Low-volume Production Research Articles

High-Precision Digital-to-Time Converter with High Dynamic Range for 28 nm 7-Series Xilinx FPGA and SoC Devices

Over the last ten years, the need for high-resolution time-domain digital signal production has grown exponentially. More than ever, applications call for a digital-to-time converter (DTC) that is extremely accurate and precise. Skew compensation and camera shutter operation represent just a few examples of such applications. The advantages of adopting a flexible and rapid time-to-market strategy focused on fast prototyping using programmable logic devices—such as field-programmable gate arrays (FPGAs) and system-on-chip (SoC)—have become increasingly evident. These benefits outweigh those of performance-focused yet expensive application-specific integrated circuits (ASICs). Despite the availability of various architectures, the high non-recurring engineering (NRE) costs make them unsuitable for low-volume production, especially in research or prototyping environments. To address this trend, we introduce an innovative DTC IP-Core with a resolution, also known as least significant bit (LSB), of 52 ps, compatible with all Xilinx 7-Series FPGAs and SoCs. Measurements have been performed on a low-end Artix-7 XC7A100TFTG256-2, guaranteeing a jitter lower than 50 ps r.m.s. and offering a high dynamic range up to 56 ms. With resource utilization below 1% and a dynamic power dissipation of 285 mW for our target FPGA, the design maintains excellent differential and integral nonlinearity errors (DNL/INL) of 1.19 LSB and 1.56 LSB, respectively.

Wastewater desalination for irrigation in inland regions

ABSTRACT Desalination using membrane processes is increasingly being implemented in coastal regions of the world to meet the water needs of the growing population. The use of this water treatment option to meet the growing water needs in inland regions, however, is impeded by the cost and regulatory challenges associated with responsible disposal of the concentrate brine. For inland regions of Australia, the use of evaporation ponds (EPs) is the only available brine management option. The high cost associated with construction and the high land requirements for EPs necessitates low waste brine volume production. This desktop study investigated the feasibility of achieving very high water recoveries and very low waste brine volumes in the reverse osmosis desalination of municipal wastewater for irrigation of crops near the inland town of Horsham, Victoria, Australia. Interstage lime and soda ash softening was selected as the means of removal of the scalants that prevent the achievement of high water recovery. It is hoped that this study will provide valuable information to aid in the planning of water reclamation operations for agricultural applications in inland areas at a time of increasing demand for water for food production and forecasts of population growth and climate change.

HVLV-Motor-KC: Production Efficiency of HVLV Motor Classification using K-means Clustering

This paper aims to introduce the K-means clustering algorithm to complement the Group Technology (GT) methodology as part of a multi-product, low-volume production system. This challenge aims to overcome the limitations of the GT methodology and optimize the production schedule to increase efficiency. We propose a high-variation, low-volume K-means clustering (HVLV-Motor-KC) algorithm, which is a K-means clustering algorithm that focuses on high-variety, low-volume data. This algorithm helps to optimize production by placing motors with similar characteristics in the same cluster.

A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms

In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.

Comprehensive Measurement and Simulation of Prototype Injection Moulds

The injection moulding industry is dynamically developing. The growing demand for more customizable products can be served by low or middle volume production using prototype moulds and inserts. The conventional material of prototype moulds is aluminum because of its excellent machinability, acceptable strength and stiffness and outstanding thermal conductivity. Prototype moulds are gaining ground in the injection moulding industry, yet their operational behavior (including exact mechanical and thermal process parameters) is largely unknown. We created a comprehensive state monitoring system that measures the operational strain, cavity pressure and temperature of different prototype injection moulds. This way, all important process parameters can be measured and the relations between the moulding parameters and the operational pressure loads, deformations and temperatures can be quantified and analysed.

