnostic method based on the use of several methods simultaneously.

Unifying The Evaluation Criteria Of Many Objectives Optimization Using Fuzzy Delphi Method

Many objective optimizations (MaOO) algorithms that intends to solve problems with many objectives (MaOP) (i.e., the problem with more than three objectives) are widely used in various areas such as industrial manufacturing, transportation, sustainability, and even in the medical sector. Various approaches of MaOO algorithms are available and employed to handle different MaOP cases. In contrast, the performance of the MaOO algorithms assesses based on the balance between the convergence and diversity of the non-dominated solutions measured using different evaluation criteria of the quality performance indicators. Although many evaluation criteria are available, yet most of the evaluation and benchmarking of the MaOO with state-of-art algorithms perform using one or two performance indicators without clear evidence or justification of the efficiency of these indicators over others. Thus, unify a set of most suitable evaluation criteria of the MaOO is needed. This study proposed a distinct unifying model for the MaOO evaluation criteria using the fuzzy Delphi method. The study followed a systematic procedure to analyze 49 evaluation criteria, sub-criteria, and its performance indicators, a penal of 23 domain experts, participated in this study. Lastly, the most suitable criteria outcomes are formulated in the unifying model and evaluate by experts to verify the appropriateness and suitability of the model in assessing the MaOO algorithms fairly and effectively.

Adjustable Robust Optimization Reformulations of Two-Stage Worst-Case Regret Minimization Problems

Although the stochastic optimization paradigm exploits probability theory to optimize the tradeoff between risk and returns, robust optimization has gained significant popularity by reducing computation requirements through the optimization of the worst-case scenario in a set. An appealing alternative to stochastic and robust optimization consists in optimizing decisions using the notion of regret. Although regret minimization models are generally perceived as leading to less conservative decisions than those produced by robust optimization, their numerical optimization is a real challenge in general. In “Adjustable Robust Optimization Reformulations of Two-Stage Worst-case Regret Minimization Problems,” M. Poursoltani and E. Delage show how to reduce a two-stage worst-case absolute/relative regret minimization problem to a two-stage robust optimization one. This opens the way for taking advantage of recent advanced approximate and exact solution schemes for these hard problems. Their experiments corroborate the high-quality performance of affine decision rules as a popular polynomial-time approximation scheme, from which, under mild conditions, one can even expect exact regret-averse decisions.