Changeover Time Research Articles

Study Evaluates Long-Term Corrosion Risk of Load-Bearing, High-Power ESP Cable

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 34898, “Long-Term Corrosion-Risk Evaluation of Load-Bearing, High-Power Electrical Submersible Pump Cable,” by Serko A. Sarian, SPE, Alkhorayef Petroleum, and Hattan Banjar, SPE, and Chidirim Ejim, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ In this study, a fit-for-purpose electrical submersible pump (ESP) load-bearing power cable was deployed inside the production tubing of a sour oil well for an extended period of service. Project economics required the power-cable metallurgy to survive long-term exposure reliably for up to 15% hydrogen sulfide (H2S) and high-salinity production fluids. Laboratory tests simulating varying sour well and extended galvanic corrosion conditions have been performed to determine the adequacy of the selected armor metallurgy in downhole corrosive environments. Introduction In recent years, various types of load-bearing power cable-deployed ESP (CDESP) systems have been introduced, eliminating the need to install or remove a conventional ESP along with the completion and reducing ESP changeover time by more than 80%. More recently, live-well CDESP systems have been developed and tested to eliminate the need for a well-kill operation. The most-critical and costly component of such a system is the load-bearing power cable. The cable has the dual purpose of conveyance and long-term powering-up of the ESP. In a conventional artificial lift operation, the ESP is conveyed using the production tubing with a non-load-bearing power cable strapped to the outside of the tubing. This operation requires a workover rig to remove and reinstall the entire completion and a killed-well operation that might permanently damage the reservoir. The main advantages of a CDESP system include operational efficiency gain and cost reduction, no reservoir skin damage, and production-loss minimization. However, a few challenges exist with the use of CDESP. These cables must have significantly different structures from commercial oil and gas power cables. These differences are detailed in the complete paper. Cable-Design Requirements CDESP system criteria include multiple-year continued service in adverse oilwell and reservoir conditions such as sour fluids, high gas/oil ratios (GOR) and temperatures, and abrasion caused by solids production. The following product acceptance requirements were set in advance: - Three redeployments or retrievals after the initial deployment - Single deployment continued runtime to meet or exceed the regional conventional ESP system runtime Based on regional statistics, a conventional ESP system single-deployment average run time is 2.5 years. The effective CDESP power-cable service life target then becomes 2.5 years × four total deployments = 10 years, which served as the basis for all cable-associated testing and reliability metrics.

Разработка алгоритма оперативного планирования серийного дискретного производства с альтернативными цепочками операций в технологических процессах

The problem of the theory of schedules with an additional condition – the need to choose an alternative chain of operations in the technical processes of products is considered. A two-stage planning algorithm is proposed, the first stage of which is the selection of chains of operations suitable for a certain criterion from the given alterna-tives, after which the task is reduced to the classic JSSP (Job-Shop Scheduling Problem) problem. At the second stage, the selected production operations are arranged on the machines, taking into account the order of the technological process and other restrictions. Minimization of changeover time in production was chosen as an optimization criterion. The description of the algorithm and its implementation is given on the example of the cable industry (production of wiring harnesses). Both stages of planning are implemented on the basis of greedy algorithms, the results of test measurements on various amounts of data (up to tens of thousands of operations) are presented. The implementation is made in C# 10 using a free platform .NET 6. The vector of further research is the implementation of more complex algorithms (in particular, based on evolutionary methods) in order to obtain more optimal plans.

Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data

ABSTRACT Changeover events occur in every industrial production when a machine is prepared and setup for production of the next product variant. Changeover times must be acquired with a high degree of validity for product cost calculations, order sequencing, and work schedules. The novelty of this article is a Machine Learning (ML) approach to automatically detect changeover events in production on manufacturing machines without direct human feedback. The machine learning approach uses several algorithms to classify different phases of the changeover process. The changeover of a milling process was defined using different phase concepts (2-phases, 6-phases, 23-phases) to be applicable to other types of manufacturing machines. Different machine learning methods were compared. The best results for the F1 score were achieved with the Random Forest, the CatBoost, and the Extra Trees algorithm (2-phases: 99.4–99.7%, 6-phases: 85.2–85.9%, 23-phases: 77.7–79.4%). It is shown that detecting changeover events can be realized based on data from an NC of a manufacturing machine without data from external sensors (2-phases: 98.9%, F1 score).

Evaluation of community engagement for resilience outcomes: A pre-engagement approach

Recent disasters have placed enormous pressure on communities to be more resilient. Community resilience encompasses a range of structural, economic, and social dimensions that affect a community's ability to withstand change and disruption from disaster events. In recognition of the social processes that contribute to resilience, governments have increased investment in community engagement programs based on the assumption these programs of activity contribute to community resilience. While several studies identify the indicators of community resilience, few operationalize these indicators for community engagement in a way that can be used to establish baselines and evaluate change overtime. This paper addresses this gap. In-depth interviews with 16 community engagement practitioners in Australia investigated how community resilience in flood prone communities was conceptualized and how the outcomes from engaging communities for resilience building were identified. Findings from the study and the extant literature on resilience empirically informed the development of a four-step pre-engagement approach that extends current community engagement models and supports the measurement of engagement activities and aligned resilient outcomes. The pre-engagement approach can be adapted to diverse community contexts beyond flood emergencies and beyond Australian borders.

Optimisation of process plan generation for reconfigurable manufacturing systems: efficient heuristics and lower bounds

ABSTRACT We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances

International Knee Documentation Committee Subjective Knee Form Latent Growth Model Analysis: Assessing Recovery Trajectories.

Patient-Reported Outcome Measures (PROMs), such as the six-item International Knee Documentation Committee Subjective Knee Form (IKDC-6), play a crucial role in assessing health conditions and guiding clinical decisions. Latent Growth Modeling (LGM) can be employed to understand recovery trajectories in patients post-operatively. Therefore, the purpose of this study was to assess LGM properties of the IKDC-6 in patients with knee pathologies that require surgical intervention and to assess differences between subgroups (i.e., sex and age). A cross-sectional study was conducted using the Surgical Outcome System (SOS) database with patients who had undergone knee arthroscopy. Our results found that preoperative scores did not influence the rate of change overtime. Perceived knee health improved over time, with varying rates among individuals. The adolescent age subgroup and male subgroup exhibited faster recovery rates compared to the older age subgroup and female subgroup. While initial hypotheses suggested IKDC-6 could serve as a prognostic tool, results did not support this. However, results indicated favorable outcomes irrespective of preoperative perceived knee impairment levels. This study provides valuable insights into recovery dynamics following knee surgery, emphasizing the need for personalized rehabilitation strategies tailored to individual patient characteristics.

METODOLOGÍA PARA MAXIMIZAR EL OBJETIVO DE PÉRDIDAS CERO MEDIANTE SMED EN LA INDUSTRIA ALIMENTARIA

The application of SMED (Single Minute Exchange of Die) gives rise to more than just reduced machinery changeover times. Correct application of SMED also reduces other types of losses which occur in the workplace and which all companies consider highly significant because of the industrial and social context in which they operate. The proposed methodology integrates prior analysis of both machinery and process, analysis and structuring of tasks, the expertise of personnel involved in applying SMED, tools that support SMED, KPI/KAI (Key Performance Indicator/Key Activities Indicator) analysis systems and standardisation. This will enable reductions in other workplace losses, such as occupational accidents and process defects that can lead to poor-quality products, as well as continuous monitoring of the machinery to reduce the number of breakdowns, etc. This article shows that the application of SMED brings discipline, task analysis, and a better understanding of machinery and worker expertise, at the same time helping to improve behaviours - indispensable aspects when seeking improvements. Usage of the performance indicators set out has been the key to demonstrating that if workplace accidents and manufacturing costs are decreased, the quality of the manufactured product is increased. Keywords: SMED; Zero losses; 5 Whys, Breakdown Analysis; Twelve Steps