A novel tooling-free carbon fibre reinforced polymer (CFRP) manufacturing method, double point incremental forming (DPIF) with direct electrical curing (DEC)

Carbon fibre-reinforced polymers (CFRPs) are essential in various industries due to their exceptional specific mechanical properties. However, conventional CFRP manufacturing involves significant costs related to moulds, ovens, and autoclaves, rendering it expensive for low-volume production and prototyping. This study introduces a novel method, Double-Point Incremental Forming with Direct Electric Curing (DPIF-DEC), which enables CFRP fabrication without the need for moulds, directly from CAD models, but it is not suited for mass production. This technique, enhanced by the addition of 2 wt.% carbon black to the epoxy resin matrix, improves through-thickness electrical conductivity, allowing uniform and rapid curing. DPIF-DEC demonstrates rapid localised curing, real-time process monitoring, and achieves mechanical properties comparable to traditional methods. Additionally, it reduces energy consumption, presenting a cost-effective and environmentally sustainable solution for low-volume and prototype CFRP production, laying the groundwork for future applications in continuous-fibre composite manufacturing directly from CAD models.

Advancing laser transmission welding for additive manufacturing: A study of glass fiber reinforced polypropylene parts

Laser transmission welding (LTW) is a well-established technique for joining high-volume thermoplastic parts, such as automotive injection molded parts. For low-volume, prototype, and custom production, additive manufacturing is an emerging technology for producing complex thermoplastic parts. When compared to injection molding, the additive manufacturing process fused filament fabrication (FFF) results in an inhomogeneous structure with entrapped air within the volume. Additionally, the presence of short glass fibers in the polymer matrix leads to higher radiation scattering during the welding process. This paper presents a fundamental study of the weldability of additively manufactured fiber-reinforced parts. The specimens were fabricated using a FFF process with glass fiber-reinforced polypropylene (GF-PP). The study investigates the influence of layer thickness and line width of the FFF process on the optical transmittance. LTW-experiments were conducted using additively manufactured uncolored and black GF-PP samples. Lap shear test specimens were welded with a different energy per unit length. This research presents a process for welding additively manufactured GF-PP parts that can be used with optimized FFF-parameters to produce high-strength and durable prototypes or spare parts for the automotive industry. This research provides valuable insights into the process parameters and considerations required to achieve robust welds in additively manufactured thermoplastic parts, facilitating broader adoption of LTW in additive manufacturing contexts.

Adding Rare Earth Oxide Markers to Polyoxymethylene to Improve Plastic Recycling through Tracer-Based Sorting.

Engineering plastics, such as polyoxymethylene (POM), are high-performance thermoplastics designed to withstand high temperature or mechanical stress and are used in electronic equipment, the automotive industry, construction, or specific household utensils. POM is immiscible with other plastics but due to a low volume of production, no methods were developed to separate it from the residual plastic waste stream. Therefore, POM recycling is minimal despite its high market value. This paper provides a proof of concept for tracer-based sorting (TBS) as a potential solution for increasing the separation efficiency of low-volume, high-quality polymers. For this purpose, yttrium oxide (Y2O3) and cerium (IV) oxide (CeO2) have been embedded into the POM matrix. Mechanical tests of samples at varying concentrations (0.1 to 1000 ppm) of both tracers were conducted, followed by an analysis of detectability and dispersibility using a portable X-ray fluorescence spectrometer (p-XRF), subsequently optimizing detection time and tracer concentration. Finally, an experimental scenario was developed to test the fate and potential recovery of the tracer material after the thermal treatment of plastics. A low detectable concentration, short measurement time, low influence on mechanical parameters of the compound, and low loss ratio after simulated recycling prove Y2O3 to be a suitable tracer for the industrial implementation of TBS.

