Sustainable Optimisation In Furniture Production

Sustainable manufacturing is connected with the effectiveness of production processes. Use mathematical model of the key processes to maximise productivity and cost reduction by identifying key processes and parameters influencing manufacturing effectiveness. By Andrea Sujova and Katarina Marcinekova, Technical University in Zvolen, Stefan Hittmar, University of Žilina

An improvement of business processes is necessary in order to maintain the competitiveness of the business and increase the financial performance. The enterprises have to carry out various innovative activities, not only for the outputs, but also for the processes.

The presumption of effective management and a sustainable enterprise improvement are measuring, assessment, control, and further optimisation of the enterprise processes. 

Efficiency and effectivity are the main parameters of processes optimisation. These two terms relate to each other closely and are often confused, though there is a certain distinction in their definitions. 

While the effectiveness expresses the ability of completing the proper steps to achieve the purpose, in other words, the level which the system achieves in the relation to the level which was stated (the current output to the expected output), the efficiency describes the level when the system uses the proper sources in a proper way, so it addresses the question of whether we do the right steps in the right way (the currently used sources to anticipate source consumption).

There are five key dimensions of the effectiveness of a production process including costs, delivery efficiency, quality, flexibility, and innovation. The knowledge on priorities was synthesised within the enterprise competitiveness raise. 

They included process flexibility: the ability to produce a few products so that the production remains cost efficient. For example, it has the ability to rapidly adapt to changes in the product mix.

The model of enterprise effectiveness management has been designed, which consists of eight parts (innovation, competitive advantage, production output, customer-driven output, financial and market output, employee-driven output, process efficiency outputs, and leadership outputs). 

The process effectiveness measures included indicators of efficiency and effectiveness (cost savings, higher productivity, emission reduction, etc.), indicators of internal response ability (circle time, production flexibility, delivery time, adjustment time, etc.), specific indicators of office work/administration efficiency improvement and other support functions (innovation level, results of Six Sigma initiative, and so on), and indicators of supplier—customer relationships (reduction of supplier—customer chain management costs, reduction of supplies and input control, improvement of electronic data exchange, etc.)


Optimising A Manufacturing Process

In the manufacturing process, narrow spots occur. In a graphical concept, the narrow spot of the production is the narrowest spot in the line of manufacturing. It occurs at the moment when the work objects come to a certain point faster than the point is able to process. 

It can be said that it is a point of accumulation in the system. By optimizing, a manufacturing process is therefore needed to focus on the key or critical sub-process. 

The key sub-process is related to the manufacturing of the key product contributing to the final value of production at most. It could be used during the procedure of calculating individual product shares in the total sale of an analysed company. 

The sub-process in which many errors occur provides a gap for improvement and optimisation. The second important aspect is the interconnection of the sub-process to the entire production process and its efficiency, with an emphasis on the technological procedure. 

Therefore, higher weights are allocated to the procedural errors occurring in the first phases of the production process.

The second step by optimizing the task is to identify the essential parameters of the critical manufacturing sub-process (the process of chip formation in parts cutting). The variables which are necessary to be defined within the problem solution can be divided into three groups:

(1)Modern indicators of effectiveness, which is connected to the vision, strategy, and objectives of the reference enterprise, with an emphasis on critical success factors (CSF).

(2)Technical parameters of the sub-process to create a mathematical model that reflects the physical nature of the particular process.

(3)Technical and economic output parameters of the sub-process, reflecting its overall effectiveness, which is understood to be a complex goal consisting of cost minimisation, quality maximisation, and productivity.


Technical and economic output parameters are focused on the solution of an optimisation problem. Optimisation objectives are divided into the following three areas:

(1)Input: optimisation of raw materials, auxiliary materials, energy, human resources, investment, etc. (costs reduction);

(2)Output: optimisation of products, by-products, emissions, cash recovery, etc. (quality improvement);

(3)In Side process: optimisation of technical and economical indexes in terms of production efficiency, energy utilisation, material yield, labour productivity, equipment operating rate, utilisation of current funds, etc. (increasing of process productivity and flexibility).


Methods To Identify Manufacturing

The first step in the optimisation problem solution was to identify the critical sub-process of manufacturing, its specific parameters, procedure, and duration of individual steps in the due enterprise.