Learning with a purpose: Acquisition of compliments and compliment responses by beginning learners of Spanish in synchronous online exchanges

This research examines the effect of explicit pragmatic instruction and student's perceptions of guided reflections on the acquisition of compliments and compliment responses (CRs) in beginning learners of Spanish. It adopts a pragmatic consciousness-raising approach and a four-step lesson plan for teaching compliments and CRs in the language classroom. Unlike many studies in interlanguage pragmatics conducted in classrooms or laboratory settings at the intermediate and advanced level, this study examines the acquisition and use (or lack thereof) of compliments and CR's though synchronous online face-to-face conversations of L2 learners with L1 speakers of Spanish. It analyzes change overtime with the same learners using a pretest-posttest design. A quantitative analysis indicated statistically significant differences in the production of compliments, yet no significant differences were observed in the production of CR's. The qualitative analysis of the guided reflections highlighted a need for more in-class practice with compliments and CR's. Students' responses suggest that reflections can help raise awareness of the use (or lack thereof) of these speech acts. This study contributes to L2 pragmatics and to our understanding of L2 acquisition developmental stages.

Improving changeover techniques in apparel manufacturing through single minute exchange of die: implications for benchmarking

PurposeBangladesh's ready-made garment (RMG) industry plays a vital role in the economic growth of this country. As the global trend in the fashion market has introduced a high-mix, low-volume ordering style, manufacturers are facing an increased number of changeovers in their production systems. However, most of the Bangladeshi RMG manufacturers are not yet ready to respond to such small orders and to improve the flexibility of their production systems. Consequently, the industry is falling behind in global market competition. Thus, this study aims to advance the current performance of RMG manufacturing operations to respond to the fast-fashion industry's challenges effectively using quick changeover.Design/methodology/approachIn this study, a Single-Minute Exchange of Dies (SMED) is applied to attain quick changeover following the best practices of lean manufacturing.FindingsThis study examined the performance of the SMED technique to reduce changeover time in two case organisations. The changeover time was reduced by 70.76% from 434.56 min to 127.08 min and 42.12% from 2,664 min to 1,542 min for the case organisations, respectively. The results of this study show that companies require improved changeover times to address the demand for high-mix, low-volume orders.Originality/valueThis study will certainly guide practitioners of the RMG industry to adopt SMED to reduce changeover time to meet small batch production.

Economic Impact of Lean Healthcare Implementation on the Surgical Process.

The objective of this study was to analyse and detail surgical process improvement activities that achieve the highest economic impact. Over 4 years, a team of technicians and healthcare professionals implemented a set of Lean surgical process improvement projects at Vall d'Hebron University Hospital (VHUH), Barcelona, Spain. Methods employed in the study are common in manufacturing environments and include reducing waiting and changeover time (SMED), reducing first time through, pull, and continuous flow. Projects based on these methods now form part of the daily routine in the surgical process. The economic impact on the hospital's surgical activity budget was analysed. Process improvements have led to annual operational savings of over EUR 8.5 million. These improvements include better patient flow, better management of information between healthcare professionals, and improved logistic circuits. The current cultural shift towards process management in large hospitals implies shifting towards results-based healthcare, patient-perceived value (VBHC), and value-added payment. A Lean project implementation process requires long-term stability. The reason a considerable number of projects fail to complete process improvement projects is the difficulty involved in establishing the project and improving management routines. Few studies in the literature have investigated the economic impact of implementing Lean management a posteriori, and even fewer have examined actual cases. In this real case study, changes to surgical block management were initiated from stage zero. After being carefully thought through and designed, changes were carried out and subsequently analysed.

