High-mix Low-volume Manufacturing Research Articles

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$$\%$$ % .

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.

Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods

In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most challenging task. This situation may even be hampered by unreliable supplier performance. This paper presents a generic decision support workflow, which first identifies the most critical external and internal factors which have a serious impact on delivery performance. Next, it suggests a method which combines traditional manufacturing system simulation with advanced machine learning techniques to support the improved daily routine lot-sizing and production scheduling activities in a HMLV company. Argumentation is motivated and illustrated by a detailed industrial case study.

A simulation-based approach to design an automated high-mix low-volume manufacturing system

In this paper, we address the profit optimization problem of an automated high-mix low-volume manufacturing system, which originates from a real-world problem at our industry partner. The manufacturing system includes buffer units from which jobs are automatically transported to workstations, i.e., using automated material handling devices. We consider three different automation concepts for the system: (1) a configuration with parallel buffers and a dedicated robot to work them, (2) a configuration that employs shared buffers that are tended to by automated guided vehicles (AGVs), and (3) a proposed hybrid configuration that takes elements of both aforementioned configurations. We propose a simulation-based approach, which uses simulated-annealing (SA), enriched with the reduced variable neighborhood search (RVNS), to determine the best system configuration for a high-mix, low-volume manufacturer. Decisions concern the choice of automation equipment and the capacity of both parallel and shared buffers. We illustrate the efficacy of the proposed hybrid concept and the proposed SA-RVNS approach with an industry case study using real-world data from our industry partner. Our analysis shows that the proposed concept increases the profit by around 10–30% compared to the others, and the AGV travel time plays an important factor in the proposed concept to yield its true potential.

Microstructure and machinability of selective laser melted high-strength maraging steel with heat treatment

Additive manufacturing (AM) provides a higher degree-of-freedom manufacturing process for high-mix low-volume manufacturing. However, the as-built surface quality by AM processes is generally inferior to that fabricated by conventional subtractive manufacturing and the machinability investigation for post-processing of additively manufactured (AMed) parts is insufficient. In this paper, milling experiments are undertaken to improve the surface finish of the AMed high-strength maraging steel (18Ni300) manufactured by additive manufacturing with and without heat treatment. The influence of microstructure on the machinability is explored, including microhardness, cutting force, surface roughness, tool wear, and chip formation. Significant variation in machinability is identified between the AMed samples that feature distinct microstructures. Surface microhardness of the as-built and heat-treated samples both increased after milling. Cutting forces and tool wear increased sharply after ageing treatment but only a minor change was observed in the as-built samples and those subjected to solution treatment. Roughness value of the as-built samples was reduced from ∼10 to <0.4 μm after milling. Ageing treatment induced chip adhesion on the tool surface and high degree of chip curling. According to the chip morphology analysis, machining of both as-built and solution-treated samples will produce smaller chip serrations and continuous chip formation comparing to the large serrations and fracture morphology in ageing-treated chips. This paper elucidates the relationship between material microstructure and machinability of AMed maraging steel.

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

Augmented reality-assisted robot programming system for industrial applications

Abstract Robots are important in high-mix low-volume manufacturing because of their versatility and repeatability in performing manufacturing tasks. However, robots have not been widely used due to cumbersome programming effort and lack of operator skill. One significant factor prohibiting the widespread application of robots by small and medium enterprises (SMEs) is the high cost and necessary skill of programming and re-programming robots to perform diverse tasks. This paper discusses an Augmented Reality (AR) assisted robot programming system (ARRPS) that provides faster and more intuitive robot programming than conventional techniques. ARRPS is designed to allow users with little robot programming knowledge to program tasks for a serial robot. The system transforms the work cell of a serial industrial robot into an AR environment. With an AR user interface and a handheld pointer for interaction, users are free to move around the work cell to define 3D points and paths for the real robot to follow. Sensor data and algorithms are used for robot motion planning, collision detection and plan validation. The proposed approach enables fast and intuitive robotic path and task programming, and allows users to focus only on the definition of tasks. The implementation of this AR-assisted robot system is presented, and specific methods to enhance the performance of the users in carrying out robot programming using this system are highlighted.

