Low-volume Manufacturing Research Articles

Characterisation and manufacturing methods of material extrusion 3D printing composite filaments based on polylactide and nanohydroxyapatite

As the pace of scientific research and technological advancement accelerates, there is an increasing demand for rapid on-site prototyping with specific materials. Additive manufacturing through material extrusion 3D printing has emerged as an economically viable solution to this need. The production of custom filaments for this process frequently depends on low-volume filament manufacturing lines. A significant challenge in such production processes is maintaining the quality of the filament throughout its entire length, as this has a considerable impact on the quality of the final printed object. To ensure consistent quality, it is essential to monitor and manage a range of essential properties, including diameter consistency, material homogeneity and compound ratio. This study presents the implementation of a real-time filament quality monitor (R-FQM), which provides detailed insight into the longitudinal characteristics of the filament during the manufacturing process and its crystallinity analysis. In conjunction with a new proof-of-concept drawing process for reducing filament diameter by multi stage drawing die solid-state extrusion, this approach addresses the challenge of maintaining filaments quality. Several PLA-based filaments were produced for research purposes: pure PLA and composites containing 5 and 10 wt% nanohydroxyapatite. Produced composite filaments had significantly greater diameter variation compared to pure PLA (largest for 5 % filler ± 0.29 mm). The diameter of the resulting filaments was reduced in the range of 2.5–1.4 mm using the SSE approach. In addition to the R-FQM method, the filaments were characterised by structural, thermal and mechanical property analysis. The analyses showed that the 10 % hydroxyapatite content in the filament subjected to the SSE process can lead to significant weakening of the material. However, all the filaments were suitable for 3D printing for biomedical applications. The research carried out demonstrated the complementarity of the R-FQM method with classical methods, confirming the effectiveness of the approach described.

Flexible and robust detection for assembly automation with YOLOv5: a case study on HMLV manufacturing line

AbstractAutomating assembly processes in High-Mix, Low Volume (HMLV) manufacturing remains challenging, especially for Small and Medium-sized Enterprises (SMEs). Consequently, many companies still rely on a significant amount of manual operations with an overall low degree of automation. The emergence of artificial intelligence-based algorithms offers potential solutions, enabling assembly automation compatible with multiple products and maintaining overall production flexibility. This paper investigates the application of the YOLO (You Only Look Once) object detection algorithm in an HMLV production line within an SME. The performance of the algorithm was tested for different cases, namely, (a) on different products having similar product features, (b) on completely new products, and (c) under different lighting conditions. The algorithm achieved precision and recall greater than 98$$\%$$ % and mAP50:95 greater than 97$$\%$$ % .

Automatic grasp planning for self-adjustable gripper frames

In high-mix, low-volume manufacturing, end-of-arm tooling (EOAT) must be able to handle various products. Conventional EOATs usually comprise fixed or limited-adjustable frames (i.e. manually or using short-stroke pistons) over-equipped with multiple grippers, which are selectively activated based on the product to be grasped. This paper presents a smart gripper frame equipped with only four grippers capable of automatically adjusting to a product’s unique geometry. To this end, a two-dimensional grasp planner has been developed that is supplied with product contours from depth images. The proposed approach has been successfully validated in multiple industrial use cases involving objects with different dimensions and materials, and applying various grippers.

Customizing Management Strategies for Product Introduction in Low-Volume Manufacturing: Enhancing Information Content Quality

Manufacturing companies characterized by high-mix and low-volume production exhibit distinct features that exert a significant influence on the product introduction process. The quality of information exchange between product design and production interfaces becomes paramount in low-volume manufacturing. Consequently, there is a pressing need to explore various methods for adapting and customizing management strategies to align with the unique characteristics of low-volume manufacturing and its associated product introduction processes. Although this knowledge is critical in low-volume manufacturing, most existing studies focus on the management aspects of high-volume manufacturing. Therefore, this study investigated the customization of management strategies for product introduction in low-volume manufacturing to enhance the quality of information content. Drawing upon a longitudinal analysis of new product introductions within a low-volume manufacturing company, this study identified four management strategies—namely, securing a production test and verification plan, revising the role of product introduction management, developing a formal design and production coordination plan, and developing lessons learned management plan. The paper explains how the examined company adapted and implemented product introduction management strategies to facilitate the enhancement of information content quality across three key stages: pre-implementation, post-implementation, and subsequent modification and refinement of strategies following the initial round of implementation. By shedding light on these strategies, this study offers a comprehensive understanding of management approaches for product introduction in low-volume manufacturing. The study makes a valuable contribution to the discourse on information quality management and design-production interface in the low-volume manufacturing and low-volume product introduction literature.