Design and destructive testing of high-value hook fabricated by laser metal deposition

Laser metal deposition (LMD) provides an emerging opportunity for the economic fabrication of high-value components at low production volume. Despite the technical and commercial opportunities associated with LMD, there exist potential failure-modes that differ from those typical of traditional manufacture; concurrently, LMD is typically applied to high-value components associated with a high consequence of failure. This report contributes to the emerging literature on LMD component design and failure analysis by documenting the design and destructive testing of a high-value tensile loaded hook component, including numerical structural simulation, manufacture characterisation, microstructural analysis and instrumented destructive testing. This systematic design contributes to the understanding of LMD design for structural integrity and supports the application of LMD as a robust commercial additive technology.

Monitoring model for enhancing adaptability in human–robot collaborative mold assembly

ABSTRACT Molds are assembled manually due to their inherent characteristics of low-volume and high-variety production. Given the ergonomic risks caused by heavy-handling and repetitive tasks and diverse requirements in mold assembly, collaborative robots offer adaptability and ease of reconfiguration, making them potential solutions to these challenges. This study introduces a monitoring model for human–robot collaborative mold assembly using two cobots. This model encompasses manual progress monitoring for cobot execution and position-sharing modules. Manual task actions are detected using the You-Only-Look-Once-v8 Nano model. Detected actions are subsequently classified into different states. These identified states are relayed to the cobots, enabling early execution and controlling the cobots’ entry into the assembly area. This study proposes a position-sharing approach to prevent collisions by receiving coordinates via Modbus between the two cobots. Most existing research has developed separate models for action and part recognition, excluding the utilization of recognition results to enable early execution. This study contributes to a novel model for monitoring manual progress to enable early execution of subsequent tasks and facilitate communication through position sharing during human–robot collaborative mold assembly tasks. The results show that assembly time and the risk of collisions between cobots can be reduced in human–robot collaborative mold assembly tasks.

Design and Development of a Flexible Manufacturing Cell Controller Using an Open-Source Communication Protocol for Interoperability

Flexible manufacturing cells provide significant advantages in low-volume mass-customization production but also induce added complexity and technical challenges in terms of integration, control, and extensibility. The variety of closed-source industrial protocols, the heterogeneous equipment, and the product’s manufacturing specifications are main points of consideration in the development of such a system. This study aims to describe the approach, from concept to implementation, for the development of the controller for a flexible manufacturing cell consisting of heterogeneous equipment in terms of functions and communication interfaces. Emphasis is put on the considerations and challenges for effective integration, extensibility, and interoperability. Scheduling and monitoring performed by the developed controller are demonstrated for a manufacturing cell producing microfluidic devices (bioMEMS) that consists of six workstations and a robot-based handling system. Communication between the system controller and the workstations was based on open-source technologies instead of proprietary software and protocols, to support interoperability and, to a considerable extent, code reusability.

Specific Effect of Innovation Factors on Socioeconomic Development of Countries in View of the Global Crisis

Although the coronavirus pandemic has now faded into the background, the global crisis caused by COVID-19 has had the most devastating impacts worldwide. Given the potential relapse of such unexpected and uncertain events, it is vital to specify the patterns thereof and develop proactive measures for the countries to acquire an advanced readiness to deal with the related incidents. The most infected countries faced an increase in business bankruptcies, unemployment and inflation rates, low production volumes, and a decline in Gross Domestic Product (GDP). To withstand such socioeconomic consequences, the countries had to employ a number of measures, with innovation development acceleration being one. This paper aims to assess the dependency of an increase in GDP and a decrease in inflation and unemployment rates on the country-level growth of innovation development according to such Global Innovation Index (GII) pillars as institutions, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs. The conducted research analysis covered the period from 2019 to 2022 based on the data for the GII pillar development level and economic performance indicators for 20 countries from five socioeconomic models. Descriptive and comparative statistics as well as correlation and regression analysis were used to prove the innovation development to be a key driver in increasing GDP and reducing inflation. To increase the GDP value, special attention should be paid to such GII pillars as institutions and human capital and research, while infrastructure and human capital and research are the pillars to reduce the inflation rates.