The product–mold–machine manufacturing problem: Complexity, MILP models and constructive heuristics

This study addresses the product–mold–machine manufacturing problem, a problem intrinsic to the fabrication processes in industries such as tires, plastics, ceramics, and glass. Each product possesses distinct shape and mechanical attributes, resulting from processing its corresponding mold on a specific machine. Uniquely, machines in this problem can handle multiple molds concurrently, implying that their processing time is dictated by the longest mold processing time within the set being used. The goal is to devise a production strategy that reduces the overall periods required to satisfy each product’s demand. This has to be achieved under constraints like limited availability of machines and molds, changeover times, and incompatibility constraints. We establish the problem’s NP-complete nature in the strong sense. Further, we introduce two mixed-integer linear programming (MILP) models and two polynomial-time constructive heuristics. These heuristics offer upper bounds for the optimal makespan and present easily implementable feasible solutions. Our numerical tests, conducted on real-world data, evaluate the efficacy of different solution strategies, specifically those using the preliminary insights from our heuristics to guide the mathematical formulations. The outcomes strongly advocate for leveraging these feasible solutions as starting points when solving the MILP models. Additionally, the notable efficiency and solution quality of our heuristics spotlight them as potent alternatives, especially in settings where traditional optimization tools might be absent.

Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing

While new production areas (greenfields) have state-of-the-art technologies for implementing digitalization, existing production areas (brownfields) and devices must first be upgraded with technologies before digitalization can be implemented. The aim of this research work is to use a case study to identify the differences in the implementation of machine learning (ML) projects in brownfields and greenfields. For this purpose, an ML application for the detection of changeover times on milling machines is implemented and analyzed in the brownfield and greenfield scenarios as well as a combined scenario. Particular attention is paid to the selection of sensors and features. It was found that the abundant availability of features in the greenfield scenario poses pitfalls when creating ML projects if the underlying sensors cannot be checked for their suitability. For the changeover detector use case, the best model quality was achieved for the combined scenario, followed by the greenfield scenario.

Powder sheet additive manufacturing of multi-material structures: Experimental and computational characterizations

Selective laser melting (SLM) of multi-material structures (MMS) is of significance because it allows for bespoke structural innovation and high-accuracy process tailoring. However, most of the currently developed loose powder-based SLM techniques for MMS are limited by the long changeover time and potential cross-contamination between materials. To address these issues, a novel Metal Additive Manufacturing using Powder Sheets (MAPS) technique is proposed for printing MMS within a single process. It utilizes flexible powder sheets as the feedstock material, which are composed of metal powder-polymer binder composites. The printability of MMS by MAPS is assessed through the fabrication of three-phase SS304-IN718-SS304 composites with increased geometric dimensions on the SS316 baseplates. The effects of part size on the evolution of the melt-pool morphology and the formation of defects during MAPS are investigated by experimental characterizations and computational modeling. The results show that when fabricating larger MMS, the use of a longer scan-vector easily leads to defects such as lack-of-fusion porosity, balling and cracks. This is due to the longer duration of inter-hatch cooling time, the reduced amount of thermal accumulation and the higher degree of residual stresses. By adopting an island scanning strategy, a defect-free large-size MMS with variations of chemical composition, microstructure and microhardness is successfully printed by MAPS. The proposed MAPS method offers a new solution for producing high-quality MMS.

