Process-related Errors Research Articles

Swift feedback and immediate error control using a lightweight simulation approach – A case study of the digital-twin-in-the-loop for machining thin-wall structures

The field of Digital Twins is rapidly growing, with an emerging trend of using virtual representations to directly control process-related errors in manufacturing operations. This paper introduces the Digital-Twin-in-the-Loop that uniquely integrates the Digital Twin (DT) into the direct feedback loop of a notoriously error-prone machining operation of low-rigidity structures. The proposed approach bypasses the need for post-process workpiece measurements by utilizing the twin to estimate 3D deformations caused by machining forces and correcting the toolpath automatically. For the first time, a fast and process-oriented simulation method, based on a mass-spring-lattice model for “on-the-fly” monitoring of workpiece deflections, as well as a comparative scaling method are presented as the backbone of the Digital-Twin. As a case study an end-milling operation is considered, focusing on the correction of the toolpath to improve the surface profile of low-stiffness components which are prone to dimensional errors. Experimental results show that the proposed method significantly improves the geometrical accuracy of the machined surface with a good repeatability, reducing errors by 78.96%. This establishes the Digital-Twin-in-the-Loop as a relevant tool for the implementation of time-efficient machining environments, while maintaining rigorous accuracy through in-process monitoring.

Safety of Enteral Nutrition Practices: Overcoming the Contamination Challenges.

Enteral nutrition (EN) has host of benefits to offer to critically ill patients and is the preferred route of feeding over parenteral nutrition. But along with the many outcome benefits of enteral feeding come the potential for adverse effects that includes gastrointestinal (GI) disturbances mainly attributed to contaminated feeds. Currently, EN is practiced using blenderized/kitchen prepared feeds or scientifically developed commercial feeds. Commercial feeds based on their formulation may be divided as ready-to-mix powder formulas or ready-to-hang sterile liquid formulas. A holistic view on potential sterility of EN from preparation to patient delivery would be looked upon. These sterility issues may potentially result in clinical complications, and hence process-related errors need to be eliminated in hospital practice, since immunocompromised intensive care unit patients are at high risk of infection. This review intends to discuss the various EN practices, risk of contamination, and ways to overcome the same for better nutrition delivery to the patients. Among the various types of enteral formulas and delivery methods, this article tries to summarize several benefits and risks associated with each delivery system using the currently available literature.How to cite this articleSinha S, Lath G, Rao S. Safety of Enteral Nutrition Practices: Overcoming the Contamination Challenges. Indian J Crit Care Med 2020;24(8):709–712.

An assembly sensitivity analysis method based on state space model

PurposeThis paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins and inertial navigation system (INSs), and establish the assembly process state space model for their assembly sensitivity research.Design/methodology/approachTo date, the process-related errors that cause large variations in key product characteristics remains one of the most critical research topics in assembly sensitivity analysis. This paper focuses on the unique challenges brought about by the multi-station system: a system-level model for characterizing the variation propagation in the entire process, and the necessity of describing the system response to variation inputs at both station-level and single fixture-level scales. State space representation is used to describe the propagation of variation in such a multi-station process, incorporating assembly process parameters such as fixture-locating layout at individual stations and station-to-station locating layout change.FindingsFollowing the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given assembly process parameters.Originality/valueA case study of assembly sensitivity for a multi-station assembly process illustrates and validates the proposed methodology.

ASPEN Safe Practices for Enteral Nutrition Therapy [Formula: see text

Enteral nutrition (EN) is a valuable clinical intervention for patients of all ages in a variety of care settings. Along with its many outcome benefits come the potential for adverse effects. These safety issues are the result of clinical complications and of process-related errors. The latter can occur at any step from patient assessment, prescribing, and order review, to product selection, labeling, and administration. To maximize the benefits of EN while minimizing adverse events requires that a systematic approach of care be in place. This includes open communication, standardization, and incorporation of best practices into the EN process. This document provides recommendations based on the available evidence and expert consensus for safe practices, across each step of the process, for all those involved in caring for patients receiving EN.

Assessing the impact of graphical quality on automatic text recognition in digital maps

Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not “properly” scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

Tool path re-planning in free-form surface machining for compensation of process-related errors

Free-form surfaces are widely used in many applications in today’s industry. This paper presents a new approach to identify and compensate process-related errors in machining of free-form surfaces. The process-related errors are identified online by a newly developed in-process inspection technique. In this technique, the surface is first machined through an intermediate semi-finishing process that is specifically designed to machine different geometric shapes on the surface with different process parameters. An inspection method is developed to identify the process-related errors in the selected regions on the semi-finished surface. The relationship between the machining/surface parameters and process-related error is then achieved using a neural network. This relationship is used to predict the process-related errors in the finishing process. The process-related errors, together with the machine tool geometric errors identified using a method developed in our previous work, are compensated in the finishing tool paths through tool path re-planning. Experiment has been conducted to machine a part with a free-form surface to show the improvements in the machining accuracy.

Error Detection and Correction Architecture of Finite State Machine State Transition for Nanometer Process Technology

Parameter variations and soft errors are the major design challenges in nanometer process technology, causing errors in the internal nodes. The basic concept for solving these problems at the circuit level, called design for variability (DFV), is to add error handling functions to the conventional circuits to increase robustness toward nanometer process-related errors. A DFV-aware finite state machine (FSM) is one of the major design challenges in nanometer circuits. Robust FSMs in a high-performance integrated circuit should not have a large area overhead, since it requires many FSMs and is usually very complex to implement. Most existing fault-tolerant FSMs can detect or correct only one bit error. We propose an area-efficient error detection and error correction architecture which can detect and correct multiple bit errors for nanometer FSMs.

An Adaptive Error Correction Method Using Feature-Based Analysis Techniques for Machine Performance Improvement, Part 1: Theory Derivation

An adaptive error correction method using a feature-based analysis technique for improving the accuracy of CNC machine tools is presented. The method described in this paper emphasizes the integration of the process-intermittent gauging and analysis techniques with information from pre-process characterization and post-process inspection. The proposed method utilizes the information from pre-process characterization, process-intermittent gauging, and post-process inspection to improve machine performance automatically. Algorithms are derived for analyzing the post-process and process-intermittent inspection data to decouple process-related errors from machine errors and for identifying the residual systematic errors. A feature-based analysis technique is developed to relate the dimensional and form errors of manufactured features to the systematic machine tool errors. Inverse kinematics and statistical methods are used to identify and characterize the contribution of each residual error component on imperfect features. Also a recursive tuning algorithm is developed for fine tuning the geometric-thermal model.

The Design of an Ultra-Precision CNC Measuring Machine

The design phase for a specialized ultra-precision CNC measuring machine has been completed. The machine is to be applied to the inspection of the inner and outer contours of hemispherical shells and other axisymmetric pans, with diameters of up to 400 mm. The overall accuracy of the machine operating in continuous path contouring mode is to be less than 0.75 micrometre per surface. This includes both instrument and process-related errors. In order to ensure that performance goals will be met, extensive use has been made of analytical tools during the design process. Finite element modelling of the entire machine system was used to predict static and dynamic structural behaviour and the thermal response of the machine under different loading conditions. Analytical results were merged with calculated and assumed parametric error values into a derailed error budget, which was used iteratively during the design process as a tool for refining the design. The error budget indicates that the completed machine will meet specified accuracy requirements. A comparison of predicted and actual machine accuracy will be presented at the completion of the project UI 1991.