Low-volume Production Research Articles

Tool path planning of consecutive free-form sheet metal stamping with deep learning

Sheet metal forming technologies, such as stamping and deep drawing, have been widely used in automotive, rail and aerospace industries for lightweight metal component manufacture. It requires specially customised presses and dies, which are very costly, particularly for low volume production of extra-large engineering panel components. In this paper, a novel recursive tool path prediction framework, impregnated with a deep learning model, is developed and instantiated for the forming sequence planning of a consecutive rubber-tool forming process. The deep learning model recursively predicts the forming parameters, namely punch location and punch stroke, for each deformation step, which yields the optimal tool path. Three series of deep learning models, namely single feature extractor, cascaded networks (including state-of-the-art deep networks) and long short-term memory (LSTM) models are implemented and trained with two datasets with different amounts of data but the same data diversity. The learning results show that the single LSTM model trained with the larger dataset has the most superior learning capability and generalisation among all models investigated. The promising results from the LSTM indicate the potential of extending the proposed recursive tool path prediction framework to the tool path planning of more complex sheet metal components. The analysis on different deep networks provides instructive references for model selection and model architecture design for sheet metal forming problems involving tool path design.

Role of Additive Manufacturing in Investment Casting Process

Abstract: The aim of this study was to describe systematically the best available evidence of Additive manufacturing (AM) technology for different casting paths and How Rapid Investment casting (RIC) is revolutionizing the field of casting. The objective of this systematic review is to investigate the capabilities and effectiveness of Additive Manufacturing to provide an effective solution for investment casting production. Google Scholar, ResearchGate, Mendeley, ScienceDirect databases are used for research purposes. The conventional method of Investment Casting is less effective in terms of cost and time to develop new hard tooling wax patterns for low volume production and prototypes. To overcome this problem, we introduced additive manufacturing for making patterns for investment casting. This paper reviews the specific applications of Rapid prototyping in the field of casting. After applying the inclusion criteria, we found 40 articles for reviewing. This study concluded that using Additive manufacturing in Investment casting in place of the conventional method is more cost- effective and time-efficient. Keywords: Additive Manufacturing, Rapid Investment Casting.

Laboratory Ozonolysis Using an Integrated Batch-DIY Flow System for Renewable Material Production.

Flow chemistry offers a solution for replacing batch methods in chemical preparation where intermediates or products may pose toxicity or instability hazards. Ozonolysis offers an ideal opportunity for flow chemistry solutions, but multiple barriers to entry exist for use of these methods, including equipment cost and performance optimization. To address these challenges, we developed a programmable DIY syringe pump system to use for a continuous flow multireactor process using 3D-printed parts, off-the-shelf stepper motors, and an Arduino microcontroller. Reaction kinetics of ozonide formation informed the use of an integrated batch–flow approach, where ozone addition to an olefin was timed to coincide with fluid movement of a single-syringe pump, followed by downstream Pinnick oxidation and reductive quench in flow. The system was demonstrated by continuous preparation of azelaic acid from ozonolysis of palmitoleic acid, a process limited to low production volumes via batch chemistry. High total production of azelaic acid with 80% yield was obtained from an algae oil sourced unsaturated fatty acid: a product with important applications in medicine, cosmetics, and polymers. This low-cost, scalable approach offers the potential for rapid prototyping and distributed chemical production.

Self-adapting WIP parameter setting using deep reinforcement learning

This study investigates the potential of dynamically adjusting WIP cap levels to maximize the throughput (TH) performance and minimize work in process (WIP), according to real-time system state arising from process variability associated with low volume and high-variety production systems. Using an innovative approach based on state-of-the-art deep reinforcement learning (proximal policy optimization algorithm), we attain WIP reductions of up to 50% and 30%, with practically no losses in throughput, against pure-push systems and the statistical throughput control method (STC), respectively. An exploratory study based on simulation experiments was performed to provide support to our research. The reinforcement learning agent’s performance was shown to be robust to variability changes within the production systems.