CONWIP versus POLCA: A comparative analysis in a high-mix, low-volume (HMLV) manufacturing environment

Purpose: Few studies comparing manufacturing control systems as they relate to high-mix, low-volume applications have been reported. This paper compares two strategies, constant work in process (CONWIP) and Paired-cell Overlapping Loops of Cards with Authorization (POLCA), for controlling work in process (WIP) in such a manufacturing environment. Characteristics of each control method are explained in regards to lead time impact and thus, why one may be advantageous over the other.Design/methodology/approach: An industrial system in the Photonics industry is studied. Discrete event simulation is used as the primary tool to compare performance of CONWIP and POLCA controls for the same WIP level with respect to lead time. Model verification and validation are accomplished by comparing historic data to simulation generated data including utilizations. Both deterministic and Poisson distributed order arrivals are considered. Findings: For the system considered in this case study, including order arrival patterns, a POLCA control can outperform a CONWIP parameter in terms of average lead time for a given level of WIP. At higher levels of WIP, the performance of POLCA and CONWIP is equivalent. Practical Implications: The POLCA control helps limit WIP in specific áreas of the system where the CONWIP control only limits the overall WIP in the system. Thus, POLCA can generate acceptably low lead times at lower levels of WIP for conditions equivalent to the HMLV manufacturing systems studied.Originality/value: The study compliments and extends previous studies of CONWIP and POLCA performance to a HMLV manufacturing environment. It demonstrates the utility of discrete event simulation in that regard. It shows that proper inventory controls in bottleneck áreas of a system can reduce average lead time.

混合品種組立ラインにおける製品投入順序付けと部品配送計画との統合化に関する一考察

In order to cope with diversified customers’ needs, it has been required to realize the high-mix low-volume manufacturing. A mixed-model assembly line is a type of production line for satisfy the requirement, in which several similar products are assembled sequentially in the same line. To achieve high efficiency of the mixed-model assembly line production, it is critical to determine the product sequence suitably. There have been numerous works for sequencing problem and many methods have been proposed. They, however, do not take parts delivery into consideration, and the product sequence obtained by them may result in poor efficiency due to constraints in delivering parts. This paper provides an integrated method for sequencing and parts delivery planning by formulating the integrated problem as an integer programming.

S1440102 遺伝的アルゴリズムによるフレキシブルジョプショツプスケジューリングの一手法

In these two decades, high-mix low-volume manufacturing and agile manufacturing have been the mainstream. This made discussion about job shop scheduling more active, and many methods based on metaheuristics, especially genetic algorithm (GA), have been proposed. However, many actual factories have alternative machines, and scheduling problem for job shops with alternative machines, i.e. flexible job shop scheduling problem (FJSP) has been discussed recently. To solve FJSP, it is necessary to perform machine selection and job selection. In conventional methods using GA, these two decision making are carried out sequentially. This would fail to search a large solution space and to obtain a good solution. In this paper, we propose a new scheduling method using GA in which the machine selection and job selection are integrated by using random key coding.

Scheduling of die casting operations including high-mix low-volume and line-type production

This article addresses the scheduling problem of a real die casting shop. The problem is of practical importance and yet complicated, especially for a modern casting environment where a variety of cast products made of different alloys are simultaneously manufactured in relatively small lot sizes. As a simple and robust scheduling methodology, a Linear Programming (LP) model is proposed so as to determine the quantity of each product in a casting shift. The solution of the LP model maximises the average efficiency of melting furnaces, i.e., the percentage use of molten alloys throughout the shifts. Our model can represent a most general casting environment to the extent that some die casting machines carry out frequent in-process die exchanges for flexible manufacturing. At the same time, we employ line-type casting as well as a combination die with multi-cavities which can cast dissimilar shapes concurrently. In the high-mix low-volume manufacturing world, the proposed LP model can assist the die casting industry to strengthen its competence by providing an optimal schedule that satisfies practical constraints on casting processes.

High-Mix Low-Volume Flow Shop Manufacturing System Scheduling

The paper deals with the production scheduling optimization in High-Mix Low-Volume (HMLV) flow shop manufacturing system. Three new heuristic production scheduling algorithms balancing the machines assignment to the production orders are proposed. The goal of the algorithms is to find a production schedule that minimizes the total production time of the set of jobs - makespan. An example case study, where the proposed scheduling algorithms are verified and compared with basic solutions using the simulation model of the HMLV manufacturing system created in Witness simulation software, is presented.