The key sub-process is the one which is associated with the manufacturing of the key product that contributes the most to the final value of the production. In the reference enterprise, we decided on the procedure of enumeration of the individual products share on the overall sale. 

The enterprise introduced 21 new products in 2014, which required substantial investment costs. The enterprise currently manufactures 48 products. Having consulted with a technologist in the enterprise, a wash basin closet was chosen as a reference item. 

The lead time (the time necessary to manufacture the product) was 34 min and 9.98 s. To optimise the process, it is necessary to concentrate on the activity with the biggest defect rate because it presents the space for improvement. 

The next important aspect is its link to the effectiveness of the overall manufacturing process, with an emphasis on the technological procedure. 

Considering this, it is important to correct the faults immediately and as early as in the initial stages of production so as not to incur costs in the further manufacturing of a faulty product. The critical sub-process in furniture manufacturing is the milling process.

The second step was to identify the essential parameters of the critical manufacturing sub-process (the process of chip formation in parts cutting-milling). 

The technical parameters of the sub-process (milling) describe its physical nature (spindle speed, cutting speed, feed rate, depth of cut, etc.). It is observed that the cutting speed is the most influential parameter for surface roughness, followed by the feed rate. 

In addition, it was not possible to vary the depth of cut during the experiment in the reference enterprise. Therefore, the input parameters were the cutting speed and feed rate, which were varied. 

The depth of cut was constant and a total volume of removed material was calculated. The output parameters of the process milling were the surface roughness, process duration, and process cost.

Having identified the parameters describing the manufacturing sub-process (cutting–milling), the next step was to create an abstract model of the manufacturing process which would allow an investigation of the manufacturing process using the mathematical description of the progression of the processes. 

The identified input parameters as independent variables were: the cutting speed (vc), feed rate (vf), and the total volume of removed material (V). 

By our experiment, the cutting speed and feed rate were varied using the CNC wood machine and in each variation, one output parameter (surface roughness) was experimentally measured and the other parameters were calculated. The variations of input variables used in the experiment are presented

To enumerate the total volume of the removed material, taking into account the complicated shape of the workpiece, and the content of the removed area was calculated according to the cross-section of the workpiece in the blueprints. 

Other areas were deducted, and consequently, the value of the removed area was multiplied by the cumulated value of the length of the workpiece being machined. The volume of the removed material was also calculated.

Within the experiment, medium density fiberboard (MDF) was used. The fiberboards with a density of 750 kg•m−3 are manufactured from wood fibers, mainly spruce, which are bound by synthetic glue under the simultaneous effect of temperature and pressure. They meet the requirements of EN 622-1 and EN 622-5 standards. 

The thickness of the input material was 22 mm, the length 1000 mm, and width 630 mm. The material was milled under conditions stated in advance, while the thickness was the milling parameter and after the operation was finished, a sample of the dimensions: length 1000 mm, width 22 mm, and thickness 29 mm, was cut out.

The samples were machined by a CNC wood machine of the HOMAG BOF 41/30/R make. The overall performance of the machine is 21 kW and engine performance of the arbor is 7.5 kW, while the revolutions can be set in the range of 0 to 18,000 revolutions per minute. The operation span of the X axis is 3000 mm and the Y axis is 1150 mm. The dimensions of the operation table are 3400 mm × 1450 mm.

The first output parameter includes the quality indicator, surface roughness. The roughness parameter was measured experimentally at the laser profile meter. And the measured and calculated values were analyzed in the Statistica 12 program by a multiple regression tool.


Experiment Aim

The solution of the optimisation problems by this method focused on the optimisation of adjustment of the cutting velocity and speed rate of a CNC machine. 

Create relations of input parameters of the mathematical model and the overall effectiveness of the critical manufacturing sub-process through the abstraction method (selection of essential characteristics and so constructing a simplified model of only those characteristics or signs whose investigation can answer research questions of the scientific objectives of the paper) and define the weights of the individual output parameters, taking into account the critical success factors of the referenced enterprise.

Considering the aim of the work, in particular creating a mathematical model of sub-process effectiveness optimisation, the following hypothesis was stated and tested: There is a significant dependency between the identified input parameters and manufacturing process effectiveness represented by the cost level per sub-process, the quality of the workpiece being machined, and the total duration of the sub-process. The hypothesis verification was carried out via the multiple regression method and artificial neural network.



