Multivariate Statistics Between Two-Observation Spaces

As mentioned in the previous chapter, industrial data are usually divided into two categories, process data and quality data, belonging to different measurement spaces. The vast majority of smart manufacturing problems, such as soft measurement, control, monitoring, optimization, etc., inevitably require modeling the data relationships between the two kinds of measurement variables. This chapter’s subject is to discover the correlation between the sets in different observation spaces.

Innovative and Robust Application of Automation for Unit Level Traceability on Dual Die configuration of Micro Electromechanical System Products

Strip mapping for unit level traceability on die attach process of semiconductor companies provide quality driven impression for end users on the market. On processing of Micro electromechanical system packages, strip map generated by operators manually, certain errors and discrepancies are encountered and inevitably experienced by the production line. This causes misleading analysis on manufacturing problems and may lead to inappropriate and incorrect solutions hurting the process line. The application of modern technology and internet of things have been considered as an improvement. This is to eliminate human intervention errors caused by manual practice and promoted fool proof design of procedures. Having a user-friendly application with integration of modern technology drives significant improvement provide benefits to both supplier and customer of the manufacturing world.

A Study of an Effect on Moisture from 3D Printer Filament Drying Processes

This study investigated characteristics of moisture desorption for polylactic acid (PLA) filaments. The filaments tend to absorb moisture from humid air, led to moisten filaments. The absorption of even small amounts of moisture by filaments during storage and/or 3D printing, degraded the quality of final parts, and therefore, caused manufacturing problems. In this work, the filaments were subjected to humid conditions to achieve various moisture concentrations (0.75, 1.3 and 1.87 wt.%). Warm air-drying processes are used to reduce the moisture for different times (1, 2, 3, 4, 5 and 6 hours) and temperature (40, 50 and 60 °C). It was founded that the moisture from the polylactic acid (PLA) filaments can be discovered the moisture by use 60 degree of temperature in 5 hours warm air-drying process.

Engineered mRNA and the Rise of Next-Generation Antibodies

Monoclonal antibodies are widely used as therapeutic agents in medicine. However, clinical-grade proteins require sophisticated technologies and are extremely expensive to produce, resulting in long lead times and high costs. The use of gene transfer methods for in vivo secretion of therapeutic antibodies could circumvent problems related to large-scale production and purification and offer additional benefits by achieving sustained concentrations of therapeutic antibodies, which is particularly relevant to short-lived antibody fragments and next-generation, Fc-free, multispecific antibodies. In recent years, the use of engineered mRNA-based gene delivery has significantly increased in different therapeutic areas because of the advantages it possesses over traditional gene delivery platforms. The application of synthetic mRNA will allow for the avoidance of manufacturing problems associated with recombinant proteins and could be instrumental in consolidating regulatory approvals for next-generation therapeutic antibodies.

Surface Hydrophobization of Block-Shaped Wood with Rapid Benzylation

With the aim of utilizing wood as a carbon cycle-oriented material, the improvement of hydrophobicity has been actively studied to solve manufacturing problems, such as dimensional stability and biodeterioration resistance. The introduction of benzyl group is a promising chemical modification for hydrophobizing wood. However, conventional benzylation methods are not suitable for industrial applications because they require high temperature and long reaction times. In this study, a novel method was developed for quickly benzylating the surface of block-shaped wood using an aqueous solution of tetra-n-butylphosphonium hydroxide as a pretreatment solvent and no heat. The color and shape of the benzylated wood was almost unchanged from that before the treatment. Analysis of the resulting chemical structure suggested that the developed method causes less damage to carbohydrates compared with the conventional method, which involves heating and stirring. The proposed method successfully imparted hydrophobicity and thermoplasticity to the benzylated wood surface. Furthermore, hydrophobicity of the benzylated wood was further improved by a simple heat treatment for only approximately 5 min. The water contact angle became ≥110° and remained almost unchanged even after 1 min after water dropping.

Using the complexity index method to manage problems related to manufacturing

The Complexity Index method is an approach developed to help manufacturing companies quantify complexity in production. This paper sheds light on the connection between complexity and manufacturing problems and how the Complexity Index method was used to capture the areas in a production line with high levels of complexity to determine the sources of manufacturing problems related to labour time, surplus production, and manufacturing error. The main areas perceived as complex were due to Work Instructions, Work Content, and Product Variants. The perceived complexities were assessed for proper actions to be taken to decrease their level of complexity. The correlations between complexity and manufacturing problems were used for tracking related issues and ways for improvement. This study presents data on the use of workers’ perception to uncover the areas of complexity, which could be used by the management team to pragmatically capture difficulties and issues related to manufacturing problems to improve the production system.

Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain

Drug shortage is always a critical issue of inventory management in healthcare systems since it potentially invokes several negative impacts. In supply chain management, optimization goes hand-in-hand with inventory control to address several issues of the supply, management, and use of drugs. However, it is difficult to determine a shortage situation in a hospital due to multiple unpredictable reasons, such as manufacturing problems, supply and demand issues, and raw material problems. To avoid the shortage problem in a hospital, efficient inventory management is required to operate the system in a sustainable way and maximize the profit of the organization in the Hospital Supply Chain (HSC). In this work, we study a drug refilling optimization problem, a general model for drug inventory management in a hospital. We then investigate a Deep Reinforcement Learning (DRL) model to address this problem under an online solution that can automatically make a drug refilling decision in order to prevent a drug shortage. We further present a numerical result to verify the performance of the proposed algorithm, which outperforms the baselines (e.g., over-provisioning, ski-rental, and max-min) in terms of the refilling cost and the shortage rate.

Search Algorithms on Logistic and Manufacturing Problems

The supply chain comprehensively considers problems with different levels of complexity. Nowadays, design of distribution networks and production scheduling are some of the most complex problems in logistics. It is widely known that large problems cannot be solved through exact methods. Also, specific optimization software is frequently needed. To overcome this situation, the development and application of search algorithms have been proposed to obtain approximate solutions to large problems within reasonable time. In this context, the present chapter describes the development of Genetic Algorithms (an evolutionary search algorithm) for vehicle routing, product selection, and production scheduling problems within the supply chain. These algorithms were evaluated by using well-known test instances. The advances of this work provide the general discussions associated to designing these search algorithms for logistics problems.