Application of genetic algorithm, GA, to solve a flow shop scheduling problem with changeover times in operations: a case study

Flow Shop Scheduling (FSS) Problems are examples of combinatorial optimization issues that are classified as NP-hard. Because of the NP-hard structure of FSS problems, it can be extremely challenging to find mathematical modeling methodologies that will result in an optimal solution for these problems. The Genetic Algorithm (GA), which is a metaheuristic approach, is one of the most important factors in the process of locating near-optimal answers to NP-hard optimization issues. In this research, a GA model for addressing an FSS problem was developed with the goal of lowering the overall weighted tardiness time and placing a constraint on the operation changeover time. When compared with the performance of the standard heuristics EDD, being used in the company under study, the GA model’s performance was shown to be superior. Based on the findings, it can be shown that the objective value was cut by 43%, going from 215.95 (h) to 123.07 (h). This demonstrates that the GA model is an effective strategy for addressing FSS problems

Testing the S-Curve Theory in OEM for Lean Operations: A Study on Organizational Transformation in the VUCA World

As the world is becoming more VUCA, only a few studies focus on exploring the plant performance and mapping its pattern in real time. The existing study shows that plant performance can be linked to an S-curve theory. This is mainly due to market turbulence, demand for continuous improvement, and the introduction of emerging technologies. Taking this idea, in this article, we focus on testing the S-curve in the lean deployment of an OEM. The study proposes integrating lean tools with decision-making techniques to achieve productivity benefits. The proposed method uses VSM to portray the changeover activities and standardize them using the SMED technique to reduce the changeover time alongside other continuous improvement suggestions. This provides real-time active solutions for decision making, prediction, and planning of resources. It also assists the industrial units in getting better insights into their productivity improvization and market evaluation trends. The study proposed an IoT-driven ecosystem for effective decision making in the OEM environment. Finally, the study is tested in an automotive OEM that has reduced changeover time by > 75% and an overall productivity increase of 27.31%. Such an ecosystem contributes to organizational transformation and promotes innovation in OEM.

Il dinamismo linguistico dell'italiano tra norma e adattamento ai mezzi di comunicazione

This article aims to describe how the pressure wielded by the means of communication, with their affordancesand techincal restrictions, has been contributing to shape not only the language usage specific to each means,but the overall current common usage of the Italian language. The grammatical norm codified by the grammarbooks, the standard variety, is incomplete with respect to the real usage of the speakers, given that it describesa variety of the language, neglecting the many lexical, syntactic, and textual possibilities existing in the usage.Speakers, in fact, select linguistic forms and constructs on the basis of different, interdependent factors and thestandard usage itself is influenced by innovative forms continually emerging from usage. Language changeover time also intertwines with the synchronic axes of variability, including the one relating to the influence ofthe channel used for communication. When the message passes through a medium, it must undergo anadaptation to its system of rules. The passage always involves a coding, which influences the form of themessage. Linguistic usage, then, is the result of an interweaving of interdependent factors, among which thegrammatical norm and the means of communication are of pivotal importance. Both of these factors imposerules, i.e. restrictions, on usage, shaping and defining it; at the same time, however, usage, by its creative andinnovative nature, constantly pushes the rules, shaping the norm and finding ways to adapt the means ofcommunication to hybrid formats. In this dynamism the language changes, it abandons worn patterns andinvents new ones, adapting itself to the world and adapting the world to itself.

Reducing Model Changeover Times in the Metal Packaging Printing Process: A Case Study

The objective of this research is to decrease the duration of model changeover times in the metal package printing process. The study involved the collection of data on the changeover times of the LP7 offset printer model, followed by the utilization of flow process charts to examine four different activities: Feeder, Printer, Coater, and Stacker. It was discovered that certain procedures experienced delays and exhibited prolonged durations. Hence, this study proposed guidelines for improvement aimed at minimizing the duration required for model alteration through the application of the Single Minute Exchange of Die (SMED) principle, achieved by transitioning internal tasks to external tasks. The application of the ECRS method involved the creation of an additional equipment to scoop ink, the use of ink duct foils to cover an ink plate, the rearrangement of work sequences, and the set of pallet shelves. The results indicated that there was a decrease in changeover times in the Printer activity, from 221.26 minutes to 167.26 minutes, resulting in a reduction of 24.41%. Similarly, in the Coater activity, the changeover times decreased from 79.45 minutes to 49.45 minutes, representing a decrease of 37.76%. Additionally, in the Stacker activity, the changeover times decreased from 55 minutes to 30 minutes, representing a reduction of 45.45% compared to the previous duration.