EFFICIENT MATERIAL ALLOCATIONS IN HIGH-MIX LOW-VOLUME MANUFACTURING

Today's globalization market drives industries toward increased expectations on lean production and quick response to the customers. These expectations have put industries under pressure to become more agile under highly dynamic market and uncertain manufacturing environment. An efficient material allocation becomes critical due to dynamics and uncertainty in the manufacturing systems. This research addresses the optimization needs for effective material allocations in high-mix low-volume manufacturing. Multi-constraint based genetic algorithm and intelligent simulation are used for efficient material allocation. The proposed effective material allocation is validated through real-world application in electronics manufacturing.

Integrated Production Planning, Scheduling and Shop Floor Tracking System for High-Mix Low-Volume Manufacturing - A Consortium Approach

In Singapore, the Precision Engineering (PE) industry is the backbone of the manufacturing sector; it supports a large number of manufacturing industries such as Electronics, Chemicals, Pharmaceuticals, Biotechnology, Aerospace, Oil &amp; Gas and Automotive. From our recent visits to companies in the PE industry, it was observed that given such a dynamic and stochastic manufacturing environment, most of these PE companies are still predominantly using Microsoft Excel as their planning &amp; scheduling tool, shop floor tracking is normally performed manually through paper traveler, and production status is only updated at the end of the day or as and when demanded by customer. The common problems highlighted include inability to provide realistic delivery commitment, machine and manpower resources are not optimally utilized to fulfill customer orders, unacceptable order cycle time and lack of production visibility. Therefore, there is an increasing demand and impending need for a computerized integrated production planning/scheduling and shop floor tracking system. In this paper, a consortium approach for the development of an integrated production planning/scheduling and shop floor tracking system for the High-Mix Low-Volume (HMLV) PE environment is presented. An illustration through the implementation of the proposed system in a PE company is highlighted. With this system, companies would be able to improve their delivery performance through effective handling of ad-hoc customer orders and the ability to react to deviations or unplanned events in production.

A Layered-Encoding Cascade Optimization Approach to Product-Mix Planning in High-Mix–Low-Volume Manufacturing

High-mix-low-volume (HMLV) production is currently a worldwide manufacturing trend. It requires a high degree of customization in the manufacturing process to produce a wide range of products in low quantity in order to meet customers' demand for more variety and choices of products. Such a kind of business environment has increased the conversion time and decreased the production efficiency due to frequent production changeover. In this paper, a layered-encoding cascade optimization (LECO) approach is proposed to develop an HMLV product-mix optimizer that exhibits the benefits of low conversion time, high productivity, and high equipment efficiency. Specifically, the genetic algorithm (GA) and particle swarm optimization (PSO) techniques are employed as optimizers for different decision layers in different LECO models. Each GA and PSO optimizer is studied and compared. A number of hypothetical and real data sets from a manufacturing plant are used to evaluate the performance of the proposed GA and PSO optimizers. The results indicate that, with a proper selection of the GA and PSO optimizers, the LECO approach is able to generate high-quality product-mix plans to meet the production demands in HMLV manufacturing environments.

Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm

Manpower scheduling problem is one of the key scheduling problems with extensive applications in manufacturing. This paper presents a mixed-integer programming model with a two-stage heuristic algorithm for solving the manpower scheduling problem in the precision engineering industry. Firstly, a mixed-integer programming formulation is developed to model the manpower scheduling problem in this high-mix low-volume manufacturing environment. Secondly, a two-stage heuristic algorithm is proposed where the first stage is deployed to calculate the skill requirements for each shift by considering the jobs, machines, and their production schedule and the second stage is designed to assign operators to the machines by considering the skill set requirements and the operator's expressed preferences. Lastly, the computational results based on problem instances emulating real-world scenarios demonstrated the feasibility and effectiveness of the proposed heuristic.

309 A Study of Autonomous Dispatching Decision-making in a Parallel Machines System

Due to the rapidly changing markets and demands driven by customers, especially processing in today's high-mix low-volume manufacturing environment, increasing the ability to design a feasible and intelligent production schedule becomes essential. Hence, an agile manufacturing system with autonomous characteristics is expected to deal with the characteristics of continuous and unanticipated changes for such manufacturing facilities and environment. In this research, the idea of applying agent-oriented technique (AOT) to construct the payoff matrix which representing the decisions made by agents is presented. By using the payoff matrix can reveal the autonomous and intelligent decision processes for work centers and machines (i.e., agents). This autonomous dispatching decision-making scheme can also be used in solving dispatching problem in a parallel machine production system. Also another one purpose of this study is to achieve the goal of the agile manufacturing system and to help business to face the changing challenges by obtaining the local optimal solution in which the system having parallel machines.