Artificial Intelligence in planning and control tasks: a study of potential use cases and perceived challenges in Austrian make-to-order companies

An increasing number of manufacturing companies in Austria are operating in market niches. These companies are characterized by a high degree of specialization, particularly in manufacturing technologies, as well as by a high degree of flexibility. Production of these kinds are often focused on single-piece-production or the production of small batches (“make-to-order”). Flexibility enables companies to address individual customer requirements and therefore gain an advantage over companies in high-volume manufacturing. Hence, flexibility, high-mix and low-volume manufacturing usually comes with high costs as planning and control of manufacturing processes becomes more complex, leading to less utilized machines and personnel, long lead and setup times and high material costs. This effect is reinforced by the general lack of skilled workers in Europe. Small and medium enterprises (SMEs) are especially challenged by these circumstances, although these challenges are not exclusively affecting SMEs. Methods from the field of artificial intelligence have the potential to support complex planning and control tasks. In order to find use-cases that are relevant for SMEs in Austria, this study selected several SMEs that are focused on make-to-order production to explore the specific challenges that companies operating in that field are facing. Based on the findings of the study, continuing research on application of artificial intelligence in production planning and control can be directed more targeted at the challenges faced by SMEs with make-to-order production.

Capturing the system requirements for adopting Industry 5.0 in quality inspection: an evidence-based approach

Manufacturers face the challenge of adopting digital solutions to maintain competitiveness in the era of flexible manufacturing. The human remains a crucial asset in ensuring this flexibility. Industry 5.0 strongly emphasizes human-centric approaches to achieve sustainability and resilience in the industry. Notably, in-line quality inspection stands out among the assembly operations to minimize waste and ensure quality. With the increasing trend toward high-mix low-volume manufacturing, the reliance on operators’ traditional tools and cognitive skills for quality inspections is no longer sufficient. Complexity escalates when manual assemblies involve components of varying sizes. Technologies such as augmented reality and computer vision can potentially support quality inspection in manual assembly. However, successful implementation depends on comprehending the specific requirements of the end users. We propose a qualitative approach to refining the requirements in applied research projects that aim to implement human-centered technologies. The approach was applied in a research project for in-line quality inspection with 18 professionals from two manufacturing companies. We instantiated the method using qualitative methods including questionnaires and focus groups with diverse stakeholders with demonstrations. We further investigated the verification and implementation of our method.

An experimental investigation of Lean Six Sigma philosophies in a high-mix low-volume manufacturing environment.

This article experimentally examines methods for implementing the philosophies of Lean Six Sigma (LSS) in a High-Mix Low-Volume (HMLV) manufacturing environment. HMLV environments present unique challenges to LSS paradigms because of the need for extraordinary operational flexibility and customer responsiveness. The subject HMLV manufacturer for this experimentation manufactures (among 8500 others) an example component for which 3 machines work independently to perform the necessary operations to manufacture this component. The experiment that is the subject of this research seeks to adapt LSS philosophies to develop treatments to improve the performance of the manufacturing of this component. These LSS-inspired treatments included 1) using cellular manufacturing methods, and the 3 machines as a single work cell to manufacture the component, and 2) using a single multipurpose machine to perform all operations required to manufacture the component. The results of this experiment demonstrate that the cellular manufacturing method was the most effective to reduce costs, to standardize operations at a process level, and to increase throughput. The single machine processing method improved production rates and on-time delivery relative to the baseline, but greatly increased lead time, thereby increasing total cost per part. These results highlight the importance of critically assessing the application of LSS within HMLV environments compared to the Low-Mix High-Volume (LMHV) environments where LSS is traditionally successful. HMLV manufacturers and researchers can use these findings to identify the most effective methods for their specific needs and to design interventions that will improve system-level manufacturing performance in high mix environments.

