Quality Control Perspectives during Mass Production with a Focus on the Chemical Industry

Mass production was part of the industrial revolution in 1870 and, with it, a huge step change in manufacturing processes. Its impact was ground breaking and became even more remarkable with automation in a business production environment. The chemical industry is one of the manufacturing sectors that has benefited from the technology of mass production achieved through automating the business process. In this era of industry 4.0 and with the associated advanced technologies of smart manufacturing, cloud computing, cyber physical systems and internet of things, mass production has been revolutionised but still faced issues such as quality control of the production process which was affected by supply chain management, customised production of commodity and specialty chemicals and huge demand from other chemical industry manufacturers. This chapter has reviewed the evolution of mass production during traditional manufacturing to the present day and carried out a risk assessment to quality of production in a mass production environment with a view to recommending adequate quality control of the production process. The chapter also included a case study for mass production of a pharmaceutical drug—Amoxicillin which was partly batch produced into dry powder and then mass produced using tableting and encapsulating machine, highlighting sources of contamination and inconsistency in tablet weight if adequate control measures were not put in place

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eightcoupled tank plant via simulation

A hybrid decision support system for managing humanitarian relief chains

Decisions regarding location, allocation and distribution of relief items are among the main concerns of the Humanitarian Relief Chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes. In this paper, a Hybrid Decision Support System (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under generated number of disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities’ locations, relief items’ allocation and distribution plan of the scenario under investigation based on calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data

Mass Production Processes

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes