Small Batch Production Research Articles

Reflow oven with Wi-Fi connectivity

Abstract This paper will address a theme that highlights the role of an electrical equipment in the electrical industry, focusing on the process of soldering components onto the surface of printed circuit boards. The choice of the theme “Reflow Oven with Wi-Fi Connectivity” was driven by the desire to create a system with practical and applicative outcomes. The aim is to develop equipment that facilitates the soldering process of Surface Mounted Device (SMD) components. Such an automated system is valuable in the production of small batches of equipment, such as electronic modules for prototypes or various applications. The equipment or electronic module aims to depict a Printed Circuit Board (PCB) on which both Surface Mounted Devices (SMD) and Through Hole Technology (THT) components are mounted. Throughout the paper, the implementation of an automated system capable of carrying out the soldering process for SMD components will be discussed.

A code-based method for carbon emission prediction of 3D printing: A case study on the fused deposition modeling (FDM) 3D printing and comparison with conventional approach

This study presents a method for predicting carbon emissions from 3D printing based on the G-code and compares the emissions between FDM and injection molding (IM) processes. Traditional manufacturing methods for (polylactic acid) PLA plastic products involve IM, which require extensive equipment and metal molds to be manufactured first. This process involves repetitive heating and machining, low material utilization, high energy consumption, and high pollution. 3D printing using a gradual accumulation of materials to manufacture parts has transformed traditional manufacturing methods. However, concerns regarding the environmental impact of 3D printing have been raised in previous studies. To achieve sustainability in 3D printing, it is important to make effective adjustments by predicting the carbon emissions before printing. In this study, we propose a method for predicting carbon emissions from 3D printing by integrating processing characteristics with G-code instructions. The deviation of the model was confirmed to be between 3.86% and 5.82% through a case study of FDM 3D printing. The optimal process parameters were determined from carbon emissions predictions, resulting in carbon emissions reductions of up to 43.77%. Carbon emissions were evaluated using both the traditional IM method and FDM 3D printing for manufacturing PLA plastic products. The results showed that the carbon emissions from FDM were significantly lower than those from traditional IM in small batches or customized production. In the traditional IM method, mold manufacturing produces high carbon emissions, accounting for more than 99% of the entire process. The findings of this study offer valuable guidance to manufacturers in selecting appropriate manufacturing methods and formulating production strategies. By predicting carbon emissions in the FDM process and adjusting the parameters based on product quality requirements, significant reductions in carbon emissions can be achieved during the production of small batches or customized PLA plastic products. The carbon emission prediction method proposed in this study presents a more sustainable solution for 3D printing technology, which is crucial for advancing sustainable production and environmental protection in this field.

Meta-learning enhanced adaptive robot control strategy for automated PCB assembly

The assembly of printed circuit boards (PCBs) is one of the standard processes in chip production, directly contributing to the quality and performance of the chips. In the automated PCB assembly process, machine vision and coordinate localization methods are commonly employed to guide the positioning of assembly units. However, occlusion or poor lighting conditions can affect the effectiveness of machine vision-based methods. Additionally, the assembly of odd-form components requires highly specialized fixtures for assembly unit positioning, leading to high costs and low flexibility, especially for multi-variety and small-batch production. Drawing on these considerations, a vision-free, model-agnostic meta-method for compensating robotic position errors is proposed, which maximizes the probability of accurate robotic positioning through interactive feedback, thereby reducing the dependency on visual feedback and mitigating the impact of occlusions or lighting variations. The proposed method endows the robot with the capability to learn and adapt to various position errors, inspired by the human instinct for grasping under uncertainties. Furthermore, it is a self-adaptive method that can accelerate the robotic positioning process as more examples are incorporated and learned. Empirical studies show that the proposed method can handle a variety of odd-form components without relying on specialized fixtures, while achieving similar assembly efficiency to highly dedicated automation equipment. As of the writing of this paper, the proposed meta-method has already been implemented in a robotic-based assembly line for odd-form electronic components. Since PCB assembly involves various electronic components with different sizes, shapes, and functions, subsequent studies can focus on assembly sequence and assembly route optimization to further enhance assembly efficiency.