Towards ultra-flexibility: a framework for evaluating the cyber-physical continuum in flexible production systems

Flexibility is often cited as a desirable key characteristic of modern production systems. In ultra-flexible production, machinery and layouts are in a constant state of adaptation to accommodate changing orders, varying products, or evolving conditions. Cyber-physical integration has been proposed as a potential approach to increasing system flexibility with Cyber-Physical Production Systems (CPPS) and Digital Twins (DT) as central concepts. While numerous architectures, frameworks and approaches have been proposed for CPPS and DT development, further research is motivated regarding the development of a requirement-based framework that links together the high-level system property of flexibility and lower-level system components, enabling the analysis, prescription and comparison of systems. Such a framework could enable manufacturers to continuously evaluate and improve manufacturing systems’ flexibility as well as make informed design decisions. Ultimately enhancing system flexibility and responsiveness to changing production conditions. This study aims to initiate the development and formulation of such a requirements-based framework linking flexibility and lower-level system components. Additionally, it seeks to introduce the concept of a ”cyber-physical continuum,” which the study aims to define as a potential quantifiable indicator reflecting flexibility within production systems. This is achieved by leveraging prior CPPS research based on high-level system requirements. These requirements were expanded by branching each requirement into lower-level components creating a more granular scope and providing a finer lens for analysis and assessment. The framework was then applied to assess a high-mix, low-volume manufacturing scenario. Application of the preliminary framework in the case study indicates its potential utility in providing a useful view of the cyber-physical content of a system. Moreover, it serves as a valuable guide for pinpointing areas for improvement and development. By developing a framework that seamlessly links high-level flexibility requirements with detailed implementation requirements, systems can be comprehensively evaluated, methodically prescribed, and effectively compared. As future work, further refinement and validation of this framework will be crucial to ensuring its validity and applicability across diverse manufacturing contexts.

Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Nothing Like Compilation: How Professional Digital Fabrication Workflows Go Beyond Extruding, Milling, and Machines

Understanding how professionals use digital fabrication in production workflows is critical for future research in digital fabrication technologies. We interviewed thirteen professionals who use digital fabrication for the low-volume manufacturing of commercial products. From these interviews, we describe the workflows used for nine products created with a variety of materials and manufacturing methods. We show how digital fabrication professionals use software development to support physical production, how they rely on multiple partial representations in development, how they develop manufacturing processes, and how machine control is its own design space. We build from these findings to argue that future digital fabrication systems should support the exploration of material and machine behavior alongside geometry, that simulation is insufficient for understanding the design space, and that material constraints and resource management are meaningful design dimensions to support. By observing how professionals learn, we suggest ways digital fabrication systems can scaffold the mastery of new fabrication techniques.

Modelling and evaluation of the post-hardness and forming limit diagram in the single point incremental hole flanging (SPIHF) process using ANN, FEM and experimental

In the single point incremental hole flanging (SPIHF) process, a sheet material with pre-cut holes is deformed using the SPIF technique to generate a flange, making it an effective approach for low volume manufacturing and quick prototyping. In the case of the SPIHF technique, the post-forming hardness property, the forming limit diagram (FLD), and spring-back phenomena are not completely evaluated. To this end, this paper employs experimental investigation and numerical validation to analyse the impact of SPIHF process parameters like tool diameter, feed rate, spindle speed, and initial hole diameter on these aspects for the truncated incrementally formed components made from AA1060 aluminium alloy and DC01 carbon steel. The plasticity behaviour of both sheet metals was simulated using the Workbench LS-DYNA model and ANSYS software version 18. Additionally, Cowper Symonds power-law hardening was added to the model to account for material properties. The average post-hardness of AA1060 and DC01 was evaluated using an SPIHF prediction model based on the performance of an artificial neural network (ANN). This ANN model was developed using a feed-forward back-propagation network trained using the Levenberg-Marquardt approach. The ANNs 4-n-1 were created by varying the transfer functions and the number of hidden neurons. Greater spindle speed and bigger pre-cut holes were shown to significantly increase the post-formed hardness of the truncated components, whereas the converse was seen when using a higher feed rate and a larger tool diameter. In addition, the FLD and spring-back improved dramatically with larger hole diameters. Employing correlation coefficient (R) and mean square error (MSE) as validation measures, it was shown that the established ANN models accurately predicted the SPIHF process response. Both the DC01 and AA1060 neural network models with a 4-8-1 network architecture performed very well, with MSE and R values of 0.0000105 and 1 for DC01 and 0.02613 and 0.99982 for AA1061.

