Scrap Reduction Research Articles

Recent Advancements towards Sustainability in Rotomoulding.

Rotational moulding is a unique low-shear process used to manufacture hollow parts. The process is an excellent process method for batch processing, minimal waste and stress-free parts. However, the process has drawbacks such as long cycle times, gas dependency and a limited palette of materials relative to other process methods. This review aimed to shed light on the current state-of-the-art research contributing towards sustainability in rotational moulding. The scope of this review broadly assessed all areas of the process such as material development, process adaptations and development, modelling, simulation and contributions towards applications carving a more sustainable society. The PRISMA literature review method was adopted, finding that the majority of publications focus on material development, specifically on the use of waste, fillers, fibres and composites as a way to improve sustainability. Significant focus on biocomposites and natural fibres highlighted the strong research interest, while recyclate studies appeared to be less explored to date. Other research paths are process modification, modelling and simulation, motivated to increase energy efficiency, reduction in scrap and attempts to reduce cycle time with models. An emerging research interest in rotational moulding is the contribution towards the hydrogen economy, particularly type IV hydrogen vessels.

A quantitative framework to support the decision between traditional, selective, and hybrid assembly

High-technological products often consist of several components, each with varying feature values that are critical to the assembly’s quality. As traditional manufacturing involves random selection of components, strict tolerance ranges are typically set to limit their feature variability, increasing scrap rates and lowering material efficiency. By smartly combining components based on their measured features, selective and hybrid assembly allow for more relaxed tolerances and more efficient use of raw materials without affecting the final product’s quality. However, the increased logistical complexity involves additional operational costs, complicating the choice for the best strategy.This study presents a decision-making model for selecting from traditional, selective, and hybrid assembly. The model is formulated by first defining the cost functions for the corresponding expected assembly cost, and then analytically determining the optimal choice boundaries between the three methods based on the involved process parameters. Estimation methods for the instance-specific process parameters are then discussed before validating the model with a fully elaborated case study.Results confirm the need for a quantitative framework to support an informed choice as this strongly depends on the involved parameter values. This study aims to pave the way towards more sustainable manufacturing by supporting practitioners to identify the conditions whereby intelligent assembly methods, in addition to allowing for scrap reduction and increased material efficiency, also represent the most cost-effective option.

Evaluation of Permeability Models for Foundry Molds and Cores in Sand Casting Processes

Predicting the permeability of different regions of foundry cores and molds with complex geometries will help control the regional outgassing, enabling better defect prediction in castings. In this work, foundry cores prepared with different bulk properties were characterized using X-ray microtomography, and the obtained images were analyzed to study all relevant grain and pore parameters, including but not limited to the specific surface area, specific internal volume, and tortuosity. The obtained microstructural parameters were incorporated into prevalent models used to predict the fluid flow through porous media, and their accuracy is compared with respect to experimentally measured permeability. The original Kozeny model was identified as the most suitable model to predict the permeability of sand molds. Although the model predicts permeability well, the input parameters are laborious to measure. Hence, a methodology for replacing the pore diameter and tortuosity with simple process parameters is proposed. This modified version of the original Kozeny model helps predict permeability of foundry molds and cores at different regions resulting in better defect prediction and eventual scrap reduction.

Developing a concept for the implementation of predictive quality in battery production

The supply of batteries plays a crucial role in the electrification of the automotive industry and thereby in reducing CO2 emissions. With strict regulations forcing the ban of internal combustion engines and the reduction of emissions, the demand for batteries has risen drastically. In current battery productions, planned production volumes are in most cases not reached due to high scrap rates. Reasons for this lay in the lack of predictability of product quality due to process complexity and interdependencies both on the process level and on the parameter level. The reduction of scrap through data-driven methods in battery production offers the potential to increase production volumes addressing the global demand and to preserve energy and materials. Tackling the challenge of high scrap rates, existing and conventional methods in quality management lack the applicability in complex processes and the predictability of product quality. Predictive quality describes an industry 4.0 application and the approach to use production data to predict process and product related quality characteristics. In this paper, a concept for the technical implementation of predictive quality in battery production is developed. For this, an overarching concept is outlined and based on this the technical infrastructure described. The concept highlights the required data collection as well as data processing and addresses the development of prediction models. By applying the developed concept, the deployment of quality prediction models and the detection and compensation of scrap at an early stage is enabled.

Energy-efficient scheduling for a flexible job shop problem considering rework processes and new job arrival

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

An integrated framework for improving safety, quality, and stewardship standards in manufacturing: A case study

Health and safety, quality assurance, and stewardship control, when integrated together, can streamline systems and work processes, reduce bureaucracy and increase efficiency. In this study, efficiency increase refers to improving and maintaining high standards of demarcation, housekeeping, no blocked safety equipment, tools storage conditions, good manufacturing practices and chemical storage conditions. Using industrial engineering tools, Plan-Do-Check-Act and Kaizen, the study illustrates a quick turnaround in standards improvement in a manufacturing plant, which resulted in culture change and increasing profitability. The manufacturing plant studied had below average standards that created health and safety hazards leading to incidents, created a risk of quality and stewardship systems failure. Incidents varied from minor to serious. With improved standards, the plant avoided and eliminated health amd safety, quality, and stewardship risks in critical areas such as production or operations floor, warehouse, laboratory, storeroom, utilities area, and workshop. This was achieved through the integration of health and safety, quality assurance and stewardship controls as one safety, quality, and stewardship (SQS) framework. The three are denoted as fundamentals and core foundation systems in a manufacturing environment in this study. An SQS tool was developed, tested, and implemented and results are shared. Implementation of the tool resulted in improvement of overall manufacturing plant standards, elimination of safety hazards, reduction of quality defects and scrap, eradication of internal controls issues, and business success. The study will be of great value for manufacturing plants who are looking to integrate more than two management systems.

Potential of prediction in manufacturing process and inspection sequences for scrap reduction

With increasing quality requirements, especially for safety-critical products such as in medical technology, manufacturing companies face the challenge, that the quality inspection of component characteristics may become a crucial factor for economic production. For increasing the ratio of correctly identified scrap without increasing the amount of physical inspection, prediction models pose a valuable tool. Prediction models in manufacturing build process models through process data collected during the manufacturing process. The prediction models can for example be used to predict the product quality from the process data, which enables the substitution of physical quality inspections through virtual inspections. Before selecting suitable prediction models, checkpoints in the manufacturing process and inspection sequence (MPIS) for integrating the prediction models profitably have to be identified. Therefore, the paper aims to provide a methodology to generate integrated quality strategies for MPIS that combine physical inspection with the prediction of quality characteristics. Furthermore, a methodology for the economic evaluation of the quality strategies as decision support is provided. Since the number of prediction models available for predicting quality based on process data is manifold, the explicit recommendation for the implementation of a certain prediction model is not the focus of this paper. The methodology presented rather focuses on the identification of suitable checkpoints in the MPIS for prediction model integration. For generating quality strategies suitable checkpoints in the manufacturing process sequence have to be identified. Therefore, three aspects are considered, the influence of the manufacturing process on the change in a characteristic and the overall importance of a characteristic for the component function as well as the ability of the characteristic and process for being predicted. A prediction recommendation is calculated that enables the derivation of quality strategies as a combination of physical and virtual inspection for different characteristics in different checkpoints. For the economic evaluation of the derived quality strategies two cases for supplementing physical inspection with prediction models to either compensate for the alpha- or beta-error rate are presented. The most reasonable quality strategy is chosen according to its economic potential. For validation, the applicability of the methodology for integrating prediction models in manufacturing process and inspection sequences to a use case of medical technology is demonstrated successfully. It is shown that the methodology supports users in identifying possible quality strategies and their economic evaluation for manufacturing process and inspection sequences as a basis for the decision regarding quality strategies to be implemented.

Inquiry into the Effectiveness of Eight Discipline-Based Problem-Solving

Purpose: The eight discipline (8D) report is a method for solving problems in industry and is based on an eight-step problem-solving process and is often called an 8D report. The 8D report has been covered in the literature; however, much of the literature on 8D reports only describes the use of 8D reports and fails to evaluate 8D reports empirically. The purpose of this research is to determine if the use of 8D reports combined with training within an organization leads to finding root causes more quickly. Methodology/Approach: The research uses a mixed-methods approach. A case study describes the implementation of 8D reports in a manufacturing organization in the automotive industry. The paper then looks at five years’ worth of data after the implementation of an 8D-based process for addressing quality problems and determined if there is a significant difference in the time to find root causes after the implementation of 8D reports combined with training. Findings: The research found that the time to solve problems was reduced between the early and later years after the implementation of an 8D process and related training. Faster problem resolution means resources can be redirected elsewhere, as well as a reduction in scrap or rework. Research Limitation/Implication: The paper only looked at the use of 8D reports in the case study organization. Originality/Value of paper: This paper provides evidence supporting the effectiveness of 8D reports as an approach to problem-solving. The case study aspect also provides guidance for managers seeking to implement 8D reports in their organizations.

