Process monitoring and visualization solutions for hot-melt extrusion : a review

Objectives: Hot-melt extrusion (HME) is applied as a continuous pharmaceutical manufacturing process for the production of a variety of dosage forms and formulations. To ensure the continuity of this process, the quality of the extrudates must be assessed continuously during manufacturing. The objective of this review is to provide an overview and evaluation of the available process analytical techniques which can be applied in hot-melt extrusion. Key Findings: Pharmaceutical extruders are equipped with traditional (univariate) process monitoring tools, observing barrel and die temperatures, throughput, screw speed, torque, drive amperage, melt pressure and melt temperature. The relevance of several spectroscopic process analytical techniques for monitoring and control of pharmaceutical HME has been explored recently. Nevertheless, many other sensors visualizing HME and measuring diverse critical product and process parameters with potential use in pharmaceutical extrusion are available, and were thoroughly studied in polymer extrusion. The implementation of process analytical tools in HME serves two purposes: (1) improving process understanding by monitoring and visualizing the material behaviour and (2) monitoring and analysing critical product and process parameters for process control, allowing to maintain a desired process state and guaranteeing the quality of the end product. Summary: This review is the first to provide an evaluation of the process analytical tools applied for pharmaceutical HME monitoring and control, and discusses techniques that have been used in polymer extrusion having potential for monitoring and control of pharmaceutical HME

Harnessing IoT Data and Knowledge in Smart Manufacturing

In the modern digitalized era, the use of electronic devices is a necessity in daily life, with most end users requiring high product quality of these devices. During the electronics manufacturing process, environmental control, for monitoring ambient temperature and relative humidity, is one of the critical elements affecting product quality. However, the manufacturing process is complicated and involves numerous sections, such as processing workshops and storage facilities. Each section has its own specific requirements for environmental conditions, which are checked regularly and manually, such that the whole environmental control process becomes time-consuming and inefficient. In addition, the reporting mechanism when conditions are out of specification is done manually at regular intervals, resulting in a certain likelihood of serious quality deviation. There is a substantial need for improving knowledge management under smart manufacturing for full integration of Internet of Things (IoT) data and manufacturing knowledge. In this chapter, an Internet-of-Things Quality Prediction System (IQPS), which is a mission critical system in electronics manufacturing, is proposed in adopting the advanced IoT technologies to develop a real-time environmental monitoring scheme in electronics manufacturing. By deploying IQPS, the total intelligent environmental monitoring is achieved, while product quality is predicted in a systematic manner

Application of six sigma methodology to reduce defects of a grinding process

Six Sigma is a data-driven leadership approach using specific tools and methodologies that lead to fact-based decision making. This paper deals with the application of the Six Sigma methodology in reducing defects in a fine grinding process of an automotive company in India. The DMAIC (Define–Measure–Analyse–Improve–Control) approach has been followed here to solve the underlying problem of reducing process variation and improving the process yield. This paper explores how a manufacturing process can use a systematic methodology to move towards world-class quality level. The application of the Six Sigma methodology resulted in reduction of defects in the fine grinding process from 16.6 to 1.19%. The DMAIC methodology has had a significant financial impact on the profitability of the company in terms of reduction in scrap cost, man-hour saving on rework and increased output. A saving of approximately US$2.4 million per annum was reported from this project

Pengukuran Kemampuan Proses Menggunakan Pendekatan Six Sigma pada Proses Pencetakan Produk Paperbag (Studi Kasus PT. X)

[Title : Capabilities Process Measurement Using Six Sigma on Paperbag Printing Process (Case Study PT. X)] PT. X is engaged in manufacturing stationery and one of them is paperbag. PT. X always puts the quality of its products by conducting quality control of production processes in all stages of the process. This study is conducted to identify the causes of defects and to help improving quality by minimizing the occurrence of defective products so that the products comply to defined specifications. One method of improving the quality of which can accommodate the demands of quality improvement is Six Sigma method with the DMAIC phases. DPMO value of defects paperbag obtained for 15281 with a value of 3.85 sigma level. Pareto diagram obtained from paperbag defect rate that gives the biggest contribution was a series of scratches of ink or called “nyemet”. Fishbone diagram of the cause of the defect consists of the human, machinery, material and method factor

Feedback of evidence into practice

Concern about risks associated with medical care has led to increasing interest in quality improvement processes. Most quality initiatives derive from manufacturing, where they have worked well in improving quality by small, steady increments. Adaptations of quality processes to the healthcare environment have included variations emphasising teamwork; large, ambitious increments in targets; and unorthodox approaches. Feedback of clinical information to clinicians is a central process in many quality improvement activities. It is important to choose feedback data that support the objectives for quality improvement - and not just what is expedient. Clinicians need to be better educated about the quality improvement process to maintain the quality of their care