Opportunities for local electric vehicle manufacturing in Africa

In order to achieve the Sustainable Development Goals (SDG) of the United Nations on Clean Energy, Sustainable Industrialization and Climate Change, there is an urgent call on Africa to decarbonize transportation. Achieving these goals requires that Africa grows its current, largely, non-existent EV fleet to a reasonable level in the near future, to counter harmful emissions from fossil fuel vehicles. This paper discusses the opportunities available to African entrepreneurs on investing in electric vehicle (EV) manufacturing. It also discusses the factors militating against the establishment of a viable automotive sector. To exploit the opportunities offered despite the limitations, the paper advocates a paradigm shift from the conventional vehicle assembly of foreign vehicles to one in which African original designs are made in low-cost, low-volume vehicle manufacturing plants consisting of general-purpose workshop equipment and intensive labour such that the whole value chain for vehicle manufacturing is developed.

Investigating the surface roughness of calamine brass in incremental sheet metal forming

Incremental sheet metal forming (ISMF) is an emerging metal sheet forming technique. ISMF does not require any special/dedicated dies for shaping the metal sheet into a desired part. The forming technique is an alternative for customized part generation, prototype making, and low-volume product manufacturing. The experimental investigation is focused on finding the significance of process parameters on the average surface roughness of the ISMF part. Limited process parameters were taken that majorly influenced the ISMF. Calamine brass (Cu67Zn33) is preferred for home alliance and grocery items because of its sustainability and corrosion-resistant properties therefore thin sheets of it were taken as worksheets. The surface roughness of the formed part was measured through atomic force microscopy (AFM) technique. Statistical analysis confirmed that the step-down size, feed rate of the tool, wall angle, and the thickness of metal sheet whose P-values were 0.013, 0.052, 0.030, and 0.036 respectively were significant for average surface roughness. Their contribution was calculated as 76.71%, 0.025%, 13.67% and 0.89% respectively.

A New Fourier Q Operator Network Based Reinforcement Learning Method for Continuous Action Space Decision-making in Manufacturing

The problems of continuous action space decision-making are widespread in industrial manufacturing. However, when dealing with these problems, existing reinforcement learning (RL) methods relies on a large number of training samples, which is always unacceptable given the limited availability or expensive nature of data, such as low-volume manufacturing. This paper proposes a new Fourier Q operator network (FQON) based RL method. The input of FQON is the expected state function and its output the Q-value function, and both functions take the action in RL as independent variables. The infinite-dimensional mapping between the function domains is established by a set of parameters that can be used with different discretization, which fixes the mapping complexity regardless of the action space resolution. By taking the advantages of the fast calculation using on Fourier kernel operator, the mapping complexity is highly reduced, and it enables that FQON can realize the decision-making in continuous action space using a small amount of training samples. Taking machining deformation control of an aero-engine casing as a case study, experimental results showed that FQON based RL method can control the deformation well with limited training samples.

Data-driven product optimization capabilities to enhance sustainability and environmental compliance in a marine manufacturing context

This paper investigates concerns related to product data and digital data flow when aiming to automate company processes. Accurate data is necessary to create value by enabling improved decision-making in product development, including sustainability capabilities. The case analyzed is an engineer-to-order (ETO) company operating in a low-volume marine manufacturing context. A participatory research approach is used to study two projects that are part of the company’s digital business transformation, aiming to digitalize information and autogenerate downstream processes. Building on the strengths promised by digitalization requires precise and extensive product and process information. An important facilitation capability is to create a digital thread from design to finished product, including product documentation. This is necessary to establish capabilities both to autogenerate appropriate compliance reporting as part of the product development process and to conduct virtual testing and validation before the physical equipment is acquired, resulting in a manufacturing process that is ‘right first time’. In addition, data capabilities guide and enable sound-decision making for improved sustainable practices in the early phase of product development. It is found that the data quality required to utilize tools within the context of Industry 4.0 demands changes to existing product design practices and focus on the three pillars harmonization, integration and automation of data and systems.