Visual pattern auto-generation for yarn-dyed plaid fabric based on modified genetic operators

With the aggravated homogenization of the commodity in the increasingly competitive marketplace, the industries have been converted to design and manufacture small batch productions with a wide variety. To reduce the designer’s burden and shorten the design cycle of the product, an auto-generation method for the pattern of yarn-dyed fabric was proposed based on the modified genetic operators. Initially, the design elements to form the pattern were extracted and encoded as a chromosome by natural number encoding. Then, the fabric weave model was built using the matrix of 0 and 1. Based on the color scheme and yarn arrangement, the pattern of yarn-dyed fabric can be generated automatically. The elements can be exchanged to generate new patterns by the genetic operators. Finally, the obtained pattern was imported into the three-dimensional virtual shirt model. Experiments show that the proposed method can help the designer obtain different patterns quickly and effectively, and different design elements can be controlled according to the requirements of the designer. Based on the annual fashion trend, fashionable colors can be involved to realize fast reaction in product design.

Lean Manufacturing Implementation in Developing Countries:Evidence from Jordanian Industrial Companies

In today's fiercely competitive landscape, industries are increasingly embracing lean manufacturing strategies for survival. However, the adoption of lean principles in Jordanian manufacturing sectors remains relatively new. This study aims to evaluate the current state of lean adoption in Jordanian industries, focusing on its techniques, motivations, and challenges. Employing a quantitative methodology, surveys targeted Production, Operation, and Quality Managers in Jordanian organizations. Out of 300 distributed questionnaires, 176 valid responses were received, representing a response rate of 58.7%. The findings indicate a relatively low adoption of lean practices in Jordanian industries, though firms that have implemented lean report significant benefits, particularly in quality enhancement and cost reduction. Key challenges include inadequate training, a scarcity of experts, and complexities in small batch production. While the study's sample size is limited, caution is warranted in interpreting the results. Further empirical research is needed to quantify the performance benefits of lean adoption. This study sheds light on lean adoption in Jordanian manufacturing, informing knowledge development and guiding future efforts to promote lean practices in the industrial landscape.

Quality prediction for rocket body structure manufacturing process based on improved ELM

In the current multi-variety and small-batch production mode, this paper proposes a quality prediction method for rocket body structure manufacturing process based on improved extreme learning machine (ELM). Aiming at the non-optimal problems caused by random selection of input layer weights and hidden layer bias of ELM, particle swarm optimization (PSO) is combined with gravitational search algorithm (GSA) to form a hybrid algorithm to optimize the random parameters of ELM. Taking a weld in a rocket body structure as an example, the weld width prediction model based on improved ELM is established and verified. The results show that compared with the prediction model based on the standard ELM and the prediction model based on PSO-ELM, the prediction model based on improved ELM has higher prediction accuracy, and its prediction deviation does not exceed 2.5%, which can effectively predict the weld width.

Comparative evaluation of formability in cup drawing and V-bending using additively manufactured tools

ABSTRACT Additive Manufacturing (AM) is rapidly transforming industrial processes by introducing advanced methods for designing, manufacturing, and prototyping key components across various industries. Its true potential is realized in integrated component repair via reverse engineering, significantly reducing rework, maintenance costs, and time. With growing demand, AM technology continues to evolve, incorporating intelligent printing systems and diverse applications in sectors like automotive, sheet metal forming, healthcare, aviation, and consumer goods. AM also aids in optimizing geometric and process parameters for tools such as dies, punches, and blank holders, enhancing economic batch production. This study focuses on the Finite Element Analysis (FEA) of two commercial operations: bending and deep drawing, using DC04 and AA6060 materials. The analysis identifies geometric precision, thinning, and formability. In steel, increasing the bottom radius from 3 mm to 20 mm results in an 11% difference in thinning, with a maximum of 0.67 mm. For aluminium, thinning varies minimally between 0.68 and 0.75 mm. Increased sheet thickness limits formability in both materials. FDM-printed tools were used for FEA simulation, followed by stress analysis of dies to assess design safety, small batch production, and dimensional conformity.