In the furniture enterprise, the critical activity or sub-process of furniture manufacturing is the milling process because it has the most significant impact on the consequent activities. 

The cost value of sanding is directly proportional to the workpiece surface roughness degree after milling as the smooth surface with a minimum roughness is an important prerequisite to manage the surface finish. 

Aiming at the determination of the quality degree of the due sub-process, we measured the workpiece roughness by two methods described in the previous sections.

The determined input and output parameters influencing the effectiveness of the milling process were measured and calculated within the experiment. Consequently, a dependence of the parameters was verified by the statistical multiply regression method.

It has shown that with recent advances in five-axis milling technology, feed rate optimisation methods have significant effects in regard to enhancing milling productivity, especially when machining complex surface parts. 

The effect of the arbour revolutions setting and feed rate on the surface roughness when milling the MDF board were also been investigated.

In the research, a shank cutter (shank mill) made of hard alloy with a diameter of 8 mm was used. They concluded that high revolutions have an apparent impact on the surface finish parameters, while dependency is indirect, so an increase in revolutions means a decrease in surface roughness. 

The impact of the feed rate is not so obvious; however, it can be stated that its increase is connected to a decrease in the surface finish quality. 

Moreover, via the parameter values with the required precision, we could determine the optimal cutting conditions through considering the monitored parameters and their multiple impacts on the quality, productivity, and costs value of the monitored sub-process, even out of the range of the input parameters used in the experiment.


Key Points 

An important aspect of manufacturing process optimisation is to identify a critical sub-process based on the key indicators of effectiveness, and consequently, to correctly identify the input and output parameters which have the greatest impact on the overall effectiveness of the process. 

In furniture manufacturing, the critical sub-process is the milling process because it has the most significant impact on the consequent activities. The technical parameters describing the physical nature of the milling process, the cutting speed and feed rate, were identified as input parameters. 

Output parameters reflecting the overall effectiveness of the milling process were economic, namely the surface roughness, process duration, and total process costs. Correctness of parameters’ determination was verified by a multiple regression tool. The dependence between parameters was proved and the hypothesis stated was confirmed.

Within optimisation, it is inevitable to follow the latest trends which have progressed, especially with the development of computer supported solutions, because they offer a possibility to deal with multi criteria and include contradictory objectives. 

These methods also include artificial neural networks which play an essential role within the created mathematical optimisation model. Owing to these models, we can forecast the estimated value of output target parameters and determine optimal conditions which will raise the effectiveness of the particular sub-process. 

The main objective of this step in milling process optimisation was to determine the optimal cutting speed and feed rate, focusing on the optimal output parameters (surface roughness, process duration, and process costs) in the furniture enterprise and respecting their contradictory character. 

The resulting artificial neural network as a mathematical optimizing model has three input and three output variables, three hidden neural layers, exponential hidden activation, and the output function and train algorithm is BFGS.

This designed mathematical model was verified by deviations between the measured and calculated values of the parameters in the experiment and the outputs of the artificial neural network. Little deviations confirm a correctness of the mathematical model. 

We came to the conclusion that the optimal values of the output parameters in the milling process will be achieved by cutting speed 4, which is 438.23 m•min−1, and a feed rate of 11.0 m•min−1 if the weights of importance of the output parameters according to the key success factors in the reference enterprise are not taken into account.

Additionally, to meet the aims of the enterprise, the weights of the individual target output measures were chosen and an Equation for the manufacturer was stated, the maximum of which is considered as the intersection of the optimal input-output values taking the specifics of the furniture manufacturing into account.

Finding the optimal solution while maintaining the required product quality lies in the reduction of important factors of the manufacturing process which include material and energy consumption, costs related to the product development, and time. 

The change of the variables in the optimisation problem design shall result in a reduction of material inputs, production costs, goods, material properties of the product, and at the end, in the minimisation of the optimisation task.


The created methodology and the used methods of process optimisation enable achieving a sustainable effectiveness of the manufacturing process, as well as its constant improvement. An application of the presented optimisation model in practical conditions of enterprises is possible by the creation of a software simulation model which will ease the implementation of optimisation in the practice.


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