Gelatin Methacryloyl (GelMA) - 45S5 Bioactive Glass (BG) Composites for Bone Tissue Engineering: 3D Extrusion Printability and Cytocompatibility Assessment Using Human Osteoblasts.

3D extrusion printing has been widely investigated for low-volume production of complex-shaped scaffolds for tissue regeneration. Gelatin methacryloyl (GelMA) is used as a baseline material for the synthesis of biomaterial inks, often with organic/inorganic fillers, to obtain a balance between good printability and biophysical properties. The present study demonstrates how 45S5 bioactive glass (BG) addition and GelMA concentrations can be tailored to develop GelMA composite scaffolds with good printability and buildability. The experimental results suggest that 45S5 BG addition consistently decreases the compression stiffness, irrespective of GelMA concentration, albeit within 20% of the baseline scaffold (without 45S5 BG). The optimal addition of 2 wt % 45S5 BG in 7.5 wt % GelMA was demonstrated to provide the best combination of printability and buildability in the 3D extrusion printing route. The degradation decreases and the swelling kinetics increases with 45S5 BG addition, irrespective of GelMA concentration. Importantly, the dissolution in simulated body fluid over 3 weeks clearly promoted the nucleation and growth of crystalline calcium phosphate particles, indicating the potential of GelMA-45S5 BG to promote biomineralization. The cytocompatibility assessment using human osteoblasts could demonstrate uncompromised cell proliferation or osteogenic marker expression over 21 days in culture for 3D printable 7.5 wt % GelMA -2 wt % 45S5 BG scaffolds when compared to 7.5 wt % GelMA. The results thus encourage further investigations of the GelMA/45S5 BG composite system for bone tissue engineering applications.

Positioning accuracy improvement for target point tracking of robots based on Extended Kalman Filter with an optical tracking system

Although industrial robots have been successfully used in a wide spectrum of applications for production automation, they still face challenges for many high precision tasks especially in low-volume high-mix production due to their low absolute positioning accuracy. To respond to such rapidly changing production tasks, an efficient means is required to determine the pose relationship between the robot and the workpiece without human intervention such as teaching the robot. For this purpose, the paper proposes the use of the Extended Kalman Filter (EKF) with an optical tracking system to improve the robot positioning accuracy with a particular focus on the target point tracking of the end-of-arm tool, which is an essential part for many robotic tasks. To this end, a comprehensive kinematic error model is first derived for the end-of-arm tool that accounts for the errors in the Denavit-Hartenberg (D-H) parameters, the positioning errors of the robot base and the end-of-arm tool installation. Then, by using the optical tracking system, the pose of the end-of-arm tool relative to the workpiece can be determined in an efficient way. Based on the EKF algorithm, the kinematic parameter errors of the system can be estimated online to compensate the positioning error of the target point during the robot movement. Simulation and experimental tests have been performed to demonstrate the effectiveness of the proposed method. The proposed approach utilizes the given trajectory to design a compensation scheme where the kinematic parameter errors of the robot are estimated during the motion and then the positioning error of the end-of-arm tool is compensated at the target point. As a result, this approach can improve the target point accuracy of the robot without continuous feedback to reduce the tracking error along the trajectory in real time. It is easy to implement and suitable for low-volume, high-mix scenarios to determine the pose relationship between the robot and the workpiece without human intervention.

Study on the Effect of Recycled Brass-Filled Epoxy Mould Inserts for Rapid Tooling

Rapid Tooling able to produce complex prototypes directly from three-dimensional CAD software using materials such as polymer, wax, and paper, but it is typically used for low-volume production. The current technology uses epoxy filled with metal fillers such as aluminum or copper to enhance the mechanical properties of rapid tooling molds. This study aims to investigate the effect of using recycled brass filler mixed with epoxy resin as mold inserts for Rapid Tooling in injection molding applications. An optimal ratio of brass filler particles will be evaluated to determine the best physical and thermal properties for the mold inserts. Significantly, this study will encourage the use of recycled materials such as metal waste from machining to offer great help in environmental sustainability.