Hot profiled rolling of C40 steel: production variable’s effect on rolling performance

ABSTRACT The usage of C40 steel bars is common in ships, mechanical, metallurgical, automobile, military hardware, and electrical construction fields. During manufacturing of these bars, roll force (RF), roll torque (RT), cropping loss (CL) and drive energy (DE) are important production variables that must be monitored for high-quality output, scrap reduction, and mill safety with minimum consumption of energy. Roll RPM, temperature of bloom, roll gap, bloom cross-section area, and diameter of roll are process characteristics that influence these response parameters. The impact of process variables on response variables during C40 steel rolling is probed in this research. To replicate the rolling procedure, FORGE® NxT 1.1 was employed. Following statistical testing, the simulated findings were validated utilizing data from experiments acquired in a bar rolling plant. The results of an analysis of variance were used to find significant model terms. There have been constructed regression models demonstrating the link between the response parameters and the process parameters.

AN APPROACH FOR THE INCLUSION OF LOADING CONDITIONS IN A POLYHEDRAL-BASED METHOD FOR EARLY VARIATION MANAGEMENT

AbstractThe variation management and product quality processes are important tasks to guarantee the assemblability of the systems, the scrap reduction and to avoid delays on production and launching. The compound of activities in Geometric Dimensioning and Tolerancing (GD&T) are necessary, especially in the early design stages, to take into account variations of different nature and from different sources. In this paper, an approach for considering the loading conditions in a polyhedral-based approach in tolerancing design is presented. The load boundary conditions are represented as additional displacement restrictions in the deviation space. The restrictions imposed by the physical limits of a system, the ones coming from the loading conditions and the degrees of freedom (DoF) can be all described and represented with a single polyhedron operand. The approach is illustrated using a simplified 2-D model for both ideal and non-ideal geometry. A 3-D model describing an unilateral contact is presented as a case study using Skin Model Shapes. By taking into account geometrical form defects, external loads, and the kinematics of the system, its sensitivity to variations can be reduced even from early design stages.

English

The production of green sand systems in iron foundries can be improved by controlling batch-to-batch mulling energy instead of controlling the mulling time. In this study, sand data from 4 foundries were evaluated before and after the transition from conventional mull-to-time (MTT) to mull-to-energy stable power mulling strategies (MTESP). Unnecessary over-mulling was eliminated with MTESP strategies, which significantly brought down the time by 75%, thus increasing the mulling output. In the process, sand properties were not adversely impacted. However, in terms of scrap, there was a 50% reduction in sand-related scrap after MTESP implementation. This paper evaluates green sand system changes during production foundry transitions from MTT strategies to MTESP strategies to significantly reduce the time, power and cost of mulling. In addition, control strategies to identify the point of stable power during mulling have been evaluated.

An investigation towards identifying and analysing the failure factors for the implementation of scrap reduction techniques in Indian SMEs

Manufacturing organisations are implementing a number of scrap reduction (SR) techniques in their organisations for the purpose of making improvements in the processes. Some of such techniques are lean manufacturing, Six Sigma, Lean Six Sigma, total quality management, etc. In many cases the implementation of SR technique is found successful, whereas at the same time in many cases it found to be a failure one as well. There are a certain number of factors which are found to be responsible for the failure of SR. Present study concentrates towards the identification of critical failure factors contributing towards the failure of implementation of SR techniques in the manufacturing organisations. Total failure mode and effects analysis (TFMEA) and FMEA has also been done on the identified critical failure factors of SR techniques.