Low-Cost Magnesium Alloy Sheet Component Development and Demonstration Project

<div class="section abstract"><div class="htmlview paragraph">Most of the applications of magnesium in lightweighting commercial cars and trucks are die castings rather than sheet metal, and automotive applications of magnesium sheet have typically been experimental or low-volume serial production. The overarching objective of this collaborative research project organized by the United States Automotive Materials Partnership (USAMP) was to develop new low-cost magnesium alloys, and demonstrate warm-stamping of magnesium sheet inner and outer door panels for a 2013 MY Ford Fusion at a fully accounted integrated component cost increase over conventional steel stamped components of no more than $2.50/lb. saved ($5.50/kg saved). The project demonstrated the computational design of new magnesium (Mg) alloys from atomistic levels, cast new experimental alloy ingots and explored thermomechanical rolling processes to produce thin Mg sheet of desired textures. A new commercial Mg alloy sheet material was sourced and pretreated with protective coil coatings, and its properties fully characterized. The Mg sheet was successfully warm-formed using novel lubricants into intermediate size benchmark parts and full-size automotive door inner and outer panels. The project also explored conventional welding processes for joining of Mg sheet, developed novel corrosion treatments for multi-metal assembly coatings, performed computer simulations of door panel forming using two new material cards based on crystal plasticity theory, and concluded with a door static and dynamic performance analysis. An overall cost driver and sensitivity assessment task compared the final cost penalty depending on the cost of the primary magnesium sheet.</div></div>

Requirement-driven evolution and strategy-enabled service design for new customized quick-response product order fulfillment process

• A novel design method based on the Kano model and TRIZ evolution trends is proposed. • Customized order fulfillment requirements in terms of laptop production are investigated and analysed. • Three design strategies namely, precise diffraction, regenerative chain, and flexible accommodation strategies are generated by the Kano-TRIZ design model. • These strategies are applied to the design of “Customized product order fulfillment System.” Under the digital transformation era, technologies such as Cyber-physical systems, the Internet of things, and Artificial Intelligence are increasingly mature, making it possible to transform from traditional factories to smart factories. During the transformation, building a communication channel between customer requirements and production capacity to realize customized order services with low volume and high-mix production is critical. This study proposes a novel requirement-driven and strategy-based model to achieve the quick response order placement and production configuration services through three phases, that is, (1) requirement-based service diagnosis, (2) design strategy generation, and (3) service system conceptualization and evaluation. Firstly, a statistical kano analysis method was proposed to mining customer requirements considering industry contexts. Then, TRIZ evolution trends were modified to design concepts for digital transformation based on key enterprise processes. Finally, a novel service development maturity model was constructed to evaluate the new digital system design. A comprehensive empirical case study of designing “Customized Product Order Fulfillment System” for the laptop production process is conducted to demonstrate this approach. The proposed novel requirement-driven and strategy-based model is expected to provide valuable insights for suggestions on technological trends and forecasting, future diverse and innovative applications in customized order fulfillment scenarios.

Design and Implementation of Synthetic Aperture Radar (SAR) Field-Programmable Gate Array (FPGA)-Based Processor

Synthetic aperture radar (SAR) is a unique imaging radar system that is capable of obtaining high-resolution images by using signal-processing techniques while operating in all weather and in the absence of a light source. The potential of SAR in a wide range of applications has led to new challenges in digital SAR processor design. On-board storage of SAR raw data is often not practical for real-time applications. The design of digital SAR processors is always restricted by the available space of the carrier system, data transfer rate, payload capacity and on-board power supplies. As reported in the literature, although customized hardware solutions could offer the desired performance, they are not feasible for low-volume production. This research aims to design and develop an efficient digital SAR processor by using field-programmable gate array (FPGA) with the consideration of hardware resources, processing speed and precision. In this paper, a hardware implementation of an FPGA-based SAR processor is presented. The implementation and architecture of the proposed SAR processor are highlighted in this paper. A MATLAB-based SAR processing range-Doppler algorithm (RDA) was developed as the benchmark for the development of an SAR processor. The target device, Altera Stratix IV GX FPGA EP4SGX230KF40C2, was selected for the design and implementation of an FPGA-based SAR processor. Comprehensive evaluations of the performance of the proposed SAR processor in terms of precision, timing performance and hardware resource utilizations are also presented. The proposed FPGA-based digital SAR processor achieves optimum performance in processing SAR signals for image formation. Evaluation shows that the designed SAR processor is capable of processing SAR images with ±1% difference error as compared to SAR images processed by MATLAB. The results also show a reduction in hardware usage via the implementation of an FPGA-based FFT/IFFT coprocessor. These promising results prove that the performance of the proposed processor is satisfactory and the achieved processing time, as well as the power consumption of the processor, outperformed existing implementations.