On Deterministic feature-based Surface Analysis

Manufacturing sector is continuously identifying opportunities to streamline production, reduce waste and improve manufacturing efficiency without compromising product quality. Continuous improvement has been the primary objective to produce acceptable quality products and meet dynamic customer demands by using advanced techniques and methods. Considering the current demands from society on improving the efficiency with sustainable goals, there is considerable interest from researchers and industry to explore the potential, to optimize- and customize manufactured surfaces, as one way of improving the performance of products and processes.Every manufacturing process generate surfaces which beholds certain signature features. Engineered surfaces consist of both, features that are of interest and features that are irrelevant. These features imparted on the manufactured part vary depending on the process, materials, tooling and manufacturing process variables. Characterization and analysis of deterministic features represented by significant surface parameters helps the understanding of the process and its influence on surface functional properties such as wettability, fluid retention, friction, wear and aesthetic properties such as gloss, matte. In this thesis, a general methodology with a statistical approach is proposed to extract the robust surface parameters that provides deterministic and valuable information on manufactured surfaces.Surface features produced by turning, injection molding and Fused Deposition Modeling (FDM) are characterized by roughness profile parameters and areal surface parameters defined by ISO standards. Multiple regression statistics is used to resolve surfaces produced with multiple process variables and multiple levels. In addition, other statistical methods used to capture the relevant surface parameters for analysis are also discussed in this thesis. The selected significant parameters discriminate between the samples produced by different process variables and helps to identify the influence of each process variable. The discussed statistical approach provides valuable information on the surface function and further helps to interpret the surfaces for process optimization.The research methods used in this study are found to be valid and applicable for different manufacturing processes and can be used to support guidelines for the manufacturing industry focusing on process optimization through surface analysis. With recent advancement in manufacturing technologies such as additive manufacturing, new methodologies like the statistical one used in this thesis is essential to explore new and future possibilities related to surface engineering

Application of Value Stream Mapping for Reduction of Cycle Time in a Machining Process

AbstractLean manufacturing initiative is being followed by various organizations in the recent years which mainly focuses on improving the efficiency of operations by eliminating and reducing wastes. This paper aimed to explain the implementation of lean manufacturing techniques in the crankshaft manufacturing system at an automotive manufacturing plant located in south India.. A multi criteria decision making model, analytical hierarchy process is applied to analyze the decision making process in the manufacturing system. The objective of the case industry was to increase the export sales. Lean manufacturing system was selected to meet the company“s quality, cost and delivery targets. Crankshaft was manufactured in a single piece flow system with the low cost machines developed indigenously and the results are that the crankshafts have passed the testing, validation and approval by the customer to produce any variant in the company. After implementing lean manufacturing system, the manufacturing lead time reduced by forty percent, defects were reduced, higher process capability achieved, quick response to the customer demand in small lots were achieved

DESIGN OF A CUSTOM SOFTWARE APPLICATION TO MONITOR AND COMMUNICATE CNC MACHINING PROCESS INFORMATION TO AID IN CHATTER IDENTIFICATION

In any manufacturing environment, it is important to be able to monitor the Computer Numerical Control (CNC) machining process so that high quality parts can be produced in the least amount of time in order to be profitable. This involves acquiring the proper parameters needed from the machine\u27s controller, which can prove to be difficult with proprietary machine tools that tend to limit access to the internal data collected by the controller. This closed approach to controller design also means that many technological advances that have recently become prevalent in society are not being adopted in the manufacturing industry, preventing the interoperability between hardware and software components and adding to the shortcomings in communicating the necessary machining parameters to machine operators. The project described in this thesis offers a solution to some of the communication, productivity, and part quality problems in the American manufacturing industry by providing a custom software application that integrates MTConnect, an emerging interoperable data communication standard, with proprietary data acquisition tools and custom sensors to monitor and communicate CNC machining process information. The application described in this thesis was designed to aid in the identification of chatter conditions to the machine operator and to other users to take action for chatter suppression and avoidance. Chatter is an undesirable phenomenon that can reduce part quality and increase tool wear. These consequences result in higher costs to replace damaged parts and tools as well as increasing the amount of machine downtime which can reduce a company\u27s overall productivity. Once chatter is detected in the audible frequency range, damage to the workpiece has already occurred. Therefore, an early identification and communication method with the machine tool is warranted to easily monitor the machine in the event of impending dynamic part damage. This application was developed to provide a means to monitor cutting conditions to reduce and prevent chatter in the machining process and to aid in analysis to avoid subsequent unstable operating conditions. Preserving part quality and productivity in manufacturing is also dependent on accurate information provided about the specific parts involved in the machining process. In addition to monitoring the process, this application facilitates the communication of part-specific information by improving the input and tracking of part numbers, and organizes the machining process information in a central location according to the specific part. Improving the part tracking process can aid in the organization of data to analyze the machining process for increased quality in future operations. The application can also be customized for other implementations, which can benefit many different industrial manufacturing facilities as well as academics in performing experimental research. It is important for the manufacturing industry and its partners in academia to be able to bridge the communication gap to increase the knowledge of the machining process and therefore manufacturing productivity and profitability