An Investigation towards identifying and analysing the failure factors for the implementation of scrap reduction techniques in Indian SME’s

An Investigation towards identifying and analysing the failure factors for the implementation of scrap reduction techniques in Indian SME’s

Estimation of critical success factors of Lean Six Sigma for scrap reduction and quality improvement in manufacturing SMEs

Estimation of critical success factors of Lean Six Sigma for scrap reduction and quality improvement in manufacturing SMEs

A data analytics model for improving process control in flexible manufacturing cells

With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology based on data analytics that simplifies the monitoring process for the operator, allowing the practitioner to concentrate on the relevant issues, anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine’s settings are needed (tool replacement, repositioning the axis, etc.). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.

Reduction of lead time in an automobile rubber components manufacturing process through value stream mapping

Increasing demand for cost reduction triggers an organisation to focus on optimisation of all the resources and efficiency improvement. Value stream mapping (VSM) finds application in the manufacturing process for achieving this result. VSM is one of the lean manufacturing tools for the identification and reduction of waste in an organisation. This article provides a case study on application of VSM in a automotive rubber parts manufacturing industry where oil seal is produced and supplied to vehicle manufacturers. Purpose of this research is collecting the performance data of the organisation, analyse the gap, apply VSM technique and measure the improved performance. Conceptual framework developed for employees to understand the overall concept and for future researcher to directly apply relevant leans tools for particular problems. As a result of the implementation of VSM, there is a reduction in lead time and waste by increasing the number of cavities from 30 to 36 in curing machine, providing interlock system in curing machine, introducing 3.7 m3 oven, auto trimming machine, magnetic flash separator, auto spring fitting machine, modification of inspection table and packing stacker in the manufacturing process. After implementation there was 81% reduction in lead time, 38% reduction in cycle time, 70% reduction in setup changeover time, 91% reduction in work in process (WIP) inventory and 90% reduction in scrap leading to 69.6% increase in process cycle efficiency by reducing breakdown, setup changeover time and implementing kanban system. Results indicates that implementation of lean tools improves the performance of the organisation.

In-line Sensor-based Process Control of the Calendering Process for Lithium-Ion Batteries

The calendering process is the final property-defining step in the manufacturing of electrodes for lithium-ion batteries. Calendering significantly affects mechanical properties by compacting the electrodes and increases the lithium-ion batteries’ volumetric energy density. The choice of calendering parameters is primarily based on empirical data, considering the final battery cell properties. The drive for increased energy densities and the corresponding high compaction rates results in an increased electrode defect and scrap occurrence. This leads to challenges in process control which are addressed in this paper by developing an agile and predictive sensor-based process control. Hereby an efficient determination of process parameters is enabled, resulting in a reduction of scrap and manufacturing costs. Applying in-line data acquisition for electrode thickness and surface topography allows real-time feedback of measurement data at the machine-product interface back into the calendering system control. Reliable data acquisition is established by integrating two sensor systems for coating thickness measurement (confocal sensors) and defect detection (laser triangulation sensor) in a cascaded arrangement, enabling real-time evaluation of the product properties achieved as a function of the process parameters. The associated potentials and challenges regarding a predictive analysis of machine and product behavior are addressed in this context. The experimental results demonstrate that the combination of both sensor systems offers the potential to establish an agile and innovative process control for the calendering process of lithium-ion batteries.

Implementation of Lean Six Sigma model for scrap reduction in machining sector

Implementation of Lean Six Sigma model for scrap reduction in machining sector

Glass Die Ink Marking Media Selection for a Robust Glass Attach Process

Glass material used on a semiconductor device for isolating currents are one of the new breakthroughs of the modern world. Challenges are inevitable due to its complex characteristics and unique appearance. The study focuses on the phenomenon of reject glass die unrecognized, picked and bonded by die attach machine on good units of the semiconductor quad-flat no-leads (QFN) device in focus. This QFN device utilizes glass die as interposer on two active dice that separates the dielectric current of each die. During die attach process, machine photo recognition system failed to recognize and detect the glass die reject marking due to its unique transparent design and will be attached on good units. Thus, resulting to gross rejection and low process yield. Practical solutions to prevent the said phenomenon are simulated and determined by performing selection of variables like the contrast of the reject mark related to the product structure and compatibility through statistical analysis. The improvement drives to promote process robustness and scrap reduction that will help the manufacturing to be competitive through innovative resolutions on problems.

Estimation of Critical Success Factors of Lean Six Sigma for Scrap reduction and quality improvement in Manufacturing SME’s

Estimation of Critical Success Factors of Lean Six Sigma for Scrap reduction and quality improvement in Manufacturing SME’s