Economic impact of quality inspection in manufacturing: A proposal for a novel cost modeling

Designing suitable quality inspections is a significant issue for manufacturing companies. In particular, performing an economic evaluation of alternative inspections is key for choosing the most appropriate, especially in the field of low-volume productions. This paper presents a cost breakdown model for assessing the economic performance of quality inspections with the aim to support designers in early inspection design phases. Unlike the main cost models developed in the scientific literature that only consider cost components related to the production process, the proposed model integrates production costs with the main cost drivers involved in the product quality-related steps, including inspection activities, pre- and after-sales reworks and unnecessary repairs due to inspection errors. Moreover, a novel performance measure providing a snapshot of profitability of product inspections – the Return On Investment of Inspections (ROII) – is presented. An application case in metal Additive Manufacturing is proposed to demonstrate the practical relevance of the model.

Product quality optimization vs production capacity optimization: an analytical perspective

PurposeThis study aims to devise generalized unconstrained optimization models for ascertaining the optimal level of product quality and production capacity level by modeling both product price and production cost as a function of product quality. Further, interrelations among investment for quality, product quality and production volume are considered. This study contributes toward the extant research, in that nuances related to price, production volume, and product quality are fused together such that two broad operational strategies of product quality optimization and production capacity optimization can be contrasted.Design/methodology/approachTo achieve the research objectives, the authors evolve unconstrained optimization models such that optimal product quality level and optimal production capacity level can be obtained employing the principles of differential calculus aimed at maximizing the manufacturer's profit. Specifically, nuances related to quality technology and efficiency, and quality loss cost has also been integrated in the integrated model. Thereafter, employing numerical analysis for a generalized product, the detailed workings of evolved models are demonstrated. The authors further carry out the sensitivity analysis to understand the impact of investment for quality onto the manufacturer's profit for both operational strategies.FindingsThe research demonstrates that the manufacturer would be better off adopting production capacity optimization strategy as an operational policy, as opposed to product quality optimization policy for the manufacturer's profit maximization. Further, considering the two operational strategies, the manufacturer does not obtain the highest possible theoretical profit when pertinent variables (product quality and production capacity) are set at highest possible theoretical level. This research discusses that in low-volume and high-margin products, it might be useful to adopt a product quality optimization strategy as a production capacity optimization strategy results in significantly high quality loss cost.Originality/valueThe findings of our study have a significant implication for industries such as steel-making, cement production, automotive industry wherein the conventional wisdom dictates that higher level of production capacity utilization always results in higher level of revenues. However, the authors deduce that beyond certain production capacity utilization, striving for higher utilization does not fetch additional profit. This work also adds to the extant research literature, in that it integrates the nuances of product quality, production volume and pricing in an integrative manner.

The Effects of Cooling and Shrinkage on the Life of Polymer 3D Printed Injection Moulds.

3D Printed Injection Moulds (3DIM), commonly used for low volume production and prototyping purposes, are known to fail abruptly and have a comparatively shorter life than conventional moulds. Investigating the underlying critical factors affecting failure may help in reducing the risk of abrupt failures and possibly prolong the 3DIM tool life. A hypothesis that the cooling time of the Injection Moulding (IM) process is a critical factor for 3DIM tool failure has been proposed. The failure hypothesis has been validated by theoretical calculations, FEA simulations and experimental investigations. Experiments were performed using two different materials for the 3DIM tool (Visijet M3-X and Digital ABS) and an engineering thermoplastic (Lexan 943-A) as the moulding material. Results showed that cooling time was a critical factor on tool life and managing the thermal loading on a 3DIM tool could lead to increased tool life. The paper identifies cooling time as the critical factor affecting 3DIM tool life and presents a cooling regime that could possibly lead to prolonged tool life.