A non-enzymatic flexible and wearable sensor based on thermal transfer printing technology for continuous glucose detection in sweat

Non-invasive, flexible wearable sensors are an important method to continuously measure glucose in sweat for diabetes and human health monitoring and management. However, how to create an alkaline environment at any time so that CuO based sensors can be applied to wearable devices to detect sweat glucose while finding a way to efficiently and quickly produce the sensors in small batches have been a challenge. Herein, we report a non-enzymatic thermal transferred flexible and wearable sensor based on CuO/CaTiO3 for continuous glucose detection in sweat. The sensor is manufactured using thermal transfer printing technology, which offers advantages like low cost, ease of operation, and rapid small batch production. CuO is known for its excellent electrocatalytic activity, making it a suitable candidate for glucose oxidation. Meanwhile, CaTiO3 particles provide a large specific surface area, good biocompatibility, and electrical conductivity, which enhance the electron transfer rate during detection and broaden the linear range of glucose detection. Specially designed NaOH/Nafion/PEO blend film make the measurements always in a strong alkali environment without any pretreatment and preparation. The synthesized material with this as-prepared flexible and wearable sensor exhibits superior performance towards glucose monitoring, such as high sensitivity of 487.3 μA mM−1 cm−2 with limit of detection(LOD) 0.75 μM, wide dynamic linear range from 0.01 mM to 2 mM and fast response time of less than 0.1 s. Additionally, the proposed sensor also exhibited excellent biocompatibility, selectivity, reproducibility and flexibility, as well as good stability with about 88 % of its initial activity after 5 weeks’ storage and it has been successfully applied for the detection of glucose concentration in human sweat real samples. This research contributes to the development of flexible and wearable sensors for non-invasive sweat diagnostics, enabling continuous glucose monitoring for various applications in healthcare and wellness.

Real-Time Monitoring of Human and Process Performance Parameters in Collaborative Assembly Systems using Multivariate Control Charts

With the rise in customized product demands, the production of small batches with a wide variety of products is becoming more common. A high degree of flexibility is required from operators to manage changes in volumes and products, which has led to the use of Human-Robot Collaboration (HRC) systems for custom manufacturing. However, this variety introduces complexity that affects production time, cost, and quality. To address this issue, multivariate control charts are used as diagnostic tools to evaluate the stability of several parameters related to both product/process and human well-being in HRC systems. These key parameters monitored include assembly time, quality control time, total defects, and operator stress, providing a more holistic view of system performance. Real-time monitoring of process performance along with human-related factors, which is rarely considered in statistical process control, provides comprehensive stability control over all customized product variants produced in the HRC system. The proposed approach includes defining the parameters to be monitored, constructing control charts, collecting data after product variant assembly, and verifying that the set of parameters is under control via control charts. This increases the system's responsiveness to both process inefficiencies and human well-being. The procedure can be automated by embedding control chart routines in the software of the HRC system or its digital twin, without adding additional tasks to the operator's workload. Its practicality and effectiveness are evidenced in custom electronic board assembly, highlighting its role in optimizing HRC system performance.

Application of Numerical Modeling and Finite Element Analysis in Fused Filament Fabrication: A Review.