A comparison between a maximum care university hospital and an outpatient clinic – potential for optimization in arthroscopic workflows?

BackgroundDue to the growing economic pressure, there is an increasing interest in the optimization of operational processes within surgical operating rooms (ORs). Surgical departments are frequently dealing with limited resources, complex processes with unexpected events as well as constantly changing conditions. In order to use available resources efficiently, existing workflows and processes have to be analyzed and optimized continuously. Structural and procedural changes without prior data-driven analyses may impair the performance of the OR team and the overall efficiency of the department. The aim of this study is to develop an adaptable software toolset for surgical workflow analysis and perioperative process optimization in arthroscopic surgery.MethodsIn this study, the perioperative processes of arthroscopic interventions have been recorded and analyzed subsequently. A total of 53 arthroscopic operations were recorded at a maximum care university hospital (UH) and 66 arthroscopic operations were acquired at a special outpatient clinic (OC). The recording includes regular perioperative processes (i.a. patient positioning, skin incision, application of wound dressing) and disruptive influences on these processes (e.g. telephone calls, missing or defective instruments, etc.). For this purpose, a software tool was developed (‘s.w.an Suite Arthroscopic toolset’). Based on the data obtained, the processes of the maximum care provider and the special outpatient clinic have been analyzed in terms of performance measures (e.g. Closure-To-Incision-Time), efficiency (e.g. activity duration, OR resource utilization) as well as intra-process disturbances and then compared to one another.ResultsDespite many similar processes, the results revealed considerable differences in performance indices. The OC required significantly less time than UH for surgical preoperative (UH: 30:47 min, OC: 26:01 min) and postoperative phase (UH: 15:04 min, OC: 9:56 min) as well as changeover time (UH: 32:33 min, OC: 6:02 min). In addition, these phases result in the Closure-to-Incision-Time, which lasted longer at the UH (UH: 80:01 min, OC: 41:12 min).ConclusionThe perioperative process organization, team collaboration, and the avoidance of disruptive factors had a considerable influence on the progress of the surgeries. Furthermore, differences in terms of staffing and spatial capacities could be identified. Based on the acquired process data (such as the duration for different surgical steps or the number of interfering events) and the comparison of different arthroscopic departments, approaches for perioperative process optimization to decrease the time of work steps and reduce disruptive influences were identified.

Back End Commonality for Advanced Packaging: Large Form Factor Project

Address key gaps for advanced packages through complex assembly processes and provides flexibility for handling different body size package • Enable new products and product segments not limited by current JEDEC body sized •Demonstrates the cost effectiveness of leveraging tool commonality • Increase Backend operational efficiency • Define large form factor product handling media that allows for flexibility and higher density • Ability to maximize footprint: # of packages in held in the “handling media” • Minimize tooling changeover times • Provide the same “outer design” for many different package sizes/types, thereby minimizing NRE and Increased Tool Flexibility

Batch scheduling in a multi-purpose system with machine downtime and a multi-skilled workforce

The paper presents a discrete-time mixed-integer linear programming (MILP) model for a generalised flexible job-shop scheduling problem as represented by a state-task network. The problem is characterised by reentrant flow, sequence-dependent changeover time, machine downtime, and skilled labour requirements. Two preprocessing procedures are proposed to reduce the size of the MILP model, and represent a major contribution of the research. The procedures reduce the number of assignment variables by exploiting job precedence and workforce qualifications. Machine availability for each task is determined as a function of possible start and end times, given duration, and maintenance schedule. The overall objective is to maximise the number of scheduled tasks while minimising their total finish time. Computational experiments are conducted with real and randomly generated instances. The results show that optimal solutions can be obtained for medium-size problems within a reasonable amount of time, primarily due to the use of the preprocessing procedures.