Towards Large Parts Manufacturing in Additive Technologies for Aerospace and Automotive applications

Well-established advantages as design freedom, acceleration of design-to-manufacturing cycle, decreased internal logistics reflect on the wider application of Additive Manufacturing as the main manufacturing process. However, its application to large-scale components manufacturing is still an open challenge, because of the limited printing volume available in off-the-shelf machines, slow manufacturing process, and low production volume. After a review of the available contributions, this paper proposes a methodology to handle large-scale 3D models, to be applied before the slicing process. The methodology is based upon the large-scale component subdivision into subparts within CAD environments, using an innovative approach tailored to the problem, and exploits the multi-head capability of collaborative large-scale AM machines. A UAV fixed-wing shows the positive effects in terms of speeding up the manufacturing process. The approach can significantly reduce the printing time of large parts, but a new generation of Additive Manufacturing machines is required to exploit the methodology.

Gas exchange and chlorophyll fluorescence in spearmint (Mentha spicata L.) leaves influenced by mineral nutrition

The production of export-quality spearmint is limited in Colombia because of low production volumes, poor compliance with good agricultural practices, nutrient availability, and fertilization management. This study aimed to identify how NPK fertilization influences photosynthesis and photochemistry in Mentha plants during vegetative growth in a mesh house. Increasing doses of chemical fertilization were evaluated with a 10-30-10 (N-P-K) formula at 0, 60, 90, 120, and 180 kg ha-1. The evaluated variables were net photosynthesis (A), transpiration (E), stomatal conductance (gs), leaf temperature (Tleaf), quantum yield (Qy), Non-photochemical quenching (NPQ), photochemical quenching (qP), and dry matter (Dm). The highest A, Qy, E, and gs values were in the plants treated with high NPK doses; the NPQ and qP increased in the plants with low NPK doses. These findings elucidated the influence of NPK on photosynthesis and other physiological parameters in the growth and development of spearmint.

Digitization of product shape data for low-volume production

Digitization of product shape data for low-volume production

Impact of Resource Allocation Policies on Lead Time in a High-Mix Low-Volume Production System

Impact of Resource Allocation Policies on Lead Time in a High-Mix Low-Volume Production System

Using Artificial Intelligence to Facilitate Assembly Automation in High-Mix Low-Volume Production Scenario

Artificial Intelligence (AI) based algorithms are being used increasingly to support industrial robots in the automation of assembly processes. The objective of this work is to detect bores of different geometry, appearance, and inner structure for automating a high-mix low-volume assembly line. Two most widely used AI algorithms namely, Single Shot Detection (SSD) and You Only Look Once (YOLO) have been used to perform the bore detection process. The results obtained by these algorithms have been compared with the conventional detection algorithm (Gradient filter) using the standard metrics used for evaluating the performance of the detection algorithms. The obtained results demonstrate the efficiency and robustness of AI-based algorithms for the detection as they exhibit better performance than the conventional detection method.

Predictive Maintenance Decision Making Based on Reinforcement Learning in Multistage Production Systems

Predictive maintenance has become increasingly prevalent in modern production systems that are challenged by high-mix low-volume production and short production life cycle. It is very helpful to prevent costly equipment failures, and reduce significant production loss caused by unscheduled machine breakdown. Although important, decision models for joint predictive maintenance and production in manufacturing systems have not been fully explored. Therefore, we propose a reinforcement learning based decision model, that brings together production system modeling and approximate dynamic programming. We start from the development of a state-based model by analyzing the dynamics of a multistage production system with predictive maintenance. It provides an approach to quantitatively evaluate the various disruptions as well as the maintenance decision’s impact on production. Then a reinforcement learning method is proposed to explore optimal maintenance policies, that optimize the production and maintenance cost. To further improve the performance of the production system, machine stoppage bottlenecks are defined. An event-based indicator is proved to identify bottlenecks with production data. We test the proposed models in simulation case studies. The proposed predictive maintenance decision model is compared with three policies, which are state-based policy (SBP), time-based policy (TBP) and greedy policy (GP). The numerical studies show that the proposed decision model outperforms the policies, and it has the lowest system cost that is 9.68%, 39.07%, and 39.56% lower than SBP, TBP, and GP, respectively. In addition, the research shows that bottleneck identification and mitigation could help manufacturing systems to achieve more than 9.00% throughput improvement.