Additive manufacturing (AM) is not necessarily a new process but an advanced method for manufacturing complex three-dimensional (3D) parts. Among the several advantages of AM are the affordable cost, capability of building objects with complex structures for small-batch production, and raw material versatility. There are several sub-categories of AM, among which is fused filament fabrication (FFF), also commonly known as fused deposition modeling (FDM). FFF has been one of the most widely used additive manufacturing techniques due to its cost-efficiency, simplicity, and widespread availability. The FFF process is mainly used to create 3D parts made of thermoplastic polymers, and complex physical phenomena such as melt flow, heat transfer, solidification, crystallization, etc. are involved in the FFF process. Different techniques have been developed and employed to analyze these phenomena, including experimental, analytical, numerical, and finite element analysis (FEA). This study specifically aims to provide a comprehensive review of the developed numerical models and simulation tools used to analyze melt flow behavior, heat transfer, crystallization and solidification kinetics, structural analysis, and the material characterization of polymeric components in the FFF process. The strengths and weaknesses of these numerical models are discussed, simplifications and assumptions are highlighted, and an outlook on future work in the numerical modeling and FE simulation of FFF is provided.

Design and Prototyping of a Collaborative Station for Machine Parts Assembly

Collaboration between humans and machines is the core of the Industry 5.0 paradigm, and collaborative robotics is one the most impactful enabling technologies for small and medium Enterprises (SMEs). In fact, small batch production and high levels of product customization make parts assembly one of the most challenging operations to be automated, and it often still depends on the versatility of human labor. Collaborative robots, for their part, can be easily integrated in this productive paradigm, as they have been specifically developed for coexistence with human beings. This work investigates the performance of collaborative robots in machine parts assembly. Design and research activities were carried out as a case study of industrial relevance at the i-Labs industry laboratory, a pole of innovation that is briefly introduced at the beginning of the paper. A fully-functional prototype of the cobotized station was realized at the end of the project, and several experimental tests were performed to validate the robustness of the assembly process as well as the collaborative nature of the application.

Operando visualization of porous metal additive manufacturing with foaming agents through high-speed x-ray imaging

Porous metals find extensive applications in soundproofing, filtration, catalysis, and energy-absorbing structures, thanks to their unique internal pore structure and high specific strength. In recent years, there has been an increasing interest in fabricating porous metals using additive manufacturing (AM), leveraging its unique advantages, including improved design freedom, spatial material control, and cost-effective small-batch production. In this study, we conducted pioneering operando visualization of AM porous metal using a laser powder bed fusion (L-PBF) setup combined with a high-speed synchrotron x-ray imaging system. Single track printing experiments using Ti6Al4V (Ti64) combined with titanium hydride (TiH2) and sodium carbonate (Na2CO3) as foaming agents, with varying mixing ratios were performed under different processing conditions. The results elucidate the dynamic development of porosity formation. The average pore size is significantly influenced by the particle size of foaming agents when pore coalescence is absent. For all foaming agent content tested in the current study, the number of pores is found to be more sensitive to changes in laser power than in laser scanning speed. Increasing linear energy density (increasing laser power or reducing laser scanning speed) promotes the foaming agent activation thereby porosity formation. However, high linear energy density skews pore distribution towards the surface despite forming deeper melt pools. In addition, the impact of additional factors including foaming agent’s laser absorptivity and decomposition kinetics with respect to AM time scales should be carefully considered to avoid ineffective activation of foaming agents during the AM of porous metals.

Exploring Polymer-Based Additive Manufacturing for Cost-Effective Stamping Devices: A Feasibility Study with Finite Element Analysis.

This research investigates the feasibility of manufacturing stamping devices using Material Extrusion (MEX) Additive Manufacturing (AM) technology, traditionally fabricated from metal, to reduce production costs and time. This study examines polymer-based devices subjected to Finite Element Analysis (FEA) to evaluate their performance in stamping metal sheets of varying thicknesses. The findings reveal that ABS polymer devices, while demonstrating potential, operate near the material's limit under compression forces, particularly for sheet thicknesses up to 1 mm. Specifically, differences of 0.7 mm were observed at the connection radii of 0.25 mm sheets and 1.4 mm for 0.5 mm sheets, with angular deviations of 1.5 degrees for 0.25 mm sheets and 4 degrees for 0.5 mm sheets. Additionally, devices made of Nylon were deemed suitable for reduced-thickness sheets (0.25 mm), performing better than those made of ABS. These results suggest that while ABS devices exhibit significant deviations (up to 45 degrees for 1 mm sheets), the method shows promise for small batch production and prototyping. Further optimisation through material enhancements and mechanical improvements is recommended to minimise deformations and enhance precision.