A framework for predicting grain morphology during incremental sheet metal forming using generative adversarial networks

Properties and service performance of parts and components depend upon microstructural characteristics including grain morphology, size and size distribution, and crystallographic orientation. Microstructure itself is dictated by manufacturing process parameters. Microstructural analysis is often necessary for evaluating product quality. Past research has attempted to map product/process characteristics using analytical, numerical, and experimental approaches. To address the limitations of conventional modeling approaches, researchers have begun to explore the application of artificial intelligence (AI) techniques in correlating and predicting the effect of process parameters on material microstructure and mechanical properties. For instance, AI-based approaches have been used to model the influence of process parameters on forming force, formability, geometric accuracy, tool path, and surface quality in incremental sheet forming (ISF), a flexible forming process suitable for low-volume and custom product manufacturing. The research reported herein introduces a framework to map ISF process parameters to microstructural images using Generative Adversarial Networks (GAN). This approach can reduce the experimental and analytical effort and costs typically necessary to evaluate the effects of ISF process settings on material microstructure and mechanical properties.

Solving a many-objective PFSP with reinforcement cumulative prospect theory in low-volume PCB manufacturing

Solving a many-objective PFSP with reinforcement cumulative prospect theory in low-volume PCB manufacturing

Review of optical direct-write technology for semiconductor manufacturing

Optical direct-write technology has been studied since the successful development of the micromirror-based spatial light modulators in the 1990s. It is expected to have advantages over electron beam direct-write in the viewpoints of higher productivity and common resist process with existing semiconductor manufacturing. In the 2000s, there was an effort to develop an optical direct-write system using deep ultraviolet (DUV) excimer laser and grayscale tilt micromirror devices, aiming at low-volume manufacturing of semiconductor devices. Recently, a new scheme of optical direct-write, called the digital scanner, has been demonstrated. It uses solid-state DUV laser and digitally controlled spatial light modulator. Past development activity of optical direct-write is reviewed with discussion of the merits and demerits of the proposed technological options in the new system, comparing them to past efforts.

Learning-based robotic grasping: A review.

As personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as logistics, fast-moving consumer goods, and food delivery, these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without much modification or downtime. Most solutions in the market are based on traditional object recognition and are, therefore, not suitable for grasping unknown objects with varying shapes and textures. Adequate learning policies enable robotic grasping to accommodate high-mix and low-volume manufacturing scenarios. In this paper, we review the recent development of learning-based robotic grasping techniques from a corpus of over 150 papers. In addition to addressing the current achievements from researchers all over the world, we also point out the gaps and challenges faced in AI-enabled grasping, which hinder robotization in the aforementioned industries. In addition to 3D object segmentation and learning-based grasping benchmarks, we have also performed a comprehensive market survey regarding tactile sensors and robot skin. Furthermore, we reviewed the latest literature on how sensor feedback can be trained by a learning model to provide valid inputs for grasping stability. Finally, learning-based soft gripping is evaluated as soft grippers can accommodate objects of various sizes and shapes and can even handle fragile objects. In general, robotic grasping can achieve higher flexibility and adaptability, when equipped with learning algorithms.

Concrete 3D Printing: Process Parameters for Process Control, Monitoring and Diagnosis in Automation and Construction

In Singapore, there is an increasing need for independence from manpower within the Building and Construction (B&C) Industry. Prefabricated Prefinished Volumetric Construction (PPVC) production is mainly driven by benefits in environmental pollution reduction, improved productivity, quality control, and customizability. However, overall cost savings have been counterbalanced by new cost drivers like modular precast moulds, transportation, hoisting, manufacturing & holding yards, and supervision costs. The highly modular requirements for PPVC places additive manufacturing in an advantageous position, due to its high customizability, low volume manufacturing capabilities for a faster manufacturing response time, faster production changeovers, and lower inventory requirements. However, C3DP has only just begun to move away from its early-stage development, where there is a need to closely evaluate the process parameters across buildability, extrudability, and pumpability aspects. As many parameters have been identified as having considerable influence on C3DP processes, monitoring systems for feedback applications seem to be an inevitable step forward to automation in construction. This paper has presented a broad analysis of the challenges posed to C3DP and feedback systems, stressing the admission of process parameters to correct multiple modes of failure.