Study on multi-objective search performance of production plan optimization in manufacturing field

There is a great demand for multi-objective optimization of production plans at the high-mix low-volume production of the manufacturing industry, and the importance of multi-objective numerical optimization technology will increase more and more in the future. In this research, we have developed a control method that can realize a wide range of Pareto solution at high speed for two object functions; cost and productivity, which are important in manufacturing fields but have a trade-off relationship. The key of present technique is a technology that can obtain a wide range of Pareto solutions, which are difficult to solve by each technology alone, by appropriately controlling a technology that has high search performance but is single objective optimization and a technology that can perform multi-objective optimization but has low search performance. This paper presents an example of solving a standard multi-objective optimization problem and the results of applying it to setup optimization problems which are often found in actual manufacturing fields.

Lead time reduction and process enhancement for a low volume product

Lead time management plays an important role in a company’s success as reduction in lead time augments productivity and profitability. An efficient lead time reduction tool can reduce manufacturing costs, improves customer satisfaction in the longer run. However, ambiguity always lies in selecting a proper tool for lead time reduction based on the product volume and product mix. Amongst the major tools available for lead time reduction, process mapping, SMED, 5S, Kaizen, Value engineering, Seven-waste reduction are quite versatile and compatible. Nevertheless, for evaluating a product lead time and cycle time Value Stream Mapping probes to be an appropriate tool. VSM faces inabilities in measuring the product with low volume. In such cases, the paraphernalia such as MSOP, process mapping, spaghetti diagram and layout optimization is used to measure the lead time of the product and also account the value added and non value added process in the system, and thereby improve the quality of the product. This paper focuses on an Un-named company which produces low volume parts of caterpillar heavy truck assembly. The goal was to reduce lead time, enhance productivity and reduce the defects in the process by implementing lean tools. In turn, this improves the sales and market shares of the company, leading to better profitability and improve customer satisfaction.

Import of goods of information and communication infrastructure in the context of customs services

Subject. The article updates the issue of information security associated with the extremely intense import of goods of information and communication infrastructure (ICT) and low volume of production of similar goods in the Russian Federation. Objectives. The aim is to develop principles of formation of customs tariff tools to regulate the import of ICT goods in the Russian Federation. Methods. The study employs the content analysis of existing approaches to defining the customs service concept, and general scientific methods of research. Results. The paper presents a unique approach to the development of tools for customs tariff regulation of imports, formulates an original definition of customs service, highlights the contradiction between the current model of implementation of the escalation principle, established by the World Trade Organization, underpins the need to differentiate the rates of import customs duties for the said group of goods. Conclusions. Due to the current specifics of the use of customs and tariff instruments, not all of them are applicable as measures to regulate the import of ICT goods. Currently, the most effective tools for regulating the import of information and communication technology goods are the unified customs tariff and the commodity nomenclature of foreign economic activity.

Oral sulfate solution versus low-volume polyethylene glycol for bowel preparation: Meta-analysis of randomized controlled trials.

Oral sodium sulfate (OSS) solution and low-volume polyethylene glycol-based solutions are two of the more common low-volume purgatives used as colonoscopy preparations. Data on how these different low-volume solutions compare are mixed. Our aim was to conduct a meta-analysis of randomized controlled trials (RCTs) to compare OSS with low-volume polyethylene glycol solutions (PEG) plus ascorbic acid (PEG + Asc) solution with respect to (i) satisfactory bowel preparation, (ii) excellent bowel preparation, and (iii) tolerability. Studies were identified by searching 10 medical databases for reports published from 1974 until 2019. Only fully published RCTs comparing OSS and low-volume PEG-based products with regard to overall satisfactory bowel preparation were included. Pooling was conducted by both fixed-effects and random effects models; results are presented from the random effects model when heterogeneity was significant. Seven studies (involving 2049 subjects) met the inclusion criteria. There was no difference between OSS and PEG + Asc with respect to adequate bowel preparation (risk ratio [RR] 1.02 [0.99-1.06]; P = 0.16). OSS did result in a higher chance of excellent bowel preparation (RR 1.18 [1.06-1.31]; P = 0.03). OSS was associated with a 30% increased risk of nausea (RR 1.35 [1.03-1.77]; P = 0.03) and more than double the risk of vomiting (RR 2.30 [1.63-2.23]; P < 0.05) compared with PEG + Asc. Begg's funnel plot indicated low probability of publication bias. Individuals at low risk of inadequate bowel preparation who use OSS for bowel preparation are more likely to achieve excellent bowel preparation, but are more likely to experience nausea and vomiting than are individuals using low-volume PEG-based solutions.