A do it yourself (DIY) point-of-care wrist ultrasound phantom for joint access training

BackgroundJoint access is essential for arthrocentesis, or joint aspiration of fluids. Joint treatments that are not performed properly can result in avoidable patient issues such as damage to the muscles, tendons, and blood vessels surrounding the joint. The use of ultrasound has become the gold standard for this procedure and proven to be a support in the skill learning process. However, success with this equipment, particularly in small joints like the wrist, depends on a clinician's capacity to recognize the crucial landmarks that guide these procedures. Prior to executing on a real patient, task trainers have proven to be an effective way for doctors to practice and prepare for procedures. However, shortcomings of current solutions include high purchase costs, incompatibility with ultrasound imaging, and low reusability. In addition, since this is a procedure that is not performed frequently, there may not be space or resources available in healthcare facilities to accommodate one at the point of care. This study aimed to close the existing gap by developing a DIY ultrasound compatible task trainer for wrist joint access training.ResultsWe developed a novel ultrasound compatible wrist joint model that can be made from sustainable materials and reusable parts, thus reducing the costs for acquisition and environmental impact. Our model, which was produced utilizing small-batch production methods, is made up of 3D-printed bones enclosed in an ultrasound-compatible gelatin mixture. It can be easily remade after each practice session, removing needle tracks that are visible under ultrasound for conventional phantoms. The ultrasonic properties of this model were tested through pixel brightness analysis and visual inspection of simulated anatomical structures.ConclusionOur results report the advantages and limitations of the proposed model regarding production, practice, and ultrasound compatibility. While future work entails the transfer to patients of the same skill, this reusable and replicable model has proven, when presented to experts, to be successful in representing the physical characteristics and ultrasound profile of significant anatomical structures. This novel DIY product could be an effective alternative to teach procedures in the context of resource-restrained clinical simulation centers.

Online scheduling problem of incompatible batch processing with deterioration effect and delivery time in the steel rolling process

As market competition intensifies, modern enterprises prioritize customer demands, leading to an increased emphasis on multi-variety and small-batch production. In this article, we study the online scheduling problem for incompatible batch processing with the deterioration effect to minimize the maximum delivery completion time in the billet rolling process, where the batch capacity and delivery capacity are unbounded. We assume that the processing time of job Jj is expressed as p j = a j ( A + Bt ) , where A > 0, B > 0, t denotes the start time of job and aj > 0 represents the job’s processing deterioration rate. For such an online problem, we first prove that the lower bound of the problem is ( 1 + B a max ) k , where k denotes the number of job families and a max represents the maximum deterioration ratio in job instance. Then we design an online algorithm Largest Delivery Time With Batch Processing and demonstrate that the competitive ratio of the algorithm is no more than 1 + ( 1 + B a max ) k . Finally, experiments on randomly generated instances prove that our algorithm is reasonable and effective.

Small-batch product quality prediction using a novel discrete Choquet fuzzy grey model with complex interaction information

With manufacturing focused on multi-variety and small-batch intelligent production, the challenge is to guarantee and ensure good product quality. This paper proposes a novel small-batch product quality prediction approach considering complex interaction information. In this paper, first, to handle the complex nonlinear correlations among the manufacturing process variables, a kernel principal components analysis is used to extract the major features from the high-dimensional data. Second, we combine the discrete multivariate grey model and generalized discrete Choquet fuzzy integral to propose a discrete Choquet fuzzy grey model so as to capture the complex interaction information among the process variables. Next, an adjoint sensitivity analysis is applied to characterize the changes in the process variables on the predicted product quality index. We conduct an experiment on six semiconductor products made in China to validate the proposed model against a library of nine other methods. Our model yields an average reduction of 11.02% on MAPE, 20.47% on MSE, 13.56% on STD, and an average increase of 46.37% on EVS for six types of products. Our results inform that the proposed model is suitable for predicting manufacturing product quality when the process variables interact significantly, and we can prioritize the process variables for remedial action accordingly.