Toward Data-Driven and Multi-Scale Modeling for Material Flow Simulation: Characteristic Analysis of Modeling Methods

ABSTRACT Material flow simulation is a powerful tool to realize efficient operations in complicated production systems such as high-mix and low-volume production. Nevertheless, great effort and expertise are necessary to construct accurate simulation models. We have proposed a semi-automatic modeling approach designated as data-driven and multi-scale modeling. The approach combines various modeling methods to maximize the simulation accuracy. This article introduces the proposed method and presents the experimentally obtained results for simple production systems to examine the characteristics of modeling methods based on queue models or machine learning models. The results of computational experiments indicate that the superiority and inferiority of modeling methods depend on the complexity of the system and the background knowledge about the activity configuration in the system.

A robust set of qPCR methods to evaluate adulteration in major spices and herbs

Consumers and companies associated with food or pharmaceuticals rely on spices and herbs in various forms. Their intricate supply chains, elevated prices, and low-volume production render them vulnerable to fraudulent practices. However, comprehensive methodologies to detect adulterants remain scarce, impeding national control laboratories from enforcing European and national legislation. In this study, we present quantitative real-time PCR (qPCR) methods designed to identify the top five adulterants of each of six commonly consumed spices and herbs: paprika/chili, turmeric, saffron, cumin, oregano and black pepper. The specificity of each method was confirmed by qPCR analysis of a large collection of relevant plant species. Each authentic sample was combined with its respective top five adulterants as identified in the European Union-wide coordinated control plan on herbs and spices in 2021 or in the existing literature. These binary mixtures were used to evaluate the method's performance with respect to sensitivity, linearity and trueness at four levels of adulterants concentration. Detection was also investigated in multi-adulterated samples. These SYBR™ Green-based qPCR methods enable the specific detection of adulterants, and their sensitivity allows for the distinction between inadvertent contamination and deliberate adulteration. Altogether, these methods contribute to safeguard the authenticity of these high-value commodities.

Inline CT-Analysis of the Melting Area during Fused Filament Fabrication

In the 21st century, 3D printing has become a mainstream process for prototyping and low-volume production. Fused Filament Fabrication is the most commonly used process for 3D printing plastics. A plastic filament is melted in a nozzle and a component is built up layer by layer. As with all manufacturing processes, there is an interest in continuously optimizing and improving the FFF process. One approach is based on process simulation, which provides a better understanding of the process. Subsequently, it is always necessary to validate the simulation models with the real process. This validation has only been possible to a limited extent in the case of FFF. In this work, a method is presented that allows a non-destructive investigation of the melt behaviour during the printing process. For this purpose, a 3D printer nozzle with an extruder was integrated into an X-ray computed tomography system. Thus, a computed tomography scan can be performed during the extrusion process. By using highly absorbent tungsten filaments, sufficient contrast can be created between the metal nozzle and the plastic filament to allow analysis of the melt behaviour. This setup makes it possible to distinguish between the solid filament area and the melt area, as well as to determine the contact between the filament and the die wall. In doing so, simulations can be validated and nozzle geometries can be improved.

Escalation System for Handling and Analyzing Production Disturbances

In a manufacturing environment the effectiveness of internal problem solving is a key success factor in the service level performance offered to the customers. Broken internal processes need to be fixed immediately or at least in a very short time, in order to fulfill the committed delivery dates. There are several methodologies applied for internal problem solving used by different companies. This article presents a solution worked out for a plant operating in a high-mix low-volume (HMLV) production environment prone to both internal and external disruptions and disturbances. Principles, architecture and information flow in our digitized disruption handling – so-called escalation – system will be shortly discussed. Lessons learned in a six-year’s period of using the system will also be summarized. The recorded data confirm that with the introduction of the escalation system the capability of the plant to adapt to changing circumstances and disruptions greatly improved.

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