Bridging the gap: integrating AR and VR production line training to enhance furniture carpentry students’ understanding of production processes

ABSTRACT Large-scale furniture production involves complex manufacturing processes and methods. However, Taiwanese schools face limitations in terms of space and equipment, restricting students from engaging in furniture prototype creation or small-batch production. As a result, students often struggle to grasp practical aspects of large-scale furniture production, leading to a partial understanding of manufacturing processes and methods. To bridge this gap, this study introduces Augmented Reality (AR) technology and miniaturized furniture production lines in a school’s woodshop. AR can be used to guide students through miniaturized production stages, emphasizing semi-automated furniture production features. Additionally, Virtual Reality (VR) technology is combined to provide an immersive experience of real-world, large-scale furniture production, allowing students to understand processing scenarios and conditions in facilities of other countries. In an experiment involving 30 first-year students, an experimental group (15 participants) received mixed AR and VR instruction, while a control group (15 participants) received traditional teaching methods. Findings reveal that AR and VR integration enhanced students’ comprehension of differences between the school workship and overseas large-scale furniture production facilities. This strategy improved their furniture production knowledge, and fostered adaptability to various production conditions and methods.

Advances in laser‐based manufacturing techniques for specialty optical fiber

AbstractAs demand for customized specialty fibers grows, standardized production methods face challenges. This article reviews industry standards and discusses potentially disruptive techniques that enable rapid prototyping and fabrication of optical fiber devices. Furthermore, we showcase laser powder deposition's (LPD) potential for additive manufacturing (AM) of customized glass structures. In the case of, for example, fiber preforms, although the feasible size is smaller than the industry standard, utilizing laser‐based manufacturing techniques for a small batch production presents an attractive avenue for rapid prototyping and expedites material and design optimization. In the realm of AM of glass, LPD offers numerous benefits, including minimal shrinkage, high densification, and the ability to tailor glass composition to achieve desired optical properties. The article reviews the latest achievements and highlights future directions in this technology.

Seam tracking and gap bridging during robotic laser beam welding via grayscale imaging and wobbling

The use of laser beam welding with robotic manipulators is expanding towards wider industrial applications as the system availability increases with reduced capital costs. Conventionally, laser welding requires high positioning and coupling accuracy. Due to the variability in the part geometry and positioning, as well as the thermal deformation that may occur during the process, joint position and fit-up are not always acceptable nor predictable a-priori if simple fixtures are used. This makes the passage from virtual CAD/CAM environment to real production site not trivial, limiting applications where short part preparations are a need like small-batch productions. Solutions that render the laser welding operations feasible for production series with non-stringent tolerances are required to serve a wider range of industrial applications. Such solutions should be able to track the seam as well as tolerating variable gaps formed between the parts to be joined. In this work, an online correction for robot trajectory based on a greyscale coaxial vision system with external illumination and an adaptive wobbling strategy are proposed as means to increase the overall flexibility of a manufacturing plant. The underlying vision algorithm and control architectures are presented; the robustness of the system to poor illumination conditions and variable reflection conditions is also discussed. The developed solution employed two control loops: the first is able to change the robot pose to follow varying trajectories; the second, able to vary the amplitude of circular wobbling as a function of the gap formed in butt-joint welds. Demonstrator cases on butt-joint welds with AISI 301 stainless steel with increased complexity were used to test the efficacy of the solution. The system was successfully tested on 2 mm thick, planar stainless-steel sheets at a maximum welding speed of 25 mm/s and yielded a maximum positioning and yaw-orientation errors of respectively 0.325 mm and 4.5°. Continuous welds could be achieved with up to 1 mm gaps and variable seam position with the developed control method. The acceptable weld quality could be maintained up to 0.6 mm gap in the employed autogenous welding configuration.

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.